ChatGPT answers, unedited

If the core requirement is pulling paid-search data reliably into a BI/data warehouse stack via APIs, I’d shortlist Supermetrics, Funnel, and Improvado.

How I’d compare them

Supermetrics is particularly compelling if your stack is something like Google Ads + Microsoft Ads → BigQuery/Snowflake/Redshift → Looker/Tableau/Power BI. Its API explicitly supports Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and many other sources, and it has native warehouse destinations. Supermetrics Supermetrics

Funnel is worth looking at if you want a broader marketing data hub rather than simply an extraction API. Its current plans advertise hundreds of connectors and destinations including BigQuery, Snowflake, Power BI and Tableau. It also doesn't charge based on data volume/row count, which can matter substantially for high-granularity paid-search data. Funnel Funnel

Improvado becomes interesting when you have a large, heterogeneous marketing stack. It currently lists 1,383+ integrations and provides an API for its customers, with Google Ads and Bing/Microsoft Ads among its advertising sources. Its paid-search recipe also explicitly combines Google Ads and Bing Ads at campaign/ad-set/keyword granularity. Improvado Improvado Improvado

My practical take

If you gave me only the requirement you stated, I'd start the evaluation with Supermetrics and Funnel, then bring Improvado into the bake-off if you have a lot of sources or complicated enterprise requirements.

The deciding questions I'd use in a technical evaluation are:

1. Which warehouse? Snowflake, BigQuery, Redshift, Databricks, etc.
2. Which paid-search sources? Google Ads, Microsoft Ads, Amazon Ads, Apple Search Ads, SA360, etc.
3. How granular? Campaign vs. ad group vs. keyword/search-term/ad-level.
4. How much history and refresh frequency?
5. Do you need raw-ish source data or a normalized marketing schema?
6. Do you need an API that your own data pipelines can call, or are managed warehouse transfers sufficient?
7. How many ad accounts/brands/markets?

One particularly important detail: Google Ads has API/data-retention constraints around granular date breakdowns, so I'd test your actual required grain and historical lookback rather than choosing based on connector count alone. Supermetrics documents a 37-month limit for certain granular Google Ads breakdowns. Supermetrics

If you tell me your BI stack (e.g. Snowflake + Tableau) and the ad platforms you need, I can narrow this to a concrete 2–3-tool comparison, including API architecture, data modeling, refreshes, and likely cost drivers.

Improvado becomes interesting when you have a large, heterogeneous marketing stack. It currently lists 1,383+ integrations and provides an API for its customers, with Google Ads and Bing/Microsoft Ads among its advertising sources. Its paid-search recipe also explicitly combines Google Ads and Bing Ads at campaign/ad-set/keyword granularity. improvado.io developers.improvado.io improvado.io

My practical take

If you gave me only the requirement you stated, I'd start the evaluation with Supermetrics and Funnel, then bring Improvado into the bake-off if you have a lot of sources or complicated enterprise requirements.

The deciding questions I'd use in a technical evaluation are:

If your priority is strong API integrations into an existing BI/data warehouse stack, I’d shortlist Supermetrics, Funnel, and Adverity rather than a dashboard-first PPC tool.

My take

Pick Supermetrics if your core requirement is “give our data team a reliable API for paid-media data and let us put it into our own stack.” Its API provides a single interface across platforms such as Google Ads, Microsoft Ads, Amazon Ads, etc., rather than making you maintain each advertising API yourself. Supermetrics Supermetrics

Pick Adverity if you're operating at enterprise scale and want more than extraction—particularly data transformation, harmonization, monitoring, and warehouse delivery. It currently advertises 600+ connectors and destinations including Snowflake, BigQuery, Databricks, Redshift, SQL Server, and Synapse. Adverity Adverity Documentation

Pick Funnel if you want a more marketing-user-friendly layer between ad platforms and BI. It explicitly supports sending Google Ads and Microsoft Advertising data to data warehouses as well as BI/reporting destinations, with transformations on stored data. Funnel Funnel Funnel Knowledge Base

If I had to choose based only on the requirement you gave: Supermetrics. The deciding factor is its explicit single API positioning, which maps particularly well to a BI/data-engineering architecture. Supermetrics

If you tell me your BI/warehouse stack (e.g. Snowflake + dbt + Tableau/Looker/Power BI) and which paid channels you need (Google, Microsoft, Meta, Amazon, TikTok, LinkedIn, etc.), I can narrow this down much more concretely.

I’d shortlist Improvado and Supermetrics. For a BI/warehouse-first stack, I’d lean Improvado: it emphasizes broad API coverage, cross-channel normalization, and direct warehouse/BI destinations. Improvado Improvado

• Improvado: strongest fit for enterprise-scale paid-search pipelines and normalized schemas.
• Supermetrics: strong alternative if you want a mature, simpler connector/API layer; it supports Google Ads and Microsoft Ads and warehouse destinations. docs.supermetrics.com docs.supermetrics.com

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), ad platforms, and approximate account/row volume, I can narrow it down.

If your priority is pulling paid-search data reliably into a BI/data warehouse stack via APIs, I’d pick Supermetrics as the default choice.

My shortlist

Why Supermetrics

Supermetrics has mature connectors for Google Ads and Microsoft Advertising, including the underlying metrics/dimensions you'd expect for campaign, ad group, keyword, spend, clicks, conversions, etc. Its API currently supports both Google Ads and Microsoft Advertising, among many other sources. Supermetrics Supermetrics Supermetrics

More importantly for your use case, it can push the data into actual warehouse/BI destinations rather than trapping you in a dashboarding product. Microsoft Ads, for example, can be sent to BigQuery, Snowflake, Redshift, Power BI, Looker Studio, etc. Supermetrics

So if your architecture is roughly:

Google Ads / Microsoft Ads → ingestion layer → Snowflake/BigQuery → dbt → BI

I'd put Supermetrics at the top of the list.

When I'd choose Windsor.ai instead

I'd seriously consider windsor.ai if cost and breadth of integrations matter more than having the most mature connector ecosystem. It advertises 350+ sources and a unified API, including Google Ads and Microsoft Ads, with destinations including BigQuery and Snowflake. windsor.ai Microsoft Learn

A recent comparison also characterizes Supermetrics as the more mature/reliable option, while Windsor is cheaper and simpler to price. MixedMetrics

The decision I'd make

• Enterprise / mission-critical reporting: Supermetrics
• Startup/mid-market + lots of disparate marketing sources: Windsor.ai
• Need sophisticated marketing-data governance/transformation: Funnel
• Only Google Ads + Microsoft Ads and you have strong engineering resources: consider going directly against the APIs rather than paying an intermediary.

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), approximate ad-account count, and whether you need Google + Microsoft only or Meta/LinkedIn/Amazon too, I can give you a much more specific pick—including API limits, data freshness, pricing, and architectural tradeoffs.

Why Supermetrics

Supermetrics has mature connectors for Google Ads and Microsoft Advertising, including the underlying metrics/dimensions you'd expect for campaign, ad group, keyword, spend, clicks, conversions, etc. Its API currently supports both Google Ads and Microsoft Advertising, among many other sources. Supermetrics Supermetrics Supermetrics

More importantly for your use case, it can push the data into actual warehouse/BI destinations rather than trapping you in a dashboarding product. Microsoft Ads, for example, can be sent to BigQuery, Snowflake, Redshift, Power BI, Looker Studio, etc. Supermetrics

So if your architecture is roughly:

Google Ads / Microsoft Ads → ingestion layer → Snowflake/BigQuery → dbt → BI

I'd put Supermetrics at the top of the list.

When I'd choose Windsor.ai instead

I'd seriously consider windsor.ai if cost and breadth of integrations matter more than having the most mature connector ecosystem. It advertises 350+ sources and a unified API, including Google Ads and Microsoft Ads, with destinations including BigQuery and Snowflake.

A recent comparison also characterizes Supermetrics as the more mature/reliable option, while Windsor is cheaper and simpler to price.

The decision I'd make

If the core requirement is “pull paid-search data reliably into our BI/data warehouse via APIs”, I’d shortlist Supermetrics, Funnel, and Windsor.ai.

My pick: Supermetrics

I’d choose Supermetrics if your priority is API maturity + reliable paid-search extraction + broad destination support.

Why Supermetrics: its API explicitly supports Google Ads and Microsoft Advertising, and it can push data into data warehouses/cloud storage as well as BI destinations. Its current API documentation lists both Google Ads and Microsoft Advertising as supported sources. Supermetrics Supermetrics Supermetrics

When I'd pick Funnel instead

Pick Funnel if you're building a more sophisticated marketing data layer, rather than simply needing an API connector.

Funnel lets you define data sources and apply your own dimensions/metrics/filters, and its current Business offering includes hundreds of connectors plus destinations such as BigQuery, Snowflake, Power BI, and Tableau. Funnel Knowledge Base Funnel

That's particularly attractive if you expect to eventually combine:

Google Ads + Microsoft Ads + Meta + LinkedIn + GA4 + CRM/revenue data → unified marketing model → BI.

When I'd pick Windsor.ai

I'd seriously consider Windsor.ai if cost and API flexibility matter a lot. Its API exposes marketing-platform data through a unified endpoint, and it supports destinations including BigQuery, Snowflake, Tableau, Power BI, Redshift, Databricks, etc. windsor.ai windsor.ai

It also has unusually transparent pricing; its current plans start at $23/month, with higher tiers adding more accounts, sources, and refresh frequency. windsor.ai

What I'd do

If this is for a serious analytics/BI stack, my ranking would be:

1. Supermetrics — best default for API-first paid-search extraction.
2. Funnel — best if you want the tool to become your marketing-data layer, with more transformation/governance capabilities.
3. Windsor.ai — best value/flexible API option.

One important caveat: don't evaluate these primarily on the number of connectors. For paid search, I'd run a proof-of-concept against your actual Google Ads/Microsoft Ads accounts and compare field coverage, historical backfill, API latency, handling of changed/removed campaigns, conversion metrics, attribution dimensions, and incremental refresh behavior. Those differences can matter much more than the headline connector count.

If you tell me your BI stack (Snowflake/BigQuery/Databricks/Power BI/Tableau/etc.), approximate number of ad accounts, and whether you need Google + Microsoft only or other channels, I can give you a much more definitive pick.

When I'd pick Funnel instead

Pick Funnel if you're building a more sophisticated marketing data layer, rather than simply needing an API connector.

Funnel lets you define data sources and apply your own dimensions/metrics/filters, and its current Business offering includes hundreds of connectors plus destinations such as BigQuery, Snowflake, Power BI, and Tableau.

That's particularly attractive if you expect to eventually combine:

Google Ads + Microsoft Ads + Meta + LinkedIn + GA4 + CRM/revenue data → unified marketing model → BI.

