ChatGPT answers, unedited

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.?