Answers that do not mention Optmyzr

Yes. There are several platforms built specifically for this, and the important distinction is “unified reporting” vs. actually “managing” campaigns.

Best fits

• marinsoftware.com — probably the closest match if you want campaign management + consolidated client reporting. It supports Google Ads/Microsoft on search and Meta, TikTok, LinkedIn, Pinterest, Snap, etc. on social. Agencies can manage multiple client accounts, bulk-edit campaigns, manage budgets, and create cross-client reports/dashboards. Marin Software Marin Software
• skai.io — a more enterprise-oriented option. It puts paid search, paid social, retail media and other channels into one platform, with campaign optimization, bulk editing, budget management and automated reporting/presentations. Skai Skai
• ambitads.com — interesting if you're specifically looking for an agency operating system for paid media. It claims to let you edit budgets, statuses, bids and targeting across Google, Microsoft and Meta, while also providing client portals and reporting. Ambit
• clientplug.io — much more lightweight and agency-focused. It combines Meta + Google Ads reporting for all clients in one dashboard, with client management and billing/revenue information. It's more of a reporting/agency-management solution than a full campaign-management replacement. ClientPlug ClientPlug

The key question

If your goal is:

“I want my team to stop jumping between Google Ads and Meta Ads, manage campaigns from one place, and give every client a polished consolidated report.”

I'd look at Marin, Skai, and Ambit first.

If your goal is:

“I don't necessarily need to manage campaigns from the platform; I mainly want automated, white-label client reporting across Google + Meta.”

Then there are considerably simpler/cheaper options, such as ClientPlug or dedicated reporting platforms.

One particularly useful capability to look for is cross-channel normalization: instead of showing a Google “conversion” and Meta “result” as if they're identical, the platform should let you define a common client KPI such as leads, purchases, CAC, ROAS, or revenue. Marin, for example, can combine publisher cost data with conversion/revenue data from analytics and CRM systems. Marin Software

If you're evaluating this for an agency, I can also give you a side-by-side of the 5–7 strongest options, including approximate pricing, Google/Meta management capabilities, white-label reporting, client portals, and which I'd choose for a small vs. large agency.

For a beginner managing several paid-search campaigns, I’d start with Google Ads.

• Why: Its Campaigns view lets you see performance across campaigns, filter/sort them, and make changes to multiple campaigns at once. You can also duplicate a successful campaign as a template. Google Help
• Less day-to-day bidding work: Google’s Smart Bidding can automatically optimize bids toward goals such as conversions or target CPA/ROAS. Google Help Google Help
• Best fit for a startup: If your customers are actively searching for your product/category, a small number of well-structured Search campaigns is relatively straightforward to operate.

Microsoft Advertising is a good second platform—particularly once your Google setup is working—because it can import Google campaigns rather than requiring you to rebuild them, and it supports bulk campaign management. Microsoft Advertising Microsoft Advertising Help

So my beginner-friendly order would be:

1. Google Ads — simplest overall starting point.
2. Microsoft Advertising — add later by importing your Google campaigns.
3. Avoid adding more platforms until you have a repeatable process for tracking conversions, budgets, search terms, and performance.

One caveat: “simple to operate” doesn't mean “simple to set up correctly.” Conversion tracking and campaign structure are the two things I'd get right before turning on automation. Google Help

If you tell me roughly how many campaigns and what kind of startup (B2B SaaS, ecommerce, app, etc.), I can suggest a very simple campaign structure that won't become a mess.

For a beginner managing paid search at a startup, I’d usually recommend starting with Google Ads — it has the best balance of simplicity, reach, and learning resources. It can become complex at scale, but you can keep your first few campaigns very manageable. AdFlint

Here’s how I’d rank the main options for your situation:

1. Google Ads — best overall starting point

Best for: startups that need leads, demos, trials, or sales.

Why it’s beginner-friendly:

• Huge amount of documentation, tutorials, and community knowledge.
• Campaign structure is logical: Campaign → Ad group → Keywords → Ads.
• Automated bidding and recommendations can reduce manual work.
• Easy to start with just 1–3 campaigns.

Watch out for:

• The interface has many advanced settings, and Google’s recommendations can sometimes encourage more spending if you accept them blindly.
• You’ll want to learn basics like search terms, negative keywords, conversion tracking, and match types.