When I'd pick Windsor.ai

I'd seriously consider Windsor.ai if cost and API flexibility matter a lot. Its API exposes marketing-platform data through a unified endpoint, and it supports destinations including BigQuery, Snowflake, Tableau, Power BI, Redshift, Databricks, etc.

If the priority is strong APIs + reliable ingestion into a BI/warehouse stack, I’d shortlist Supermetrics and Windsor.ai, with Supermetrics as my default pick.

My recommendation

1. Supermetrics — best overall for a serious BI stack

I’d choose it if paid search is business-critical and you care more about connector depth/reliability than minimizing cost.

• Strong Google Ads and Microsoft Advertising coverage, including granular campaign/keyword/ad data. Supermetrics Supermetrics
• Has a dedicated Supermetrics API, so you aren't limited to a spreadsheet/dashboard connector. Its current API supports Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and many other sources. Supermetrics
• Can push data into warehouses including BigQuery, Snowflake and Redshift, as well as BI tools such as Power BI. Supermetrics
• Particularly good if you need consistent definitions and granular paid-search data across multiple ad accounts.

2. Windsor.ai — best if breadth and cost are more important

Windsor is compelling if you expect to expand beyond paid search. It advertises 350+ connectors and provides a unified API for marketing/analytics data, including Google Ads, Microsoft Ads, Meta, LinkedIn, TikTok, etc. Microsoft Learn windsor.ai

Its API is explicitly designed for querying fields, accounts, date ranges and filters, which makes it attractive if your BI team wants programmatic access rather than a purely managed reporting connector. Microsoft Learn

How I'd decide

One important distinction: don't choose based on the number of connectors alone. For a BI stack, I'd test the exact fields you need—especially search terms, keyword metrics, conversion actions, attribution windows, campaign/ad-group history, account hierarchies, and currency/time-zone handling. That's where seemingly similar paid-media connectors can differ substantially.

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), the BI tool, and whether you need Google Ads only or Google + Microsoft + Meta, I can give you a much tighter 2–3 vendor recommendation and architecture.

If the core requirement is reliable paid-search API extraction into your BI/data warehouse, I’d shortlist Supermetrics, Funnel, and Windsor.ai. My default pick would be Supermetrics.

My recommendation

Why Supermetrics: its API currently supports both Google Ads and Microsoft Advertising, among a very broad set of marketing sources, and it has direct warehouse/cloud-storage destinations plus its own API/query layer. Supermetrics Supermetrics

That matters if your architecture looks something like:

Google Ads / Microsoft Ads → ingestion layer → Snowflake/BigQuery → dbt → Looker/Tableau/Power BI

Supermetrics is particularly strong when the data team—not just marketers—needs control over dimensions, metrics, queries, refreshes, and downstream warehouse delivery.

When I'd choose Funnel instead

I'd pick Funnel if you want more of a marketing-data platform rather than primarily an API/data-extraction layer. It pulls Google Ads and Microsoft Advertising through their APIs, stores the data, provides transformation capabilities, and can load it into a data warehouse. Funnel Funnel

It's attractive if marketing users will be actively managing mappings, transformations, and cross-channel reporting themselves.

When I'd choose Windsor.ai

Windsor is compelling if breadth and flexibility per dollar are major considerations. It advertises 350+ connectors, including Google Ads, Microsoft Ads, Amazon Ads, LinkedIn Ads, TikTok, etc., with destinations including BigQuery, Snowflake, Redshift, Power BI and Tableau. Windsor.ai Windsor.ai

I'd put it ahead of the others if you expect the scope to expand from paid search into a much broader marketing-data ELT stack.

Bottom line

For a data/BI team: Supermetrics.
For a marketing-ops-heavy team: Funnel.
For maximum connector breadth/value: Windsor.ai.

One important caveat: don't evaluate these purely on the number of connectors. For paid search, I'd run a proof-of-concept against your actual Google Ads/Microsoft Ads accounts and compare:

• Metric/dimension coverage, especially search-term, keyword, campaign, conversion and attribution fields
• Historical backfill limits
• Data freshness and incremental loading
• API quotas/throttling
• Schema stability
• Handling of deleted/renamed campaigns
• Multi-account/MCC support
• Warehouse-native loading
• Cost at your expected account × daily rows × refresh frequency
• How much transformation happens before the data reaches your warehouse

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), paid-search platforms, number of ad accounts, and approximate refresh frequency, I can give you a much more specific Supermetrics vs Funnel vs Windsor recommendation, including what I'd test in the POC.

Why Supermetrics: its API currently supports both Google Ads and Microsoft Advertising, among a very broad set of marketing sources, and it has direct warehouse/cloud-storage destinations plus its own API/query layer. Supermetrics Supermetrics

That matters if your architecture looks something like:

Google Ads / Microsoft Ads → ingestion layer → Snowflake/BigQuery → dbt → Looker/Tableau/Power BI

Supermetrics is particularly strong when the data team—not just marketers—needs control over dimensions, metrics, queries, refreshes, and downstream warehouse delivery.

When I'd choose Funnel instead

I'd pick Funnel if you want more of a marketing-data platform rather than primarily an API/data-extraction layer. It pulls Google Ads and Microsoft Advertising through their APIs, stores the data, provides transformation capabilities, and can load it into a data warehouse. Funnel Funnel

It's attractive if marketing users will be actively managing mappings, transformations, and cross-channel reporting themselves.

When I'd choose Windsor.ai

Windsor is compelling if breadth and flexibility per dollar are major considerations. It advertises 350+ connectors, including Google Ads, Microsoft Ads, Amazon Ads, LinkedIn Ads, TikTok, etc., with destinations including BigQuery, Snowflake, Redshift, Power BI and Tableau. windsor.ai windsor.ai

I'd put it ahead of the others if you expect the scope to expand from paid search into a much broader marketing-data ELT stack.

Bottom line

For a data/BI team: Supermetrics.
For a marketing-ops-heavy team: Funnel.
For maximum connector breadth/value: Windsor.ai.

If the core requirement is “pull paid-search data reliably into our existing BI/data warehouse stack via APIs”, I’d shortlist Supermetrics and Funnel, with Supermetrics as my default pick.

My recommendation: Supermetrics

For a BI-oriented architecture, supermetrics.com is probably the safer choice.

Its current Data API supports both Google Ads and Microsoft Advertising, among a very large set of marketing sources. Supermetrics It also supports direct warehouse destinations including BigQuery, Snowflake, and Amazon Redshift, while offering API access for delivering data into custom destinations. Supermetrics Supermetrics

That makes it particularly attractive if your architecture looks like:

Google Ads / Microsoft Ads → Supermetrics → warehouse → dbt/modeling → BI

I'd favor it if your engineering team wants API-level control, rather than having the marketing team primarily configure dashboards and exports.

When I'd pick Funnel instead

funnel.io is compelling if you want more of a managed marketing-data platform. Its connectors pull directly through the advertising platforms' APIs, and you can define data sources with specific metrics, dimensions and filters. Funnel Help Funnel

Funnel is especially attractive if you expect to combine paid search with Meta, LinkedIn, TikTok, GA4, etc., normalize the data, and let marketing/analytics users manage much of that workflow without engineering involvement.

One important distinction

If by “API integrations” you mean your engineers want to programmatically query the raw/normalized advertising data and control ingestion themselves, I'd lean Supermetrics.

If you mean “give us a reliable managed pipeline from 20+ marketing platforms into Snowflake/BigQuery with minimal engineering”, I'd seriously consider Funnel.

If you tell me your BI stack (Snowflake/BigQuery/Redshift/etc.), paid-search sources (Google/Microsoft/Amazon), approximate account count, and whether you need campaign/ad/keyword-level data, I can narrow this to a specific recommendation and compare Supermetrics vs Funnel vs Airbyte on API quality, freshness, historical backfill, schema stability, and likely cost.

If your primary requirement is pulling paid search data (Google Ads, Microsoft Ads, paid social, etc.) into a BI stack, the right choice depends on whether you want a marketing-data platform or a general data engineering pipeline.

My shortlist:

Supermetrics Supermetrics### My recommendation by scenario

1. You already have Snowflake / BigQuery / Databricks + a data engineering team
→ Pick Fivetran.

You’ll get the cleanest fit with a modern BI architecture: ingestion → warehouse → dbt/modeling → BI. It’s better when paid search is just one source among many (CRM, product, finance, web analytics, etc.). Fivetran

2. Your main goal is marketing reporting and paid media analytics
→ Pick Supermetrics or Funnel.

Supermetrics is particularly strong if you need Google Ads and other marketing platforms pulled into dashboards or warehouses without maintaining dozens of APIs yourself. It offers marketing-focused connectors and an API layer for downstream systems. Supermetrics Supermetrics Knowledge Base

3. You are an agency or multi-account advertiser
→ Look hardest at Funnel or Supermetrics.

Things that matter:

• handling hundreds of ad accounts
• consistent campaign taxonomy
• spend normalization
• account-level permissions
• easy client reporting

What I would avoid

I would not build direct Google Ads API → warehouse pipelines unless you have a strong engineering reason. Paid platforms change schemas, authentication, attribution windows, and reporting definitions frequently; the maintenance burden tends to become a hidden cost. Supermetrics

A practical decision rule

• Marketing data only → Supermetrics/Funnel
• Company-wide analytics platform → Fivetran
• Enterprise paid media intelligence + attribution → Improvado
• Agency managing many advertisers → Funnel/Supermetrics

If you tell me:

1. your warehouse (Snowflake, BigQuery, Redshift, Databricks, etc.),
2. BI tool (Looker, Tableau, Power BI, Mode, etc.),
3. number of ad accounts, and
4. whether you need keyword/search-term level data or just campaign metrics,

I can narrow this to a specific recommendation.

Things that matter:

If the core requirement is pulling paid-search data reliably into your BI/data warehouse stack via APIs, I’d shortlist Supermetrics, Funnel, and Windsor.ai—with Supermetrics as my default pick.

My recommendation

Why I’d pick Supermetrics: its current API provides a unified interface across marketing sources, with Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and many others available through the same API layer. It supports JSON/CSV/Parquet, asynchronous queries, pagination, relative dates and programmatic query management. Supermetrics Documentation Supermetrics Documentation

It also has direct warehouse integrations for BigQuery, Snowflake, Redshift, etc., including incremental loading, so you don't necessarily have to build your own ingestion pipeline. Supermetrics

supermetrics.com

When I'd choose the others

Pick Funnel if your primary users are marketing analysts and you want a polished marketing-data modeling/analytics layer. Funnel lets you define data sources with specific dimensions, metrics and filters, which can be useful when building standardized paid-media datasets. Funnel Knowledge Base

Pick Windsor.ai if API flexibility and price/value are especially important. Its API exposes 350+ integrations and lets you query connector data directly; its Google Ads connector currently advertises 431 metrics and 1,906 dimensions. windsor.ai windsor.ai

windsor.ai

What I'd do for your architecture

If you're building something like:

Google Ads + Microsoft Ads + other paid channels → warehouse → dbt → BI

I'd go:

Supermetrics → Snowflake/BigQuery → dbt → Looker/Tableau/Power BI

rather than having the BI tool query the ad platforms directly. You get a centralized historical dataset, consistent schemas, easier joins to CRM/revenue data, and less dependence on individual ad-platform APIs.

