Answers that do not mention Optmyzr

For a small ecommerce team on a tight budget, I’d prioritize a tool that combines Google Ads + Meta (Facebook/Instagram) without charging enterprise-level fees.

My shortlist

Yes. There are now several platforms aimed specifically at agencies that want to manage Google/search and Meta/social campaigns together and roll the results up into client reporting.

The important distinction is whether you want campaign management + reporting or primarily reporting/analytics.

If you want to actually manage campaigns

• ambitads.com — Probably closest to what you're describing. It says agencies can edit budgets, bids, statuses and targeting across Google Ads, Microsoft Ads and Meta from one interface, as well as launch campaigns and produce unified client reporting. Ambit
• clientplug.io — More of an agency operating system. It combines Meta + Google campaign data with client management, billing, tasks and white-label reporting. ClientPlug ClientPlug

If reporting consolidation is the bigger problem

• nopanicreport.com — Focused on Google + Meta, with unified dashboards, automated branded reports, budget pacing and anomaly detection. It appears designed more for monitoring/reporting than campaign editing. NoPanic Report
• kampalo.com — Unifies Google Ads, Meta Ads, GA4, Search Console and organic social, with client-specific report templates. Kampalo
• useduneside.com — Particularly interesting if you also need SEO/organic visibility in the client report: Google Ads, Meta, GA4 and Search Console in one reporting layer, with white-label reports and scheduled delivery. Used Duneside
• reportsmate.com — Broader reporting across Google, Meta, LinkedIn, TikTok, GA4, Search Console and other sources, with automated white-label reports. ReportsMate

The architecture I'd look for

If you're running an agency, the ideal setup is essentially:

Google Ads + Meta Ads → unified campaign data → client-level dashboard → automated branded report

And ideally you can see things like:

That blended view is the part that can be much more useful than simply putting a Google Ads report next to a Meta report.

If you tell me roughly how many clients/accounts you manage and whether you need to actually launch/edit campaigns from the platform (rather than just report on them), I can narrow this down to 3–5 options and compare pricing, Google/Meta management, white-label reporting, automation, and client dashboards.

Yes. There are several platforms designed for agencies that want to manage paid search + paid social together and roll up reporting into client-facing dashboards/reports. The right choice depends on whether you mainly need campaign execution, reporting, or both.

Common agency setups

Option 1: Reporting layer (most common)

• Continue optimizing inside:

• Google Ads
• Microsoft Ads
• Meta Ads Manager
• LinkedIn Ads
• TikTok Ads
• Connect everything into a reporting tool.
• Deliver:

• spend
• impressions/clicks
• conversions
• CPA/CPL
• ROAS
• channel comparisons
• branded client dashboards/PDFs

For example, AgencyAnalytics supports combining paid, social, search, and web data into unified client views with integrations across platforms such as Google Ads, Facebook, LinkedIn, and Google Search Console. AgencyAnalytics

Option 2: Operations platform
If your team wants to actually manage campaigns from one interface (budget pacing, campaign changes, account oversight), look at platforms that go beyond reporting. Ambit positions itself around reporting, editing, and launching campaigns across channels including Google Ads, Microsoft Ads, and Meta. Ambit

What I’d evaluate before choosing

• Client count: 5 clients vs. 200 clients changes the ideal tool.
• Channels: Google + Meta only is easy; add TikTok, LinkedIn, Amazon, CTV, etc. and integrations matter.
• Attribution model: Make sure clients understand why Meta, Google, and analytics platforms may report different conversion numbers.
• White labeling: Look for custom domains, logos, automated emails, and client permissions.
• Optimization workflow: Many “reporting” tools don’t actually let you make campaign changes.

If you’re an agency managing multiple clients, a typical stack is:

• Google Ads/Meta/etc. for execution
• A reporting platform for client visibility
• A CRM/project system for client management

If you tell me roughly:

1. number of clients,
2. channels you run (Google, Meta, LinkedIn, TikTok, etc.), and
3. whether you need to edit campaigns inside the platform or just report,

I can narrow this down to a few best-fit options.

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

Why it tends to be the simplest default:

• It has a large ecosystem of beginner guides, templates, and integrations.
• You can keep multiple campaigns organized within one account rather than juggling separate tools.
• Google’s automated bidding and campaign types can reduce the amount of manual optimization you need to do.
• It’s a sensible place to learn the fundamentals of keywords, search terms, conversions, budgets, and campaign structure.