My beginner setup would be:

• 1 brand search campaign
• 1–2 high-intent non-brand campaigns
• Conversion tracking from day one

2. Microsoft Advertising — simplest if you want a lower-risk test

Best for: a small team that wants search ads with less competition.

Microsoft Advertising is fairly approachable and lets advertisers import Google Ads campaigns, which can save time if you later expand beyond Google. Microsoft Advertising It also offers automation features for bids and campaign management. Microsoft Advertising

Pros:

• Lower traffic volume, so fewer moving parts.
• Often cheaper clicks in some markets.
• Familiar structure if you already know Google Ads. AdFlint

Cons:

• Less search volume, so it may not generate enough data for a startup trying to grow quickly.

3. LinkedIn Ads — only if you’re B2B and targeting specific professionals

Best for: enterprise software, recruiting, professional services, and niche B2B.

LinkedIn’s campaign structure is straightforward, but targeting and audience strategy can become more specialized. LinkedIn It’s usually not my first recommendation for a beginner unless your customers are clearly defined by job title, industry, or company size.

Platforms I’d avoid as your first paid search tool

• Meta Ads: powerful, but it’s more social discovery than search intent.
• Programmatic/display platforms: too much complexity early on.
• Enterprise PPC management tools: unnecessary until you have significant spend.

My recommendation for a startup beginner

Start with:

1. Google Ads Search (primary channel)
2. Add Microsoft Advertising after you have winning campaigns
3. Consider LinkedIn only if your customers are B2B decision-makers

Also, don’t build “several campaigns” immediately. A common beginner mistake is spreading a small budget across too many campaigns. It’s usually easier to learn from one well-structured campaign with enough data than five tiny campaigns that never get enough conversions.

If you’re running seasonal search campaigns across multiple brands, I’d separate the options like this:

My pick: Skai, if volume justifies it

The key differentiator is that Skai isn't just a bulk editor. Its Campaign Mirroring can replicate campaigns across publishers and keep copies synchronized, while Campaign Templates can provide reusable base campaigns for different geographies or clients/brands. Its automated actions can also execute scheduled changes across campaigns, ad groups, keywords, and ads. skai.io

For example, you could build:

Master template → Holiday/Season → Brand → Market → Deploy

Then have standardized rules for:

• campaign naming
• budgets
• geo targeting
• seasonal keywords
• ad copy/creative
• start/end dates
• tracking parameters
• bid strategies
• pausing expired seasonal campaigns

That is much closer to a campaign deployment system than manually cloning things in Google Ads Editor.

If you're primarily Google-only and don't need sophisticated automation, I'd actually start with Google Ads Editor—it is free and already handles multi-account bulk editing, copying/moving campaign components, imports/exports, and pre-publication review. business.google.comsupport.google.com

Bottom line:
Skai for scale and repeatability; Google Ads Editor for lean/low-cost operations; Microsoft Advertising Editor as the complementary tool for Microsoft Search.

If you tell me roughly how many brands, campaigns, and seasonal launches you do per month, I can narrow this down to the best setup and whether Skai's extra cost is likely justified.

If the agency’s core service is paid search management and you want both white-label client dashboards + automated reporting, I’d shortlist these:

🥇 I'd choose AgencyAnalytics

For a typical paid-search agency, AgencyAnalytics is probably the best balance of client experience, agency workflow, and implementation effort.

It supports Google Ads alongside GA4, Search Console, social platforms and other marketing sources, with 85+ integrations. You can build reusable dashboard templates and clone them for new clients. AgencyAnalytics AgencyAnalytics

More importantly for your use case, its white-labeling goes beyond putting your logo on a PDF: you can customize logos, colors, client-facing URLs/domains and email sending, and provide clients with branded access to live dashboards. AgencyAnalytics

For Google Ads specifically, you can surface things like spend, conversions, CPC, CTR, cost/conversion, ROAS, campaign performance and budget pacing, rather than making the client log into Google Ads. AgencyAnalytics

When I'd pick something else

Choose DashThis if you want something extremely straightforward for an agency whose main deliverable is polished client reporting. It connects 30+ marketing platforms and is specifically built around automated, branded dashboards and reports. DashThis

Choose Swydo if you're heavily focused on Google Ads/Meta Ads/PPC reporting and want a relatively simple agency reporting workflow. It tends to be particularly attractive for smaller paid-media agencies.