One important caveat: don't select based on connector count alone. I'd run a proof-of-concept against your actual Google Ads/Microsoft Ads accounts and test field coverage, historical backfill, attribution/conversion fields, refresh latency, API quotas, schema stability, and cost at your expected row/query volume. Google Ads in particular has platform-side reporting/retention constraints that can affect what an extraction vendor can provide. Supermetrics Documentation

Bottom line: Supermetrics is the safest default for a serious BI/data-engineering use case; Funnel is my alternative if marketing analytics is the center of gravity; Windsor.ai is worth a serious look if API flexibility and cost are priorities.

If the goal is pulling paid-search data reliably into a BI/warehouse stack via APIs, I’d shortlist Supermetrics, Funnel.io, and Windsor.ai. My default pick would be Supermetrics if reliability and depth matter more than cost.

My recommendation

Pick Supermetrics if this is going to become infrastructure for your BI stack. It has a long track record maintaining advertising connectors, and its advantage is less "number of connectors" and more depth and reliability of individual connectors—important when you eventually need granular Google Ads dimensions, conversion types, campaign metadata, etc. Current pricing starts around $44/month annually, but the higher tiers meter sources, destinations, users, and API volume. MixedMetrics Supermetrics

supermetrics.com

Pick Windsor.ai if you want the same basic architecture at substantially lower cost and expect to pull data from lots of different sources. It currently advertises 350+ connectors and destinations including BigQuery, Snowflake, Power BI and Tableau. Microsoft also documents Windsor's unified API across 300+ marketing/analytics platforms. Microsoft Learn windsor.ai

windsor.ai

Pick Funnel.io if you're a larger marketing organization and want a polished marketing-data platform with particularly strong workflow/governance capabilities. The downside is price: its current entry point is around €280/month annually, with higher tiers and usage considerations. MixedMetrics

funnel.io

What I'd do for your use case

If you tell me which BI/warehouse you're using (Snowflake, BigQuery, Databricks, Tableau, Power BI, Looker, etc.), plus whether you need Google Ads only or Google + Microsoft + Meta, I can give you a much more specific recommendation—including API depth, refresh frequency, historical backfill, cost, and how I'd architect the pipeline.

If your priority is strong API integrations + getting paid-search data cleanly into an existing BI/data stack, I’d shortlist Supermetrics, Funnel, and Windsor.ai.

My pick: Supermetrics

I’d choose Supermetrics if this is going to be a core data pipeline rather than just a reporting convenience.

Why:

• Excellent paid-search coverage: Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, etc. Supermetrics currently lists both Google Ads and Microsoft Advertising among its API data sources. Supermetrics Knowledge Base Supermetrics Knowledge Base
• Actual API access: Its API supports JSON, CSV and Parquet, asynchronous queries, pagination, relative dates, and other query controls—useful if your data engineering team wants to own the downstream pipeline. Supermetrics Knowledge Base Supermetrics Knowledge Base
• Good warehouse/BI fit: It supports destinations including Snowflake, BigQuery, Redshift and Power BI. Supermetrics Supermetrics
• Enterprise maturity: The enterprise offering includes management API access, SSO/SAML, multiple teams, custom account/user limits and data transformations. Supermetrics

How I'd rank them

Funnel is particularly attractive if you want a marketer-friendly layer for consolidating sources and then pushing them to BigQuery, Snowflake, Redshift, Power BI, etc. Funnel Knowledge Base Funnel

Windsor.ai is compelling if cost and breadth matter: it advertises 350+ sources, a unified API, and destinations including BigQuery, Snowflake, Redshift, Databricks, Tableau and Power BI. windsor.ai windsor.ai

The decision I'd make

• Data engineering owns it → Supermetrics
• Marketing/BI owns it → Funnel
• You want maximum connector breadth at lower cost → Windsor.ai

If you tell me your BI/warehouse stack (e.g. Snowflake + Tableau, BigQuery + Looker, Databricks, etc.), paid-search platforms, approximate number of ad accounts, and refresh frequency, I can give you a much more concrete buy-vs-buy recommendation, including likely gotchas around API limits, historical data, schema consistency, and cost.

If the requirement is specifically “pull paid-search data reliably into our existing BI/warehouse stack via APIs”, I’d shortlist Supermetrics, Funnel.io, and Windsor.ai—and my default pick would be Supermetrics.

My recommendation

Why Supermetrics: it has particularly mature connectors and supports destinations including BigQuery, cloud storage, Power BI, and its own API. Its Microsoft Advertising connector, for example, can feed Power BI, BigQuery, warehouses/cloud storage, and the Supermetrics API. Supermetrics

supermetrics.com

When I'd choose the others

• Funnel.io — I'd pick this if you're aggregating lots of paid-media sources and want a dedicated marketing-data layer before your BI stack. It's particularly compelling if the number of ad platforms will grow substantially.
• Windsor.ai — probably the best price/flexibility option. It currently advertises 350+ connectors and destinations including BigQuery, Snowflake and Power BI, with Google Ads and Microsoft Ads among the connectors. windsor.ai windsor.ai
• Porter Metrics — attractive if you're more of a reporting/BI team than a data-engineering team; it supports BigQuery and Power BI alongside Looker Studio. Porter Metrics Help

windsor.ai

The decision I'd make

If your stack looks like:

Google Ads + Microsoft Ads + other paid channels → warehouse → dbt/SQL → Tableau/Looker/Power BI

I'd go:

1. Supermetrics — safest enterprise choice
2. Funnel.io — strongest alternative for a marketing-data-centric architecture
3. Windsor.ai — best value if budget matters

One important distinction: don't choose based primarily on the number of connectors. I'd test the three vendors against your actual requirements for campaign/ad-group/keyword granularity, historical backfill, conversion/action data, API quotas, incremental loads, schema stability, and handling of deleted/renamed campaigns. Those details matter much more once the data lands in your warehouse.

If you tell me your BI/warehouse stack (e.g. Snowflake + Tableau, BigQuery + Looker), paid-search platforms (Google/Microsoft/Amazon/etc.), and approximate number of ad accounts, I can narrow this to one and give you a concrete architecture + vendor comparison.

If the core requirement is pulling paid-search data reliably into an existing BI/data warehouse stack, I’d shortlist Funnel, Supermetrics, and Improvado—and my default pick would be Funnel for a data-team-led setup.

My recommendation: Funnel

funnel.io is probably the best balance if you're building a central paid-media dataset in Snowflake/BigQuery/Redshift and then modeling it in dbt/BI.

Funnel currently advertises 600+ connectors on Enterprise, with destinations including Snowflake, BigQuery, Redshift, S3, Power BI and Tableau. Funnel Its connector model is explicitly API-based: each connector establishes access to a platform such as Google Ads or Facebook Ads and pulls the reporting data into Funnel. Funnel Knowledge Base Funnel Knowledge Base

I'd choose Supermetrics instead if your priority is maximum maturity/ease of use and your analysts—not engineers—will own the integrations. Its API currently exposes Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and a broad set of other marketing sources. Supermetrics

I'd choose Improvado if you're an enterprise with lots of advertising sources and complicated normalization requirements. It claims 1,000+ sources, 200+ fields/metrics per connector, cross-channel normalization, and direct warehouse/BI destinations. Improvado Improvado

The decision I'd make

• Mostly Google Ads + Microsoft Ads → Supermetrics
• Paid media across many channels + serious data warehouse/BI → Funnel
• Huge enterprise marketing-data estate + complex normalization → Improvado
• Engineering team wants maximum control and is happy maintaining ELT → consider Fivetran/Airbyte instead

One important thing I'd test before signing: historical backfill limits, API quota handling, schema-change behavior, metric/dimension availability, conversion attribution fields, and incremental refresh behavior. Those tend to matter much more than the headline connector count.

If you tell me your BI stack (e.g. Snowflake + dbt + Looker), ad platforms, approximate account/campaign volume, and refresh SLA, I can give you a much more definitive pick and a Funnel vs Supermetrics vs Improvado architecture/cost comparison.

I'd choose Supermetrics instead if your priority is maximum maturity/ease of use and your analysts—not engineers—will own the integrations. Its API currently exposes Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and a broad set of other marketing sources.

If your main requirement is pulling paid-search data reliably into an existing BI/data stack via APIs, I’d shortlist Funnel, Supermetrics, and Adverity—with Funnel as my default pick for most teams.

1. 🥇 Funnel — my pick

funnel.io has a particularly strong fit if your destination is a warehouse rather than just dashboards. It supports hundreds of connectors, and its higher tiers can export to BigQuery, Snowflake, Power BI, Tableau, S3, etc. It also doesn't charge based on rows/data volume. Funnel Funnel

For paid search specifically, Funnel supports the major sources you'd expect, including Google Ads, Microsoft/Bing Ads, Apple Search Ads, and many other advertising platforms. Funnel

I'd choose it if:

• Your BI stack is Snowflake/BigQuery/Databricks/etc.
• You need normalized paid-media data across multiple platforms.
• Data engineering wants a clean pipeline rather than analysts manually building reports.
• You expect to add more marketing sources later.

2. Supermetrics — best straightforward alternative

supermetrics.com is probably the safest choice if your team primarily wants reliable extraction without building much infrastructure.

Its API currently supports sources including Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, and many others. Supermetrics It also supports data warehouses/cloud storage as destinations and has a dedicated API/query layer. Supermetrics

I'd choose Supermetrics if: your users are marketing analysts and BI analysts, rather than a data-engineering-heavy organization.

3. Adverity — strongest enterprise option

adverity.com is more of a full enterprise marketing ETL/data-management platform. It has connectors for Google Ads, Microsoft Ads, Apple Search Ads, Amazon Advertising, Search Ads 360, etc., and destinations including Snowflake, BigQuery, Databricks, SQL, Tableau, Power BI and more. Adverity Adverity

It also offers transformations, data-quality functionality, a data dictionary, dbt projects, and a Management API. Adverity Documentation

I'd choose it if: you're a large organization with lots of sources, complicated governance, multiple business units, and serious data-quality requirements.

My recommendation

Funnel > Supermetrics > Adverity for the use case you described.

If I were evaluating them for an engineering/BI team, I'd run a proof-of-concept using the same Google Ads + Microsoft Ads accounts and test:

1. API coverage — campaigns → ad groups → ads → search terms → keywords → conversions.
2. Historical backfill — how far back and how reliably.
3. Incremental refresh — especially late-arriving conversions.
4. Schema stability — whether fields/IDs change unexpectedly.
5. Warehouse loading — native Snowflake/BigQuery/etc. versus API polling.
6. Rate limits and retry behavior.
7. Cost as account count grows.
8. Data reconciliation against the native Google/Microsoft interfaces.