Microsoft Advertising is another relatively straightforward option, particularly if you already have Google Ads campaigns. Its current Import tools can copy campaigns from Google Ads and even schedule recurring imports, reducing duplicate setup work. Microsoft Advertising Microsoft Advertising

My practical recommendation

If you're starting from scratch:

Google Ads → 3–5 tightly structured campaigns → simple conversion tracking → automated bidding once you have enough data.

I wouldn't add a third-party campaign-management platform yet. The extra abstraction can actually make learning harder.

If you tell me roughly how many campaigns, your monthly ad budget, and whether you're selling SaaS, ecommerce, or services, I can suggest a very simple campaign structure that won't become a mess as the startup grows.

If you’re a beginner managing several paid-search campaigns, I’d start with Google Ads if Google Search is where your customers are. It’s the standard platform for search advertising, and you’ll get the most useful experience learning it.

For keeping complexity down:

• Google Ads — Best starting point if search volume and customer intent matter. Start with a small number of campaigns, use automated bidding cautiously, and keep the account structure simple.
• Microsoft Advertising — Worth adding once your Google setup is working. Its current interface emphasizes simpler campaign management, and it can import selected Google Ads campaigns, including recurring imports, which reduces duplicate work. Microsoft Advertising Microsoft Advertising
• Meta Ads — Useful for paid social, but it isn't really a substitute for search advertising; the targeting and campaign structure are different.

For a startup beginner, my simple workflow would be:
Google Ads → 2–5 well-structured campaigns → get conversion tracking working → optimize → optionally import those campaigns into Microsoft Advertising.

If by "platform" you mean a tool that manages Google + Microsoft + Meta from one dashboard, rather than the ad networks themselves, that's a different category—I can give you the simplest options for that.

If by “search campaigns” you mean managing SEO/search-marketing work across an in-house team, Semrush currently has the stronger collaboration feature set.

Semrush specifically supports individual team logins, shared folders and keyword lists, personalized dashboards, activity monitoring, notes, and admins who can manage access and usage. Its sharing system also lets teams collaborate on reports and assets without repeatedly exporting data. Semrush Semrush

Ahrefs has substantially improved its team capabilities: its workspace supports Owner/Admin/Member roles, and projects and folders can be shared with specific people or the whole workspace. Enterprise users also get more granular guest access. Ahrefs Help Center Ahrefs Help Center

Practical takeaway

For an in-house marketing team with SEO, content, PPC, and managers/stakeholders working together, I'd look at Semrush first, because collaboration is integrated into a broader marketing workflow. Semrush Semrush

If your team is primarily SEO-focused and collaboration means sharing keyword research, backlink research, audits, and projects, Ahrefs is also a strong fit, particularly with its newer workspace permissions. Ahrefs Help Center

If you tell me your team size (e.g. 3, 10, 25 people) and whether you mean SEO campaigns, Google Ads campaigns, or both, I can compare the two specifically on permissions, workflow, reporting, and total seat cost.

For seasonal search campaigns across multiple brands, the best solution usually depends on whether your bottleneck is speed of build, cross-account scale, or automated recurring launches.

A common enterprise stack would look like this:

My recommended workflow for your use case

For multiple brands running seasonal search:

1. Create a master campaign template

• Naming conventions
• Campaign/ad group structure
• Tracking parameters
• Audiences
• Extensions/assets
• Budget rules
• Build seasonal variants in Google Ads Editor

• Clone last year's campaigns
• Swap seasonal keywords
• Update ad copy/promotions
• Change landing pages
• Validate before publishing

Google Ads Editor is specifically designed for bulk campaign creation and edits. Google

3. Use Search Ads 360 if you have many brands or markets

• Centralize multiple advertisers
• Schedule automated changes
• Apply rules at scale
• Use templates/data feeds for repeatable launches

Google Support Google Blog1. Automate the calendar triggers
Examples:

• Launch Black Friday campaigns 14 days before event
• Increase budgets during promotion windows
• Pause expired promotions automatically
• Swap creative after sale end dates

Short recommendation by company size

• 5–20 brands / hundreds of campaigns: Google Ads Editor + scripts is often enough.
• 20+ brands / multiple countries / agencies: Search Ads 360 is usually the better operational platform.
• Thousands of recurring seasonal campaigns: Build a template-driven automation layer (SA360 Templates, API, or internal tooling).

If you share roughly number of brands, monthly campaign launches, search engine mix (Google only vs Google + Microsoft), and whether campaigns are product-feed driven, I can narrow this to a specific stack.