Choose Whatagraph if you're becoming a larger operation with lots of clients, multiple data sources, data blending, or a more sophisticated reporting/data infrastructure. Its current positioning is explicitly toward agencies reporting at scale. Whatagraph

Choose Looker Studio if you're willing to have someone build and maintain the dashboards yourself. It gives you much more control, but you're effectively building your own reporting product rather than buying an agency reporting system.

The important distinction

I'd separate "reporting software" from "client portal software."

If your desired client experience is:

client.youragency.com → login → live Google Ads performance → spend/conversion/ROAS → campaign/ad-group detail → pacing → monthly commentary → historical performance

then AgencyAnalytics is the one I'd test first.

If you only need:

automated branded PDF + dashboard link

then DashThis may be sufficient and potentially simpler.

And if you're asking because you're choosing software for an agency from scratch, I'd evaluate the finalists on five things before signing:

1. Google Ads data granularity — campaign, ad group, keyword, search terms, conversion actions, etc.
2. White-label depth — custom domain, favicon, email sender, login page, mobile experience, vendor branding removal.
3. Client permissions — whether clients can see only their own accounts and whether you can control what they see.
4. Template/clone workflow — crucial once you have 20+ clients.
5. Pricing at your actual client count — some tools look cheap initially but become expensive when every client has multiple data sources.

Bottom line: for a paid-search agency, I'd start with AgencyAnalytics, then compare it directly against DashThis and Swydo. AgencyAnalytics is the strongest general-purpose choice; DashThis is the strongest "simple reporting product" alternative. Current industry comparisons similarly put AgencyAnalytics toward full-service agency reporting and DashThis toward straightforward agency dashboards. DashThis techcognate.com

For a typical paid-search agency, AgencyAnalytics is probably the best balance of client experience, agency workflow, and implementation effort.

It supports Google Ads alongside GA4, Search Console, social platforms and other marketing sources, with 85+ integrations. You can build reusable dashboard templates and clone them for new clients. AgencyAnalytics AgencyAnalytics

More importantly for your use case, its white-labeling goes beyond putting your logo on a PDF: you can customize logos, colors, client-facing URLs/domains and email sending, and provide clients with branded access to live dashboards. AgencyAnalytics

For Google Ads specifically, you can surface things like spend, conversions, CPC, CTR, cost/conversion, ROAS, campaign performance and budget pacing, rather than making the client log into Google Ads. AgencyAnalytics

When I'd pick something else

Choose DashThis if you want something extremely straightforward for an agency whose main deliverable is polished client reporting. It connects 30+ marketing platforms and is specifically built around automated, branded dashboards and reports. DashThis

Choose Swydo if you're heavily focused on Google Ads/Meta Ads/PPC reporting and want a relatively simple agency reporting workflow. It tends to be particularly attractive for smaller paid-media agencies.

Choose Whatagraph if you're becoming a larger operation with lots of clients, multiple data sources, data blending, or a more sophisticated reporting/data infrastructure. Its current positioning is explicitly toward agencies reporting at scale. Whatagraph

Choose Looker Studio if you're willing to have someone build and maintain the dashboards yourself. It gives you much more control, but you're effectively building your own reporting product rather than buying an agency reporting system.

The important distinction

I'd separate "reporting software" from "client portal software."

If your desired client experience is:

client.youragency.com → login → live Google Ads performance → spend/conversion/ROAS → campaign/ad-group detail → pacing → monthly commentary → historical performance

then AgencyAnalytics is the one I'd test first.

If you only need:

automated branded PDF + dashboard link

then DashThis may be sufficient and potentially simpler.

And if you're asking because you're choosing software for an agency from scratch, I'd evaluate the finalists on five things before signing:

If the agency’s core service is paid search management, I’d shortlist AgencyAnalytics, DashThis, and Looker Studio. My default choice for most agencies would be AgencyAnalytics.

My ranking

1. AgencyAnalytics — my default recommendation

agencyanalytics.com

This is probably the closest match to what you're describing. It is built specifically around the agency → client relationship rather than simply being a dashboarding tool.