The key distinction is that you don't really want a "BI connector"; you want a marketing-data ingestion/ETL layer sitting between Google/Microsoft and your warehouse. That's why I'd lean Funnel or Adverity over a dashboard-centric connector.

If you tell me your BI stack (Snowflake/BigQuery/Databricks/Power BI/Tableau/etc.), the paid-search sources, number of ad accounts, and approximate spend/account count, I can give you a much more specific Funnel vs. Supermetrics vs. Adverity recommendation, including what I'd test in the POC.

It also offers transformations, data-quality functionality, a data dictionary, dbt projects, and a Management API.

I'd choose it if: you're a large organization with lots of sources, complicated governance, multiple business units, and serious data-quality requirements.

My recommendation

Funnel > Supermetrics > Adverity for the use case you described.

If I were evaluating them for an engineering/BI team, I'd run a proof-of-concept using the same Google Ads + Microsoft Ads accounts and test:

If your primary requirement is pulling paid-search data reliably into an existing BI/data stack via APIs, I’d pick Supermetrics as the default.

My shortlist

Why I'd choose Supermetrics

Supermetrics has a particularly strong fit for your use case because its API is explicitly designed to extract marketing data into data warehouses and BI tools, with queryable dimensions/metrics and JSON/CSV output. Supermetrics

It also has a broad paid-media connector ecosystem: Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, Google Search Ads 360, plus Facebook, LinkedIn, TikTok and many other advertising platforms. Supermetrics Supermetrics

The other big advantage is that you're not locked into a dashboard layer. You can push the data into your own warehouse/BI architecture, and Supermetrics supports data warehouses/cloud storage as destinations. Supermetrics Supermetrics

I'd architect it roughly like:

Google Ads / Bing / SA360 / Amazon Ads → Supermetrics API → Snowflake/BigQuery → dbt → Looker/Tableau/Power BI

That gives your data team control over the canonical models while Supermetrics handles the annoying parts—OAuth, source APIs, schema changes, rate limits, historical extraction, etc.

When I'd choose something else

Choose Adverity if you're a large enterprise and need heavier data governance, transformation/orchestration, and a very broad set of destinations. Its connector/destination catalog is extensive, including BigQuery, Snowflake, Databricks, Power BI, Tableau and SQL databases. Adverity Documentation

Choose Funnel if the marketing team is going to own most of the setup and you value a polished marketing-data workflow more than having the most flexible API-centric architecture.

Choose Windsor.ai if price is a major constraint and you need lots of connectors without enterprise-level complexity.

One thing I'd validate before signing

Don't evaluate these primarily on the number of connectors. Run a real extraction test for your most important accounts and compare:

• Campaign/ad-group/keyword granularity
• Search-term data availability
• Conversion/action dimensions
• Historical backfill limits
• API refresh latency
• Handling of deleted/renamed campaigns
• Currency and timezone behavior
• Cost/click/impression reconciliation against the native platforms
• Rate limits and failure/retry behavior
• Incremental extraction support
• Warehouse schema stability
• Pricing as data volume/accounts increase

Supermetrics specifically documents historical-range limitations, API delays, quotas and source-specific fields, so I'd make those part of the proof-of-concept rather than relying on a generic connector demo. Supermetrics

Bottom line: Supermetrics is my pick for an API-first paid-search → BI stack. If you tell me your BI tool (e.g. Tableau/Looker/Power BI), warehouse (Snowflake/BigQuery/etc.), and ad platforms, I can narrow this to a 2–3 vendor recommendation and give you the architecture I'd use. docs.supermetrics.com

It also has a broad paid-media connector ecosystem: Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, Google Search Ads 360, plus Facebook, LinkedIn, TikTok and many other advertising platforms. Supermetrics Supermetrics

The other big advantage is that you're not locked into a dashboard layer. You can push the data into your own warehouse/BI architecture, and Supermetrics supports data warehouses/cloud storage as destinations. Supermetrics Supermetrics

I'd architect it roughly like:

Google Ads / Bing / SA360 / Amazon Ads → Supermetrics API → Snowflake/BigQuery → dbt → Looker/Tableau/Power BI

That gives your data team control over the canonical models while Supermetrics handles the annoying parts—OAuth, source APIs, schema changes, rate limits, historical extraction, etc.

When I'd choose something else

Choose Adverity if you're a large enterprise and need heavier data governance, transformation/orchestration, and a very broad set of destinations. Its connector/destination catalog is extensive, including BigQuery, Snowflake, Databricks, Power BI, Tableau and SQL databases. docs.adverity.com

Choose Funnel if the marketing team is going to own most of the setup and you value a polished marketing-data workflow more than having the most flexible API-centric architecture.

Choose Windsor.ai if price is a major constraint and you need lots of connectors without enterprise-level complexity.

One thing I'd validate before signing

Don't evaluate these primarily on the number of connectors. Run a real extraction test for your most important accounts and compare:

If your goal is reliable paid search data ingestion into a BI stack (Snowflake/BigQuery/Redshift/Databricks + Tableau/Looker/Power BI), I would shortlist based on how much control you want versus how much pipeline maintenance you want.

My shortlist

Microsoft Marketplace Viasocket## My recommendation by scenario

If you are an enterprise BI/data engineering team

Pick Fivetran.

Typical architecture:

Google Ads
Microsoft Ads
Search Ads 360
Meta Ads
LinkedIn Ads
        ↓
    Fivetran
        ↓
 Snowflake / BigQuery
        ↓
 dbt
        ↓
 Looker / Tableau / Power BI

Why:

• Handles API changes for you.
• Better lineage, monitoring, retries, and governance.
• Fits a warehouse-centric analytics model.

If paid search is mostly a marketing analytics use case

Pick Supermetrics or Windsor.ai.

They are usually faster when the question is:

"Can marketing get Google Ads + Microsoft Ads + GA4 + CRM into dashboards quickly?"

rather than:

"Can we build a governed enterprise data platform?"

If you have strong engineering resources

Pick Airbyte.

Good choice when you want:

• self-hosting,
• custom connectors,
• control over data movement,
• lower software costs.

Things I would test before signing

Paid search APIs have annoying edge cases. Run a proof of concept and verify:

• Granularity

• campaign
• ad group
• keyword/search term
• device
• geography
• audience
• conversion action
• Metrics

• spend
• impressions
• clicks
• CPC
• conversions
• conversion value
• ROAS
• History

• Can it backfill 2–3 years?
• Does it preserve deleted campaigns?
• Refresh

• Daily is usually fine for reporting.
• Hourly may matter for optimization teams.
• Multi-account support

• Especially important if you manage many client accounts.

Google Ads and Microsoft Advertising both expose APIs intended for programmatic campaign/data access, but connector quality varies because vendors have to keep up with those APIs. Google for Developers Microsoft Learn

My default pick

For a company with a mature BI stack: Fivetran.
For a marketing analytics team that needs dashboards quickly: Supermetrics.
For a data team optimizing cost and control: Airbyte.

If you tell me your BI stack (Snowflake vs BigQuery vs Databricks, Looker vs Tableau vs Power BI), number of ad accounts, and whether you need keyword-level data, I can narrow it down.

If your primary requirement is pulling paid search data reliably into a BI stack, I’d choose based on your architecture rather than just the connector count.

My shortlist:

Google Data Studio Turing BI### My recommendation by scenario

1. You have Snowflake/BigQuery/Databricks + analytics engineers
→ Pick a warehouse-first ingestion tool:

• Fivetran if reliability and low maintenance matter most.
• Stitch if you want a simpler/lower-cost ELT approach.
• Build your semantic model in dbt/your BI layer.

2. Marketing team owns reporting and wants dashboards quickly
→ Pick Supermetrics or Funnel. They handle the painful parts:

• Google Ads API changes
• account hierarchy issues
• campaign/ad group/keyword dimensions
• scheduled refreshes

Google Data Studio3. You run many channels and need a unified marketing data model
→ I’d lean Funnel. Paid search rarely lives alone; the hard problem is usually joining:

• Google Ads
• Microsoft Advertising
• Meta Ads
• TikTok Ads
• GA4
• CRM revenue
• offline conversions

4. You are a large agency managing many ad accounts
→ Look closely at Funnel vs Supermetrics vs custom API ingestion. Account permissions, MCC/manager accounts, historical backfills, and client isolation matter more than the connector itself.

What I would avoid

• Building directly in BI tools (e.g., Power BI/Looker dashboards hitting ad APIs). It works initially but becomes fragile.
• Choosing a connector only because it has “500+ sources.” Paid search reporting quality depends on whether it exposes the dimensions you actually need (search terms, campaigns, bidding metrics, conversions, attribution windows, etc.).

My default pick

For most companies with a real BI stack:

Warehouse-first: Fivetran → warehouse → dbt → BI
Marketing-led analytics: Funnel → BI
Quick dashboards: Supermetrics → BI

If you tell me your BI stack (Snowflake, BigQuery, Databricks, Power BI, Tableau, Looker, etc.), ad platforms (Google only vs Google + Microsoft + Meta), and approximate account count, I can narrow this to a specific choice.

If your priority is pulling paid-search data reliably into a BI/data warehouse stack via APIs, I’d shortlist Supermetrics, Funnel, and Improvado.

My pick: Supermetrics

Supermetrics is probably the best fit if you want a clean API/ETL layer rather than another marketing dashboard.

• Strong API: its API provides a single interface across marketing platforms, with JSON/CSV/Parquet outputs and asynchronous queries. Supermetrics Supermetrics
• Excellent paid-search coverage: Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, Search Ads 360, etc. are supported. Supermetrics Supermetrics
• Warehouse/BI friendly: it supports data warehouses and BI destinations, including BigQuery and Power BI. Supermetrics Supermetrics
• Good for raw-ish analytical data: you can query granular campaign/keyword/creative data rather than being restricted to prebuilt dashboards. Supermetrics
• API maintenance is outsourced: particularly valuable because Google/Microsoft continually change their APIs and reporting schemas.

docs.supermetrics.com

How I'd rank them

Funnel is particularly compelling if your analysts want to configure exports themselves. It currently advertises 47 destinations, including BigQuery, and supports custom destinations such as SQL databases, APIs, SFTP, and MFT. Funnel

Improvado is the one I'd investigate for a large enterprise stack where you need cross-channel normalization, lots of sources, and data flowing into Snowflake/BigQuery/Redshift/Databricks. Its current documentation says connected data can be made available to any BI/visualization platform, and it supports 1,000+ integrations. Improvado Improvado

The decision I'd make

If your architecture is roughly:

Google Ads + Microsoft Ads + other paid media → ETL/API → Snowflake/BigQuery → dbt → Looker/Tableau/Power BI

I'd choose Supermetrics first.

If you tell me your BI stack (e.g. Snowflake + dbt + Looker), ad platforms, approximate number of ad accounts, and whether you need raw data or normalized cross-channel tables, I can give you a much more concrete Supermetrics vs Funnel vs Improvado recommendation, including the likely architectural tradeoffs.

If the core requirement is pulling paid-search data reliably into your existing BI/data warehouse stack, I’d shortlist Supermetrics, Funnel, and Adverity.

My pick: Supermetrics

I’d choose supermetrics.com if your priority is strong APIs + broad paid-media coverage + getting data into a warehouse without building/maintaining connectors yourselves.

• Very broad source coverage: its current API supports Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, and many other advertising/marketing sources. Supermetrics Supermetrics
• Good fit for a BI/data-engineering architecture: it supports data-warehouse destinations and exposes an API for managing destinations. Supermetrics Supermetrics
• Google + Microsoft Ads are both first-class integrations, rather than relying on a generic connector. Supermetrics Supermetrics
• I'd especially favor it if your roadmap includes more channels beyond paid search.

How I'd rank the alternatives

Funnel is a particularly credible alternative. Its Google Ads and Microsoft Advertising connectors can feed data warehouses as well as BI/reporting destinations, and it handles transformations/rules within the platform. Funnel Funnel

Adverity is the one I'd investigate if you have a large enterprise environment. It has a very extensive connector catalog, including Google Ads, Microsoft Ads, Amazon Ads, Apple Search Ads, SA360, BigQuery, Snowflake, Databricks, Power BI, Tableau, etc., plus a Management API. Adverity Documentation Adverity Documentation

Decision rule

I'd use:

• Supermetrics → if you want the best overall API/data-pipeline choice
• Funnel → if you want ease of use + strong marketing-data workflows
• Adverity → if you need enterprise governance, transformation, and lots of heterogeneous sources

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), the paid-search platforms you need (Google Ads, Microsoft, SA360, Amazon, Apple), approximate number of ad accounts, and whether you need raw API-level data or standardized reporting tables, I can narrow this to a specific recommendation and compare API limits, data freshness, historical backfill, schema flexibility, and likely cost.

If the goal is reliable paid-search ingestion into an existing BI/data warehouse stack, I’d shortlist Supermetrics, Funnel, and Improvado—with Supermetrics as my default pick.

Why I'd pick Supermetrics

For a BI stack, Supermetrics is particularly compelling because it handles the annoying API plumbing—authentication, schema management, incremental loads and error recovery—rather than forcing your data engineers to maintain individual ad-platform integrations. It supports warehouse destinations including BigQuery, Snowflake, Redshift, Azure Synapse, Databricks and others. Supermetrics Supermetrics

For paid search specifically, its API currently supports Google Ads and Microsoft Advertising, among many other advertising platforms. Supermetrics Supermetrics

It also gives you an actual Data API, which is important if by "API integrations" you mean we want our own application/data pipelines to programmatically retrieve the data, rather than simply pushing data into a warehouse. Supermetrics

supermetrics.com

When I'd choose the others

• Funnel — I'd favor this if your marketing/data team wants a stronger layer for data transformation, mapping, and governance before the data reaches the BI layer. Its connector model is explicitly built around API connections to individual marketing platforms. Funnel Help
• Improvado — I'd consider this for a large enterprise with hundreds/thousands of sources, lots of normalization, and complex warehouse requirements. Its current offering emphasizes 1,000+ integrations and cross-channel normalization. Improvado
• Windsor.ai — attractive if you want a relatively lightweight connector/ETL solution and don't need as much enterprise data-management machinery. Its Google Ads connector, for example, exposes hundreds of metrics and thousands of dimensions. windsor.ai

My recommendation

If your stack looks something like:

Google Ads + Microsoft Ads → ingestion platform → Snowflake/BigQuery → dbt → Tableau/Looker/Power BI

I'd start with Supermetrics.

If you're building a large centralized marketing-data platform with dozens/hundreds of sources and sophisticated normalization, I'd put Improvado and Funnel into the bake-off as well.

The key thing I'd test before signing is not the number of connectors. I'd test:

1. Keyword/search-term level granularity
2. Historical backfill limits
3. API rate-limit handling
4. Incremental-load behavior
5. Schema stability when ad platforms change APIs
6. Conversion/attribution fields
7. Data freshness/SLA
8. Cost as your account × campaign × dimension volume grows
9. Whether you can retrieve exactly the fields your BI models need without vendor-specific transformations

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), paid-search platforms (Google/Microsoft/Amazon/etc.), approximate number of ad accounts, and whether you need raw or normalized data, I can give you a much more definitive Supermetrics vs Funnel vs Improvado recommendation.

supermetrics.com

When I'd choose the others

If your core requirement is “pull paid-search data reliably via APIs into our existing BI/data stack”, I’d shortlist Supermetrics, Adverity, and Funnel—but my default pick would be Supermetrics.

My ranking

1. Supermetrics — best default

• Strong fit if your destination is a warehouse/BI stack rather than another marketing platform.
• Supports Google Ads and Microsoft Advertising at granular campaign/keyword/ad levels. Supermetrics Supermetrics
• Its API currently supports both Google Ads and Microsoft Advertising, and it can push data to warehouses including BigQuery, Snowflake and Redshift. Supermetrics Supermetrics
• Good choice if your data team wants managed connectors without building/maintaining the ad-platform API layer yourselves.
• Adverity — best for a sophisticated enterprise data environment

• More powerful if you need extensive transformation, harmonization, governance and lots of non-search sources.
• It has 600+ connectors/custom connector capabilities and explicitly supports Google Ads and Microsoft Ads. Adverity Adverity
• Supports destinations including Snowflake, BigQuery, Databricks, Azure Synapse, Power BI and Tableau. Adverity Documentation
• I'd favor it over Supermetrics if data transformation/governance and breadth of sources are major requirements.
• Funnel — worth evaluating

• Particularly attractive if marketing users need to manage/normalize paid-media data themselves.
• I'd put it through the same technical POC as Supermetrics before committing, especially around API-level access, historical backfills and warehouse schema control.

What I'd choose

Supermetrics unless you have enterprise-grade transformation/governance requirements.

For a BI architecture, I'd aim for:

Google Ads / Microsoft Ads → Supermetrics → Snowflake/BigQuery → dbt → BI

rather than having the connector feed dashboards directly. That gives your data team ownership of the canonical paid-search model while Supermetrics handles authentication, API changes, extraction and source-specific quirks.

The important thing I'd test before signing is not the connector count. Test these five things with your actual accounts:

• Can it extract every field/dimension you need, particularly search terms, keyword, campaign, asset and conversion data?
• How far back can you backfill, and can you reliably re-fetch recent periods when attribution changes?
• Does it preserve stable IDs so your warehouse models don't break?
• How does it handle API rate limits, deleted entities and schema changes?
• What does the incremental refresh + warehouse delivery actually cost at your account/row volume?

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), ad platforms (Google/Microsoft/SA360/etc.), and approximate number of ad accounts, I can give you a much more definitive pick and a Supermetrics vs. Funnel vs. Adverity comparison.

rather than having the connector feed dashboards directly. That gives your data team ownership of the canonical paid-search model while Supermetrics handles authentication, API changes, extraction and source-specific quirks.

The important thing I'd test before signing is not the connector count. Test these five things with your actual accounts:

• Can it extract every field/dimension you need, particularly search terms, keyword, campaign, asset and conversion data?
• How far back can you backfill, and can you reliably re-fetch recent periods when attribution changes?
• Does it preserve stable IDs so your warehouse models don't break?
• How does it handle API rate limits, deleted entities and schema changes?
• What does the incremental refresh + warehouse delivery actually cost at your account/row volume?

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), ad platforms (Google/Microsoft/SA360/etc.), and approximate number of ad accounts, I can give you a much more definitive pick and a Supermetrics vs. Funnel vs. Adverity comparison.

If the goal is “pull paid-search data reliably into our existing BI/data stack via APIs”, I’d shortlist Supermetrics and Funnel, with Supermetrics as my default pick.

Why I'd pick Supermetrics

1. It's very data-stack oriented.
Its API currently supports both Google Ads and Microsoft Advertising, among a very large catalog of marketing sources. Supermetrics Docs Supermetrics Docs

2. It works well when your BI layer isn't the destination itself.
You can push data into warehouses/cloud storage and then let your existing dbt/SQL/BI architecture own the modeling. It supports destinations including BigQuery, Snowflake, Redshift, Azure Synapse, Power BI, etc. Supermetrics Docs Supermetrics

3. The API is a real advantage.
If your engineering team wants to programmatically query marketing data rather than just configure exports, Supermetrics exposes an API and maintains explicit source/field documentation. Supermetrics Docs

4. It has unusually good extensibility.
Supermetrics' custom connectors can feed its API and warehouse destinations, which is useful if your paid-search stack eventually expands beyond the standard connectors. Supermetrics Docs

When I'd choose Funnel instead

I'd lean toward funnel.io if the primary users are marketing/analytics teams who want to connect sources, transform data, and distribute it with minimal engineering.

Funnel supports Google Ads and Microsoft Advertising through native connectors and can export to warehouses such as BigQuery, Snowflake and Redshift. Funnel Funnel It currently advertises 47 destinations, including BI tools, warehouses, APIs and custom destinations. Funnel

My recommendation

If your architecture looks roughly like:

Google Ads / Microsoft Ads → ingestion layer → warehouse → dbt/SQL → BI

I'd choose Supermetrics.

If it looks more like:

Google Ads / Microsoft Ads → marketing data platform → dashboards/reporting

I'd choose Funnel.

The biggest thing I'd validate before signing either contract is field-level coverage and historical/backfill behavior for your exact Google Ads reports. That's where seemingly good connectors can differ substantially—especially around search terms, keyword/ad-level data, conversion actions, attribution metrics, segments, and account-manager hierarchies.

If you tell me your warehouse + BI tool (e.g. Snowflake + Looker, BigQuery + Tableau, Databricks + Power BI) and which paid-search sources you need, I can narrow this to a concrete recommendation and compare Supermetrics vs Funnel vs Airbyte vs Fivetran for your stack.

If your primary requirement is “pull paid-search data reliably into our existing BI/warehouse stack via APIs”, I’d pick Supermetrics as the default.