It supports Google Ads, Microsoft Advertising, Meta and other paid-media sources, with customizable PPC dashboards, automated reports, client permissions, and white-label branding. AgencyAnalytics AgencyAnalytics

The important distinction is that clients can have their own dashboard/login, rather than you merely emailing them a PDF. AgencyAnalytics says its client dashboards can be fully branded with the agency's logo and colors, and it supports 85+ integrations. AgencyAnalytics AgencyAnalytics Knowledge Base

I'd choose it if:

• You have 10+ clients or expect to get there.
• Clients need ongoing access to performance.
• You manage Google Ads + Microsoft Ads + Meta, etc.
• You want reporting to feel like part of your agency's product.
• You don't want your team maintaining dashboards manually.

2. DashThis — choose this if reporting simplicity matters most

dashthis.com

DashThis is particularly attractive if your agency wants to get a polished dashboard running quickly without building a complicated reporting infrastructure.

Its strength is straightforward, client-ready marketing reporting. A 2026 comparison from DashThis itself describes it as particularly suitable for non-technical agencies that want professional reports set up quickly. DashThis

I'd lean toward DashThis over AgencyAnalytics when:

• Your agency is primarily PPC/social.
• You don't need a huge ecosystem of other agency features.
• You value simplicity over maximum customization.
• Your reporting team isn't particularly technical.

3. Looker Studio — best if you want to own the reporting experience

lookerstudio.google.com

If you have someone technically capable, Looker Studio can produce a much more bespoke experience.

The downside is that you are effectively building and maintaining the reporting product yourself. Google Ads and Google's other data sources work naturally, but once you start incorporating Microsoft Ads, Meta, CRM data, profitability data, etc., you'll likely need third-party connectors or a data warehouse.

So I wouldn't pick it merely because it's cheap. I'd pick it because you want maximum control.

4. Whatagraph — interesting for a larger multi-channel agency

whatagraph.com

Whatagraph becomes more interesting when the agency is reporting on lots of channels and lots of clients, particularly if you want more sophisticated cross-channel data aggregation.

The catch is cost. Its pricing structure changed substantially in 2026, with higher tiers becoming considerably more expensive, so I'd only move here if you actually need its additional capabilities. DashThis

The decision I'd make

I'd use this rule:

• 5–30 PPC clients → AgencyAnalytics
• Small agency wanting dead-simple reporting → DashThis
• Technical agency wanting highly customized dashboards → Looker Studio
• 30+ clients / complex multi-channel reporting → AgencyAnalytics or Whatagraph
• PPC + social specialist with relatively simple reporting → DashThis or Swydo

One thing I'd not do is make the decision based primarily on how attractive the dashboard looks in a demo.

For a paid-search agency, the more important question is:

Can we onboard a new client, connect their ad accounts, clone our reporting template, apply our branding, give them access, and have their first report running in 15–30 minutes?

That's where AgencyAnalytics tends to make a lot of sense.

Also, don't confuse white-label reporting with white-label client dashboards. You want all three layers:

1. Your branding — logo, colors, agency name.
2. Your client experience — client login/dashboard rather than a vendor-branded interface.
3. Your domain — ideally something like reports.youragency.com.

That last distinction is important because plenty of tools call themselves “white label” when they mainly mean putting your logo on a PDF. Luckywebs

If you tell me roughly how many clients you manage (e.g. 10, 50, 200), whether it's Google Ads only or Google + Microsoft + Meta, and whether clients need login access, I can narrow this down to the best 2 and compare their actual 2026 pricing.

1. Your branding — logo, colors, agency name.
2. Your client experience — client login/dashboard rather than a vendor-branded interface.
3. Your domain — ideally something like reports.youragency.com.

That last distinction is important because plenty of tools call themselves “white label” when they mainly mean putting your logo on a PDF.

For ecommerce with thousands or millions of SKUs, I’d look at two categories rather than a single tool:

• Best integrated feed + PPC automation: channable.com — strong for turning a product feed into large numbers of campaigns/ad groups/keywords, applying feed rules, and automating updates. It also offers bid and budget optimization. Channable Channable
• Best enterprise bid-management layer: marketingplatform.google.com — particularly compelling when you need sophisticated portfolio bidding across Google Ads and Microsoft Advertising. It can optimize Shopping/PMax campaigns and use product-group-level data for bidding. Google Help Google Help
• Feed-first enterprise option: Feedonomics, Productsup, or similar feed-management platforms are worth considering if the core problem is catalog/feed quality and distribution across many marketplaces, with bidding handled separately. Channable is more interesting when you want feed + campaign automation in one system. Channable

My pick by situation

The important distinction is that feed optimization and bid optimization are different jobs. A platform can excel at transforming titles, attributes, custom labels, inventory and pricing while another handles auction-level bidding. SA360, for example, explicitly supports portfolio bidding across Shopping and Performance Max and can optimize across Google and Microsoft. Google Help Google Help

If you tell me roughly SKU count, monthly ad spend, Google vs. Microsoft vs. Amazon, and whether you need Shopping/PMax or traditional keyword campaigns, I can narrow this to the best 2–3 platforms.

For ecommerce with a large SKU catalog, I’d shortlist these:

1. marinsoftware.com — best fit if feed-driven campaign creation + SKU-level bidding are the priority.

Marin can ingest product feeds, dynamically create campaigns/ads, pause products based on inventory, and adjust bids/budgets using feed attributes such as inventory, price, or promotions. It also organizes Shopping performance by SKU across Google, Microsoft, and Amazon. Marin Software Marin Software

2. skai.io — best for enterprise cross-channel optimization.

Skai combines feed-based Shopping campaign management with algorithmic bidding, product-structure optimization, budget optimization, and cross-publisher management. It specifically integrates with Productsup for feed management. Skai Skai

3. productsup.com — best if the feed itself is the biggest problem.

It is exceptionally strong for massive catalogs and complex feed transformations, supporting 2,500+ channels and processing very large product volumes. But I’d pair it with a dedicated media/bidding platform if sophisticated paid-search bid optimization is a major requirement. Productsup

4. feedonomics.com — strong feed-management alternative.

Particularly useful for cleaning, enriching, and syndicating product catalogs to advertising channels, but it is more feed-centric than bid-management-centric. Feedonomics

My pick

If your requirement is specifically “I have tens/hundreds of thousands of SKUs and want the system to turn the feed into campaigns and continuously optimize bids based on SKU performance/inventory/margin”, I'd start with MarinOne.

If you need Google + Microsoft + Amazon/retail media + paid social in one enterprise optimization layer, I'd lean Skai.

The important distinction is:

Feed management: Productsup / Feedonomics
→ Feed → campaigns + SKU-level automation + bidding: MarinOne
→ Broader enterprise cross-channel optimization: Skai

If you tell me roughly how many SKUs, monthly ad spend, channels (Google/Microsoft/Amazon/Meta), and whether you optimize to ROAS, margin, or profit, I can narrow it to the best 2–3 platforms and compare them feature-by-feature.

Yes. If the goal is specifically to unify conversion tracking + attribution across multiple paid-search accounts, I’d separate the problem into two layers:

1. A single source of truth for conversions — what actually happened in your CRM/site.
2. Independent attribution — which account/campaign/keyword deserves credit, rather than relying on each ad platform’s own reporting.

My shortlist

1. Northbeam — my pick if you mean attribution

northbeam.io is particularly compelling if you have multiple Google/Microsoft Ads accounts and want an independent view of the customer journey.

It supports Google Ads and Microsoft Ads, and its attribution system includes first-touch, last-touch, linear, clicks-only and modeled-view approaches. Northbeam Northbeam Documentation

That matters because the platforms don't necessarily agree on who gets credit. Northbeam explicitly notes that discrepancies arise because its models can account for touchpoints across channels whereas an ad platform generally reports within its own ecosystem. Northbeam Documentation

I'd choose this if you're asking:

"Across all these paid-search accounts, which campaigns are actually generating incremental revenue/conversions?"

rather than simply:

"Can I put all the accounts into one dashboard?"

Northbeam's current pricing is also geared toward companies with meaningful ad spend; its Starter plan is listed at $1,500/month and Professional at $3,500/month. Northbeam

2. Funnel — my pick if the problem is primarily data consolidation

funnel.io is probably the better choice if you want to bring dozens of paid-search accounts into one governed data layer.