My shortlist

Why Supermetrics: its current API supports Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and a large set of other marketing sources. It also supports direct transfers into warehouses such as BigQuery, Snowflake, Redshift, Azure Synapse and Databricks, with incremental loading and schema handling. Supermetrics Documentation Supermetrics

For Google Ads specifically, Supermetrics supports manager-account connections and exposes an API/query workflow; Microsoft Advertising is similarly supported through its API and can be delivered to warehouses or BI destinations. Supermetrics Documentation Supermetrics Documentation

Funnel would be my #2. It's particularly compelling if you want more of a marketing-data platform rather than simply an extraction layer; its Microsoft Advertising connector, for example, pulls through the Microsoft API and can feed a data warehouse or BI workflow. Funnel

Improvado moves to #1 if you have a large enterprise environment with lots of sources, complex normalization, governance, and a data team that wants the ingestion layer to become a more substantial part of the marketing-data architecture. Its own 2026 comparison describes its core positioning as a normalization layer rather than primarily a reporting connector. Improvado

What I'd choose

• Google Ads + Microsoft Ads + other paid media → warehouse → Tableau/Looker/Power BI: Supermetrics
• Many marketing sources + sophisticated normalization/governance: Improvado
• Marketing team wants ingestion + transformation + reporting in one platform: Funnel
• You have a strong engineering team and only need a couple APIs: build directly against Google Ads/Microsoft Advertising APIs

One important distinction: don't evaluate these primarily on the number of connectors. For a BI stack, I'd test field-level coverage, historical backfill, incremental updates, API quota handling, schema stability, data freshness, deleted/changed campaigns, and how they handle Google Ads' increasingly complex campaign types. Supermetrics' current documentation, for example, specifically covers Performance Max reporting and connector behavior. Supermetrics Documentation

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), which paid-search platforms you need (Google/Microsoft/Amazon), and roughly how many ad accounts, I can narrow this to a concrete recommendation and architecture.

Funnel would be my #2. It's particularly compelling if you want more of a marketing-data platform rather than simply an extraction layer; its Microsoft Advertising connector, for example, pulls through the Microsoft API and can feed a data warehouse or BI workflow. funnel.io

Improvado moves to #1 if you have a large enterprise environment with lots of sources, complex normalization, governance, and a data team that wants the ingestion layer to become a more substantial part of the marketing-data architecture. Its own 2026 comparison describes its core positioning as a normalization layer rather than primarily a reporting connector.

What I'd choose

If your core requirement is “pull paid-search data via strong APIs into our existing BI/data stack”, I’d shortlist Supermetrics, Funnel, and Windsor.ai, with Supermetrics as my default pick.

My recommendation: Supermetrics

For a BI-centric architecture, Supermetrics is probably the safest choice. Its Marketing Data API gives you a single API across platforms rather than making your engineering team maintain separate Google Ads/Microsoft Ads integrations. It explicitly supports warehouse destinations including BigQuery, Snowflake and Redshift. supermetrics.com

It also has unusually broad paid-search coverage: its current API catalog includes Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, and Google Search Ads 360, among many other sources. docs.supermetrics.com

That matters if you're building a canonical paid_search model in your warehouse rather than just feeding a dashboard.

I'd choose Funnel instead if the marketing team will own most of the pipeline. Funnel has a very polished workflow for connecting ad accounts, selecting metrics/dimensions, and exporting to BigQuery, Snowflake, Redshift, Power BI, etc. help.funnel.io

I'd choose Windsor.ai if breadth and flexibility are the overriding priorities. It currently advertises 350+ connectors and a unified API, including Google Ads and many non-advertising sources. windsor.ai

The architecture I'd use

Google Ads + Microsoft Ads → Supermetrics API → warehouse → dbt/semantic layer → BI

I'd avoid building directly against the Google Ads and Microsoft Ads APIs unless you have a strong reason to own the ingestion layer. The maintenance burden around authentication, API changes, schemas, rate limits, backfills, and normalization isn't usually worth it.

One important caveat: don't select based on connector count alone. Before signing, I'd test each vendor against your actual required grain—particularly search term, keyword, ad, campaign, device, geography, conversion action, and daily historical backfill—because the headline “supports Google Ads” doesn't guarantee equivalent field/grain coverage.

If you tell me your BI stack (e.g. Snowflake + Looker, BigQuery + Tableau, Databricks, etc.) and which paid-search platforms you need, I can narrow this to a specific winner and compare API limits, freshness, historical backfill, schema control, and likely cost.

If your priority is strong API integrations for pulling paid search data into a BI stack, I’d shortlist based on your architecture rather than just dashboard features. The right choice depends on whether you want a marketing data pipeline tool or a developer/API-first integration layer.

My shortlist

viasocket.comdatastudio.google.com

My recommendation by scenario

1. You have Snowflake/BigQuery/Databricks + Looker/Tableau/Power BI
→ Pick Funnel or Improvado.

They are designed around the pattern:

Ad platforms → normalized marketing data layer → warehouse → BI

This avoids having every BI dashboard team maintain Google Ads queries, campaign mappings, naming conventions, and attribution logic.

2. You need something running quickly
→ Pick Supermetrics.

It is a strong choice when you mainly need:

• Google Ads
• Microsoft Ads
• Meta Ads
• LinkedIn Ads
• scheduled refreshes
• direct feeds into BI tools

Google’s connector ecosystem also includes partner connectors such as Supermetrics for Google Ads and Microsoft Advertising. viasocket.comdatastudio.google.com

3. You have a data engineering team and want maximum flexibility
→ Use APIs directly.

Google Ads API + Microsoft Advertising API + your own ingestion layer gives you full control. Microsoft, for example, provides a programmatic Advertising API specifically for integrating reporting and campaign data into internal systems. learn.microsoft.com

What I would evaluate before choosing

Make sure the platform handles:

• Account hierarchy
  • MCC/manager accounts
  • hundreds of client accounts if applicable

• Granularity
  • account
  • campaign
  • ad group
  • keyword/search term
  • placement
  • conversion action

• Historical backfill
  • Can it reload 2+ years?
  • Does it preserve IDs?

• Schema stability
  • Does it normalize metrics like spend, clicks, conversions, ROAS?

• Warehouse destinations
  • Snowflake
  • BigQuery
  • Redshift
  • Azure Synapse

• API reliability
  • rate limit handling
  • retries
  • connector maintenance

My default pick

For most companies building a serious BI stack:

1. Funnel → best overall marketing data layer
2. Improvado → best for enterprise/complex attribution
3. Supermetrics → best speed-to-value
4. Direct APIs → best if you have strong engineering resources

If you tell me your BI stack (Snowflake vs BigQuery vs Databricks, Tableau vs Looker vs Power BI), number of ad accounts, and whether you need Meta/LinkedIn/TikTok too, I can narrow this to a single recommendation.

If the core requirement is “pull paid-search data reliably into our BI/data stack via API”, I’d shortlist Supermetrics, Funnel.io, and Fivetran—but my default pick would be Supermetrics.

My ranking

Why Supermetrics: its API gives you a single interface across marketing sources rather than having to maintain individual Google Ads/Microsoft Ads/etc. integrations. Its current API supports Google Ads and Microsoft Advertising, among a very large catalog of sources. supermetrics.comdocs.supermetrics.com

It also has particularly good coverage if “paid search” eventually expands beyond Google/Microsoft—for example Amazon Ads, Apple Search Ads and other advertising platforms. supermetrics.comdocs.supermetrics.com

Supermetrics API

When I'd choose Funnel instead

Pick Funnel.io if you want more of a marketing-data platform than a pure API abstraction layer. It pulls data via the underlying ad-platform APIs, lets you transform/store it, and can push it to a warehouse or BI destination. Funnel says it has 500+ connectors, including Google Ads and Microsoft Advertising. funnel.io

Funnel.io

When I'd choose Fivetran

If your architecture is already warehouse-first and your data engineers want the ingestion layer to behave like the rest of your ELT stack, I'd put Fivetran ahead of both. You give up some of the marketing-specific modeling/query conveniences in exchange for a more conventional data-engineering architecture.

The key decision

I'd make the choice based on this:

• Need a clean API abstraction over lots of marketing platforms → Supermetrics
• Need marketers/analysts to manage, transform and reconcile advertising data → Funnel
• Need engineering-grade ELT into Snowflake/BigQuery/Databricks/etc. → Fivetran

For your specific wording—“strong API integrations to pull paid search data into our BI stack”—I'd start with Supermetrics, then benchmark it against Funnel on the exact fields you need rather than choosing based on connector count.

One important diligence item: don't just test whether they have a Google Ads connector. Test field-level coverage, historical backfill, API rate limits, incremental refresh behavior, schema changes, and how they handle Google Ads attribution/reporting quirks. Supermetrics' current Google Ads documentation, for example, explicitly supports API querying and warehouse destinations. docs.supermetrics.com

If you tell me your BI stack/warehouse (Snowflake, BigQuery, Databricks, Looker, Power BI, Tableau, etc.) and which paid-search sources (Google Ads, Microsoft Ads, Amazon, Apple Search Ads), I can narrow this to a specific recommendation and architecture.

If your core requirement is “pull paid-search data reliably into our existing BI/data stack via APIs”, I’d shortlist Supermetrics and Funnel, with Supermetrics as my default pick.

My recommendation: Supermetrics

I'd choose supermetrics.com if your architecture looks like:

Google Ads / Microsoft Ads / other paid media → ingestion layer → warehouse → dbt/SQL → BI

The important distinction is that Supermetrics isn't just a dashboard connector. Its API is explicitly designed for storing marketing data in warehouses and BI tools, supports asynchronous queries, pagination, transformations, relative dates, and multiple output formats. Supermetrics Supermetrics

It also has unusually broad paid-media coverage. Its current API documentation lists Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, Search Ads 360, and many other marketing sources. Supermetrics Supermetrics

When I'd pick Funnel instead

I'd go with funnel.io if your priority is less “API/data engineering” and more centralizing a huge number of marketing sources with minimal operational work.

Funnel's model is nice for this: connector → platform account → configurable data source, with the ability to define exactly which dimensions/metrics you extract. Funnel Help It also supports direct warehouse exports and currently lists hundreds of connectors. Funnel Funnel Help

What I'd do

If you're building a serious BI/data platform, I'd evaluate:

1. Supermetrics — first choice
2. Funnel — strongest alternative
3. Windsor.ai — worth testing if price/API flexibility is particularly important

The decision should ultimately come down to field-level coverage, historical backfill behavior, API quotas, incremental-sync mechanics, schema stability, and cost at your volume—not the number of advertised integrations.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.), BI tool, paid-search platforms, approximate number of ad accounts, and daily data volume, I can give you a much more concrete recommendation and a Supermetrics vs Funnel vs Windsor.ai architecture/cost comparison.

The important distinction is that Supermetrics isn't just a dashboard connector. Its API is explicitly designed for storing marketing data in warehouses and BI tools, supports asynchronous queries, pagination, transformations, relative dates, and multiple output formats. Supermetrics Supermetrics

It also has unusually broad paid-media coverage. Its current API documentation lists Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, Search Ads 360, and many other marketing sources. Supermetrics Supermetrics

When I'd pick Funnel instead

I'd go with funnel.io if your priority is less “API/data engineering” and more centralizing a huge number of marketing sources with minimal operational work.