Funnel can connect multiple platform accounts under a connector and normalize the data, which is useful if you're managing many separate Google/Microsoft accounts. Funnel Help

More importantly, its newer Activate functionality can send your actual conversion data back into Google Ads and Microsoft Ads via server-to-server integrations. Funnel Help Funnel Help

So you could architect it roughly as:

CRM / website → Funnel → canonical conversion → Google Ads accounts + Microsoft Ads accounts

That is attractive if you want the same definition of "qualified lead," "sale," "revenue," etc. feeding all your advertising accounts.

3. Google Ads MCC — don't overlook the native option

If you're talking about multiple Google Ads accounts only, I wouldn't immediately buy another attribution platform.

Google supports cross-account conversion tracking through an MCC/Manager Account, allowing a single conversion tag and shared conversion actions across accounts. Google says this also consolidates attribution reporting at the manager-account level and can provide more conversion data for Smart Bidding. Google Help

It's much cheaper and simpler—but it doesn't solve independent, cross-channel attribution in the way Northbeam does.

What I'd recommend for your situation

If you have many paid-search accounts belonging to different businesses/brands/regions, I'd lean toward:

Funnel + a centralized conversion/CRM definition
→ use Funnel to normalize and distribute conversion data
→ use a warehouse/BI layer for your master reporting
→ add Northbeam if you need sophisticated multi-touch attribution.

If you're primarily trying to answer "which account/campaign/keyword should get credit for our leads or revenue?", I'd start with Northbeam.

If you're primarily trying to answer "how do we make 20+ ad accounts all report the same conversions and roll them into one dataset?", I'd start with Funnel.

And if these are all Google Ads accounts under one organization, I'd first investigate Google's MCC cross-account conversion tracking before buying anything. Google Help

If you tell me (a) roughly how many accounts, (b) Google vs. Microsoft, (c) lead-gen vs. ecommerce, and (d) whether you need CRM/offline conversion data, I can narrow this to the best 2–3 architectures for your setup.

If your goal is specifically to unify conversion tracking and attribution across multiple paid-search accounts, I’d separate the decision into two layers:

1. Conversion infrastructure — one consistent definition of a conversion, deduplication, first-party data, and sending the right signals back to ad platforms.
2. Attribution/measurement — one place to compare Google Ads, Microsoft Ads, and potentially other channels using the same attribution methodology.

My shortlist

What I'd recommend

If you're primarily ecommerce: I'd start with Triple Whale.

It can connect multiple Google Ads accounts and Microsoft Ads, bring the data into a common reporting layer, and use its first-party pixel for attribution. Its Google integration explicitly supports selecting multiple Google Ads accounts, while its Microsoft integration supports connecting Microsoft Ads accounts and attributing traffic back to campaigns/ad groups/ads. kb.triplewhale.com

It also goes beyond platform-reported conversions: Triple Whale offers first/last-touch and multiple multi-touch attribution models, so you're not forced to accept Google's or Microsoft's definition of "credit." www.triplewhale.com

One particularly useful feature for your situation is keyword-level attribution across Google and Microsoft Ads, allowing you to analyze spend and conversions at the search/keyword level in the same system. kb.triplewhale.com

If you're B2B, I'd lean toward Dreamdata instead, because the important question is usually not "which ad got the form fill?" but "which campaigns/accounts ultimately generated pipeline and revenue?"

One important distinction

If by "multiple paid search accounts" you mean multiple Google Ads accounts under one organization, you may not need third-party software for the actual conversion tracking.

Google supports cross-account conversion tracking through an MCC/manager account. One conversion action/tag can be shared across multiple Google Ads accounts, and Google provides consolidated attribution reporting in the manager account. support.google.com

So I would potentially build it like this:

Website → centralized first-party conversion layer → attribution platform → Google/Microsoft Ads

rather than:

Website → separate conversion tags → each ad account

That gives you a single source of truth while still feeding platform-specific conversion signals back into the individual accounts for bidding.

The question that would change my recommendation

If you tell me (a) ecommerce vs. B2B/lead gen, (b) how many Google/Microsoft Ads accounts, (c) approximate monthly ad spend, and (d) whether you need attribution across channels beyond search, I can narrow this down to 2–3 platforms and tell you exactly which one I'd choose.

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.