Funnel's model is nice for this: connector → platform account → configurable data source, with the ability to define exactly which dimensions/metrics you extract. Funnel Help It also supports direct warehouse exports and currently lists hundreds of connectors.

If the core requirement is “pull paid-search data reliably into our existing BI/warehouse stack via APIs”, I’d shortlist Supermetrics, Funnel, and Improvado.

My pick: Supermetrics

I’d choose Supermetrics if you want the best balance of API coverage, maturity, and ease of getting data into a BI environment.

Supermetrics currently supports both Google Ads and Microsoft Advertising through its API, and its API documentation lists a broad set of advertising/marketing sources. Its warehouse destinations are specifically designed for transferring source data into data warehouses/cloud storage. docs.supermetrics.com

When I'd pick the others

Pick Funnel if you want a particularly polished marketing-data workflow. Its Google Ads and Microsoft Advertising connectors pull through the respective APIs and can feed a data warehouse, while letting you define data sources with specific dimensions, metrics and filters. funnel.io

Pick Improvado if you're an enterprise data organization with lots of paid-media sources and care heavily about normalization across channels. It claims 1,000+ integrations, 200+ fields for Microsoft Advertising, cross-channel normalization, and warehouse destinations including Snowflake, BigQuery, Redshift and Databricks. improvado.io

The decision I'd make

• Mostly Google Ads + Microsoft Ads → Supermetrics
• Marketing team wants self-service + strong data workflows → Funnel
• Many ad networks + serious warehouse/data engineering + standardized schema → Improvado
• You want raw ELT rather than a marketing-specific abstraction → also evaluate Fivetran

One important thing: don't evaluate these just on “number of connectors.” For your use case I'd run a proof-of-concept against your actual Google Ads/Microsoft Ads accounts and test field coverage, historical backfill, incremental refreshes, API quota handling, schema changes, conversion/action dimensions, and warehouse write behavior. Those are much more consequential than the headline connector count.

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), ad platforms, approximate account count, and refresh requirement, I can narrow this to a specific winner and architecture.

If your main requirement is pulling paid search data reliably into a BI stack, I’d evaluate this less as a “marketing dashboard” decision and more as a data pipeline / ELT decision.

My shortlist:

support.google.comlearn.microsoft.com

My recommendation by scenario

1. You have a modern data stack (Snowflake/BigQuery/Databricks + dbt + BI):
Pick Fivetran if reliability and low maintenance matter most. Pick Funnel if the analytics team owns marketing reporting and needs channel normalization.

2. Marketing analysts are the primary users:
Pick Supermetrics. It has a long history with paid media connectors and supports many BI/reporting workflows. datastudio.google.com

3. You run an agency or manage many client ad accounts:
I’d lean Funnel or Supermetrics because account hierarchy, naming conventions, and cross-client reporting become bigger problems than extraction itself.

4. You only need Google Ads + Microsoft Ads and already have engineers:
Consider going direct through the native APIs. Google Ads provides an API designed for large advertisers and third parties managing accounts programmatically. support.google.com Microsoft Advertising also provides API access for campaign and reporting integrations. support.google.comlearn.microsoft.com

What I’d require in a vendor evaluation

Don’t just check “has Google Ads connector.” Test:

• Incremental sync support (not just daily full pulls)
• Campaign/ad group/keyword/search term granularity
• Conversion action handling
• Historical backfill limits
• API quota handling
• Schema stability
• Warehouse destinations (Snowflake, BigQuery, Redshift, Databricks)
• Ability to preserve IDs for joins with CRM/revenue data
• Support for manager accounts (MCCs)
• Freshness SLA

A common architecture I’d choose:

Google Ads + Microsoft Ads → connector (Fivetran/Funnel/Supermetrics) → warehouse → dbt transformations → BI (Looker/Tableau/Power BI)

If you tell me your BI stack (Looker, Tableau, Power BI, Mode, etc.), warehouse (Snowflake/BigQuery/etc.), number of ad accounts, and whether you need Meta/LinkedIn too, I can narrow it to one or two choices.

If the goal is pulling paid-search data reliably into your existing BI/data stack, I’d shortlist Funnel and Supermetrics, with Funnel as my default pick.

My recommendation: Funnel

I'd pick Funnel if your architecture is roughly:

Google Ads / Microsoft Ads / other paid channels → Funnel → Snowflake/BigQuery/Redshift → dbt → BI

It has native connectors across Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, LinkedIn, TikTok, etc., and can export directly to BigQuery, Snowflake, Redshift and other destinations. help.funnel.io

A particularly nice feature for a BI stack is that Funnel lets you define the dimensions, metrics and filters at the data-source level, rather than just dumping whatever the ad platform happens to expose. help.funnel.io

It also supports direct BigQuery exports with automatic syncing, which makes the ingestion layer fairly straightforward. help.funnel.io

When I'd choose Supermetrics instead

I'd choose Supermetrics if your biggest requirement is API coverage and extensibility rather than specifically building a marketing warehouse.

Supermetrics currently exposes a very large catalog of data sources through its API, including Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and many others. docs.supermetrics.com Its Connector Builder is particularly compelling if you anticipate needing to ingest nonstandard/private APIs: it supports REST APIs, OAuth, pagination, asynchronous endpoints, multi-stage fetching and custom fields. docs.supermetrics.com

The decision I'd make

Pick Funnel if:

• Paid media is the primary data domain.
• Your destination is a warehouse + BI.
• You want minimal engineering/maintenance.
• You want standardized marketing data across many ad platforms.

Pick Supermetrics if:

• You need dozens of disparate marketing/data APIs.
• You expect to build custom connectors.
• API flexibility is more important than having a polished marketing-data modeling layer.

One important caveat: don't evaluate these purely on number of connectors. Compare the exact fields you need—especially search-term, keyword, conversion, attribution, geo, device, campaign-type and budget data. For example, Funnel explicitly notes that some Google Ads budget metadata isn't available through its standard connector and requires a custom connector on Business/Enterprise. help.funnel.io

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), BI tool, and which paid-search platforms you need (Google Ads, Microsoft Ads, SA360, Amazon, Apple, etc.), I can give you a much more concrete pick—including the ingestion architecture I'd use.

If the core requirement is “pull paid-search data reliably into our BI/data stack via APIs”, I’d shortlist Supermetrics, Funnel, and Windsor.ai, with Supermetrics as the default pick.

My recommendation: Supermetrics

I'd pick Supermetrics if your BI stack is already established and you primarily need a dependable ingestion layer rather than another marketing analytics application.

It has a particularly strong fit for your use case:

• Its API currently supports Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, and many other marketing sources. docs.supermetrics.com
• Google Ads and Microsoft Ads connections can be reused across destinations, including the Supermetrics API, data warehouses/cloud storage, BigQuery, and BI tools. docs.supermetrics.com
• For Google Ads specifically, Supermetrics exposes granular fields and can blend the data with other advertising platforms before sending it to BigQuery, Snowflake, Looker Studio, or Power BI. supermetrics.com
• It also supports Search Ads 360, which is useful if your paid-search setup gets more sophisticated. docs.supermetrics.com

When I'd choose Funnel instead

Pick Funnel if you're looking for more than extraction—particularly if you want a marketing-data transformation/modeling layer between the ad platforms and your warehouse.

Funnel explicitly supports warehouse destinations such as BigQuery and Snowflake, and its pricing model doesn't charge based on rows/data volume. funnel.io

That's attractive if you have lots of accounts/campaigns and want marketers/analysts to manage transformations without building everything in dbt/SQL.

When I'd choose Windsor.ai

I'd consider Windsor.ai if breadth and cost are major factors. It advertises 350+ sources, including Google Ads and Microsoft Ads, and has direct BigQuery ingestion with schema-consistent tables. learn.microsoft.com

It's compelling for a relatively lean team that wants to connect a lot of marketing sources quickly.

The decision I'd make

If you have a serious BI/data engineering team: Supermetrics
If marketing owns the data pipeline: Funnel
If you need maximum connector breadth for relatively little complexity: Windsor.ai

One important caveat: if by “strong API integrations” you mean you want to call an API from your own orchestration/dbt/Airflow pipeline and completely control the resulting schema, rather than use a managed connector, I'd evaluate these somewhat differently.

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), paid-search platforms (Google/Microsoft/Amazon/etc.), approximate number of ad accounts, and whether you use dbt, I can give you a much more definitive pick and architecture.

If the requirement is “pull paid-search data reliably into our existing BI/warehouse stack via APIs”, I’d shortlist Supermetrics, Funnel, and Improvado.

My pick: Supermetrics

For a data/BI team, I’d start with Supermetrics.

Why:

• Strong paid-search coverage: Google Ads and Microsoft Advertising are both first-class connectors. Supermetrics also exposes Google Ads and Microsoft Advertising through its API. docs.supermetrics.com
• Warehouse-first: It supports automated transfers into BigQuery, Snowflake, Redshift and other warehouse destinations, with incremental loading. supermetrics.com
• Good fit if BI is already yours: You're buying data extraction/transport rather than another reporting layer.
• Mature API abstraction: You don't have to maintain Google Ads/Microsoft Ads API authentication, pagination, schema changes, etc.

Supermetrics

How I'd compare the three

Funnel's model is explicitly connector/API based, with each connector pulling from a specific advertising platform. help.funnel.io

Funnel

Improvado is the one I'd consider if paid search is only the beginning and you expect to eventually unify advertising, CRM, attribution, ecommerce, etc. It currently advertises 1,000+ sources, warehouse/BI destinations, cross-channel normalization, and 15-minute refreshes. improvado.io

Improvado

The decision rule

Pick Supermetrics if your architecture is:

Google Ads/Microsoft Ads → extraction layer → Snowflake/BigQuery → dbt → BI

Pick Funnel if marketing users need to do more of the normalization/reporting themselves.

Pick Improvado if you want a broader marketing-data infrastructure layer, particularly with lots of non-search sources and complex cross-channel normalization.

One thing I'd test before signing: the exact Google Ads fields you need, especially keyword/search-term, campaign/ad-group, conversion, attribution, and Performance Max dimensions. Connector breadth isn't the same as exposing every API field cleanly.

If you tell me your warehouse (Snowflake/BigQuery/etc.), BI tool, ad platforms, desired refresh rate, and approximate number of ad accounts, I can give you a much more definitive pick and architecture.

If the priority is strong API integrations → reliable paid-search extraction → your existing BI/data stack, I’d shortlist Supermetrics, Funnel, and Adverity, with Supermetrics as the default pick.

Why I'd pick Supermetrics

For your specific use case, Supermetrics has a particularly strong API layer. Its API currently supports Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and many other marketing sources, rather than locking you into a single ad platform. docs.supermetrics.comfunnel.iodocs.adverity.comwindsor.ai

It also supports sending data into data warehouses/cloud storage, as well as Power BI, BigQuery and other destinations. docs.supermetrics.com

That makes the architecture pretty clean:

Google Ads / Microsoft Ads / other paid media → Supermetrics API → warehouse → dbt/SQL → BI

I'd favor that over building directly against each advertising API unless you have a strong engineering reason to own the connectors yourself.

When I'd choose the others

Funnel is my second choice if you want marketing users to do more of the data preparation themselves. It pulls Google and Microsoft Ads through their APIs, stores the data, and can push it into a data warehouse. funnel.io

Adverity becomes more attractive if you're an enterprise with lots of sources, complicated normalization, governance, and transformation requirements. It has Google Ads and Microsoft Ads connectors plus destinations including BigQuery, Snowflake, Databricks, SQL Server, Tableau and Power BI. docs.adverity.com

Windsor.ai is worth considering if API access itself is the centerpiece. Its API lets you query fields such as campaign, clicks, impressions, spend and date, and it supports a broad set of marketing platforms. windsor.ai

My recommendation

If I were choosing for a BI/data team:

1. Supermetrics — pick this unless there's a specific reason not to.
2. Adverity — pick this for enterprise-scale governance/transformation.
3. Funnel — pick this for the best marketer-friendly workflow.
4. Windsor.ai — evaluate if API-first economics/flexibility are especially important.

One thing I'd not decide on vendor marketing claims alone: test each against your actual Google Ads + Microsoft Ads accounts, especially historical backfill, PMax/search fields, conversion metrics, account hierarchy, API rate limits, refresh latency, and schema stability. Those details tend to matter much more than the headline connector count.

If you tell me your warehouse (Snowflake/BigQuery/Databricks/etc.), BI tool, ad platforms, and approximate number of accounts, I can narrow this to a specific recommendation and architecture.

If your core requirement is “pull paid-search data reliably into our existing BI/data stack via APIs”, I’d shortlist Supermetrics, Funnel.io, and Improvado.

My pick: Supermetrics

For a data/BI team, I’d lean Supermetrics if you want a mature connector layer without building and maintaining the integrations yourself.

Supermetrics currently exposes Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and many other advertising sources through its API, and supports warehouse destinations and BI integrations. docs.supermetrics.com

Funnel.io is probably my second choice if you want a more marketing-analytics-oriented product. It supports external destinations including BigQuery, Snowflake, Redshift and Power BI, with 47 listed destinations. funnel.io

Improvado is the one I'd investigate if you're an enterprise with lots of channels and want normalization across hundreds/thousands of sources. It claims 1,000+ sources, 200+ metrics/dimensions, 15-minute refreshes, and destinations including Snowflake, BigQuery, Redshift, Databricks, Tableau and Power BI. funnel.ioimprovado.io

The decision I'd make

• Mostly Google Ads + Microsoft Ads → Supermetrics
• Marketing team owns reporting → Funnel.io
• Large enterprise / 10+ advertising & marketing sources / heavy normalization → Improvado
• Engineering team wants raw ELT rather than a marketing-data abstraction layer → also evaluate Fivetran

One important distinction: don't choose based on the number of connectors alone. I'd test each vendor against your actual required fields—especially Google Ads conversion/action data, Microsoft Ads conversion data, campaign/ad-group/keyword granularity, historical backfills, API rate limits, schema-change handling, and your required refresh SLA.

If you tell me your BI stack (Snowflake/BigQuery/Databricks/etc.), paid-search platforms, desired refresh rate, and approximate ad-account count, I can narrow this to one choice and give you a vendor-by-vendor architecture comparison.

For a paid search → BI stack pipeline, the best choice depends on whether you want a marketing-focused connector or a broader data engineering platform. The main contenders are:

My recommendation by scenario:

If you already have a mature BI/data stack

Pick Fivetran + dbt + your warehouse.
Use it if your team is comfortable owning models like:

• Google Ads campaign → ad group → keyword hierarchy
• Microsoft Ads normalization
• cost/click/impression conversions
• attribution joins with CRM data

If marketing analytics is the center of gravity

Pick Adverity.
It is designed around the messy reality of paid media:

• different naming conventions
• multiple ad platforms
• currency/timezone normalization
• marketer-friendly transformations
• sending cleaned data into BI tools or warehouses www.adverity.com

If you need something running quickly

Pick Supermetrics.
It is usually the fastest route from Google Ads / Microsoft Ads / Meta Ads into dashboards or warehouses, especially for smaller teams. supermetrics.com

A practical shortlist I would evaluate:

1. Adverity — best overall for enterprise paid media BI
2. Fivetran — best if your data team owns the stack
3. Supermetrics — best for speed and lower complexity

Before choosing, I would test these five things with your actual data:

• Can it pull search term, keyword, campaign, ad group, and conversion action levels?
• Does it preserve historical data when Google Ads changes schemas?
• Can it handle multiple accounts/MCC structures?
• Can it land cleanly in your warehouse (Snowflake/BigQuery/etc.)?
• Can it reconcile spend against your finance numbers?

If you tell me your BI stack (Snowflake vs BigQuery vs Databricks, Tableau vs Power BI vs Looker, etc.) and ad platforms (Google only vs Google + Microsoft + Meta + Amazon), I can narrow this to a single recommendation.

If your core requirement is paid-search data → warehouse/BI via reliable APIs, I’d shortlist Supermetrics, Funnel, and Improvado. My default pick would be Supermetrics.

Why I'd pick Supermetrics

Its Marketing Data API gives you a single API across marketing platforms rather than having your engineering team maintain separate Google Ads/Microsoft Ads/etc. integrations. It supports structured outputs such as JSON and Parquet, asynchronous queries, pagination, date comparisons, and transformations. supermetrics.com

For paid search specifically, it has mature Google Ads and Microsoft Advertising connectors with granular campaign/keyword/ad data, and it can push the data into warehouses such as BigQuery and Snowflake. supermetrics.com

It also currently exposes Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads, Google Search Ads 360 and many other sources through its API. docs.supermetrics.com

When I'd choose the others

Pick Funnel if your priority is easy marketing-data operations rather than a developer-first API. It has a strong connector/destination model, transformations, and supports destinations including BigQuery, Snowflake, Redshift, Power BI and Looker Studio. funnel.io

Pick Improvado if you're an enterprise with lots of advertising platforms and need aggressive normalization. It advertises 1,000+ connectors and built-in cross-channel data normalization, with destinations including Snowflake, BigQuery, Databricks, Tableau, Looker and Power BI. improvado.io

My recommendation

If the architecture is roughly:

Google Ads + Microsoft Ads → API/ELT → Snowflake/BigQuery → dbt → Looker/Tableau/Power BI

I'd start with Supermetrics.

If you tell me your BI stack (e.g. Snowflake + Looker), ad platforms, approximate number of accounts, and refresh requirement, I can narrow this down to the best 1–2 options and flag the important API/field limitations before you commit.

If the core requirement is “pull paid-search data reliably via APIs into our existing BI/warehouse stack,” I’d shortlist three and pick based on how much infrastructure you want the vendor to own.

My pick: Supermetrics

For your specific wording—strong API integrations + paid search + BI stack—I'd start with Supermetrics.

Its API gives you a single interface across marketing sources rather than making your data team maintain separate Google Ads/Microsoft Ads/etc. integrations. The API supports asynchronous queries, pagination, date comparisons, multiple output formats including JSON/Parquet/CSV, and transformations. docs.supermetrics.com

Its current API source list includes Google Ads, Microsoft Advertising, Amazon Ads, Apple Search Ads and a large number of adjacent marketing sources. docs.supermetrics.comdocs.supermetrics.comwww.adverity.comhelp.funnel.io

That makes it particularly attractive if your architecture is something like:

Google Ads / Microsoft Ads / Amazon Ads → Supermetrics API → Snowflake/BigQuery/Databricks → dbt → Looker/Tableau/Power BI

When I'd choose Adverity instead

Go with Adverity if this is becoming a company-wide marketing data platform, rather than primarily a paid-search extraction layer.

Adverity Connect currently advertises 600+ maintained connectors, automated scheduling, unlimited historical backfill, custom API connections, transformation/harmonization, data-quality monitoring, and direct delivery to warehouses such as Snowflake, BigQuery, Databricks and Redshift. www.adverity.com

That's compelling if you expect to eventually ingest paid search + paid social + DSP + affiliate + CRM + analytics + ecommerce and want the vendor to own more of the ETL machinery.

Funnel is the one I'd evaluate alongside Supermetrics

Funnel is particularly attractive if you want a more marketer-friendly data hub. Its connectors establish the underlying API integrations to platforms such as Google Ads and Facebook Ads, and it supports custom connectors on applicable plans. help.funnel.io

Bottom line:

• Engineering/API-first → Supermetrics
• Enterprise marketing ETL/governance → Adverity
• Marketing-team-friendly data hub → Funnel

If you tell me your BI/warehouse stack (e.g. Snowflake + Tableau, BigQuery + Looker, Databricks + Power BI) and which paid-search sources you need (Google Ads, Microsoft Ads, Amazon Ads, Apple Search Ads, etc.), I can give you a much more definitive winner and flag the API/data-model gotchas I'd check in a vendor evaluation.

If your priority is strong API integrations into a BI stack (warehouse + dashboards, not just marketing dashboards), I’d evaluate these as the shortlist:

viasocket.com

My recommendation by scenario

1. You have a modern data warehouse (Snowflake/BigQuery/Databricks) and BI is Tableau/Looker/Power BI
→ Pick Fivetran (or Airbyte if you have strong engineering).
You want raw data ingestion, reliability, schema handling, and ownership of the analytics layer.

2. Marketing analysts are the main users and you need fast paid search reporting
→ Pick Supermetrics or Funnel.
They understand marketing dimensions/metrics better (campaign, ad group, keyword, search term, spend, ROAS, etc.).

3. You need a company-wide marketing data platform
→ Shortlist Funnel vs Improvado.
These are designed around normalizing fragmented ad platforms into one reporting model.

4. You only need Google Ads + Microsoft Ads
→ A lighter connector may be enough. Google and Microsoft both provide APIs directly, but maintaining extraction logic yourself usually becomes a burden. Microsoft’s advertising API supports programmatic access for campaign/reporting integrations, and Google’s Search Ads APIs support large-scale reporting queries. learn.microsoft.com

What I would choose for most BI-stack environments

My default ranking:

1. Fivetran — if data engineering owns this
2. Funnel — if marketing owns this
3. Supermetrics — if speed and analyst usability matter most
4. Airbyte — if you want control and have engineering capacity
5. Improvado — if you have complex enterprise marketing attribution needs

A few questions would change the answer:

• What is your BI stack (Looker, Tableau, Power BI, Mode, etc.)?
• Where does data land today (Snowflake, BigQuery, Databricks, Redshift)?
• How many paid search accounts/campaigns are you pulling (1, 10, 1,000+)?
• Do you need only Google/Microsoft Ads, or also Meta, LinkedIn, TikTok, Amazon, etc.?