If you're running seasonal Search campaigns across multiple brands/accounts, I’d recommend a template + automation workflow centered on Google Ads Editor and Google Ads Scripts, rather than trying to do everything manually in the Google Ads UI.

Best fit: Google Ads Editor + Scripts

How I'd structure it

For something like Black Friday → Christmas → Valentine's → Mother's Day, I'd create a master template for each campaign archetype:

MASTER SEARCH TEMPLATE
│
├── Brand
│   ├── Brand - Core
│   ├── Brand - Generic
│   └── Brand - Competitor
│
└── Seasonal
    ├── [SEASON] - Brand
    ├── [SEASON] - Generic
    └── [SEASON] - Promo

Then give every campaign standardized labels such as:

BRAND=Acme
SEASON=Christmas
YEAR=2026
STATUS=SEASONAL
LAUNCH=2026-12-01
PAUSE=2026-12-26

Google Ads labels are particularly useful here because Scripts can filter campaigns by labels and process groups of campaigns across runs. Google for Developers

Your deployment process becomes:

1. Select template → 2. Clone → 3. Replace brand/season variables → 4. Apply budgets/URLs/keywords → 5. QA → 6. Schedule → 7. Publish

That can turn what is currently a multi-hour manual process into something much closer to a repeatable deployment pipeline.

If you also run Microsoft Ads

I'd add Microsoft Advertising's Google Import rather than maintaining a completely separate workflow. Microsoft supports scheduled imports from Google Ads, including daily/weekly/monthly recurring imports. Microsoft Learn

So the architecture could be:

Master Google campaign → Google Ads Editor/Scripts → Google Ads → scheduled Microsoft import

That is especially attractive if Google is your primary platform and Microsoft is essentially a secondary deployment target.

What I would not use as the primary solution

I wouldn't make a third-party PPC management platform your first choice unless you need a broader agency-management layer—reporting, approvals, budgets, client workflows, etc.

For "we have 20 brands, seasonal campaigns recur constantly, and we need to clone/deploy them quickly," I'd first build the process around Google's native tooling. Google explicitly supports copying campaigns between accounts through Editor/manager accounts, while Scripts provide the automation layer that Editor itself doesn't provide. Google Help Google for Developers

My recommendation:
Google Ads Editor + Google Ads Scripts + standardized campaign templates/labels, with Microsoft Google Import downstream if you're also running Bing.

If you tell me roughly how many brands/accounts you manage and whether the campaigns are mostly Search, PMax, or both, I can lay out the exact automation architecture—including what should be templated, what should be scripted, and what can be fully one-click.

If your agency’s core service is paid search management and you want white-label client dashboards + automated reporting, I’d narrow the field to AgencyAnalytics, DashThis, Swydo, and Whatagraph.

Quick comparison

My take

AgencyAnalytics is the one I'd investigate first if you want the reporting platform to become part of your agency's overall client experience. It supports 85+ integrations, including Google Ads and GA4, and offers branded client dashboards and reports. AgencyAnalytics

agencyanalytics.com

DashThis is particularly attractive if you're primarily selling PPC management and don't want your team spending much time building dashboards. It has 30+ integrations, PPC-focused templates, automated refreshes, and white-label dashboards/reports. DashThis

dashthis.com

Swydo is worth looking at if most of your clients are Google Ads/Meta Ads/social clients and you want a relatively lean reporting operation. Current 2026 comparisons specifically identify it as a PPC/social-focused option, with white-labeling included across its plans. DashThis Databox

Whatagraph becomes more interesting when you're running a larger operation and need sophisticated cross-channel data blending and more advanced data workflows. Its current pricing is substantially higher than the lighter-weight options, so I'd evaluate it based on your client/account volume rather than simply its feature list. DashThis SaaSTracker

The decision I'd make based on agency size

• 5–15 PPC clients: DashThis or AgencyAnalytics
• 15–50 clients: AgencyAnalytics or Swydo
• 50+ clients / complex reporting infrastructure: Whatagraph or AgencyAnalytics
• Mostly Google Ads and very simple reporting: DashThis
• PPC + SEO + GA4 + call tracking + lots of integrations: AgencyAnalytics

One thing I'd not make the primary deciding factor is the number of integrations. For a paid-search agency, I'd instead test these five things with an actual client account:

1. How quickly you can clone a dashboard for a new client.
2. Whether the client can log in and see live data without seeing the software vendor's branding.
3. Whether Google Ads metrics map cleanly to the KPIs you actually sell—spend, conversions, CPA, ROAS, impression share, search terms, etc.
4. How much work it takes to create a good monthly narrative around the numbers.
5. What your cost is at 10, 25, 50, and 100 clients. Pricing models differ substantially, and that can matter more than the headline monthly price. Databox MixedMetrics

If you tell me roughly how many clients you have (e.g. 10, 30, 100+) and whether you manage Google Ads only or Google + Microsoft + Meta, I can narrow this to 2–3 options and compare the actual economics/features for your agency.

agencyanalytics.com

DashThis is particularly attractive if you're primarily selling PPC management and don't want your team spending much time building dashboards. It has 30+ integrations, PPC-focused templates, automated refreshes, and white-label dashboards/reports. DashThis

dashthis.com

Swydo is worth looking at if most of your clients are Google Ads/Meta Ads/social clients and you want a relatively lean reporting operation. Current 2026 comparisons specifically identify it as a PPC/social-focused option, with white-labeling included across its plans. DashThis databox.com

For a paid-search-focused agency, I’d shortlist:

• AgencyAnalytics — strongest all-around choice if you want polished white-label dashboards, Google Ads integration, custom domains, automated reports, and room to add SEO/social later. AgencyAnalytics AgencyAnalytics
• DashThis — worth considering if reporting simplicity is the priority. It offers PPC dashboards, templates, custom branding, custom domains, and automated reporting. DashThis
• ReportGarden — consider if you also want budgeting, proposals, and invoicing alongside reporting. The Digital Project Manager

My starting point: AgencyAnalytics. It appears particularly well matched to an agency selling managed paid search because the client-facing experience can be thoroughly branded while consolidating Google Ads and other marketing data. AgencyAnalytics

If you tell me roughly how many clients you manage (e.g. 10, 50, 200) and whether clients need live dashboards, monthly reports, or both, I can compare the economics and features more specifically.

Yes. If you manage multiple Google Ads accounts (especially under an MCC/Manager Account), there are tools that can help with cross-account negative keyword management, search-term waste detection, and preventing accidental blocking of valuable traffic. The best choice depends on whether you want a native Google solution or automation/AI.

Recommended options

SearchSavior QueryGuard Cascader## My typical recommendation by scenario

1. You manage 2–10 accounts and want control

Use Google Ads Manager Account + shared negative keyword lists first. Google allows manager accounts to create shared negative keyword lists that can be applied across client accounts. Changes to a shared list propagate to campaigns using that list. Google Help

A practical structure:

• Global exclusions

• jobs
• careers
• free
• DIY (if not relevant)
• support
• login
• Industry exclusions
• Client-specific exclusions
• Campaign-specific exclusions

2. You manage many accounts or significant spend

I would look at Cascader or a similar automation layer. These tools are designed to scan search terms, surface wasted spend, and help apply negatives with approval workflows rather than relying entirely on manual reviews. Cascader

3. Your biggest problem is negative keyword mistakes

Consider SearchSavior-type tooling. A common hidden issue is adding a negative keyword that unintentionally blocks profitable queries. Tools focused on conflict detection can catch this before it costs money. SearchSavior

Also worth implementing regardless of tool

• Weekly search-term audits
• Negative keyword ownership rules (who can add negatives)
• Change-history reviews
• A shared negative keyword taxonomy
• Alerts for sudden drops in impressions/conversions after negative changes

If you tell me:

1. how many Google Ads accounts you manage,
2. monthly spend range, and
3. whether you need Google Ads only or Google + Microsoft Ads,

I can narrow this down to the best-fit stack.

For a large ecommerce catalog, I’d separate the problem into feed management and bid/campaign optimization:

My shortlist

1. Feedonomics + Skai — strongest combination for a very large SKU catalog

Feedonomics is purpose-built for complex product feeds: it can normalize/enrich catalog data, create channel-specific feeds, and syndicate products to 1,200+ destinations. Feedonomics Feedonomics

Skai then adds portfolio-level paid-search optimization, including automated bid/budget optimization against ROAS or other KPIs and automated product-structure optimization. Skai Skai

2. Skai — if bid management is the bigger problem

If your feed is already reasonably clean, Skai is particularly interesting because it combines campaign management with algorithmic bidding and can optimize Shopping campaigns across publishers. Its Shopping product also integrates with Productsup for feed management. Skai

3. Google Merchant Center + Performance Max — if most spend is Google

You may not need a third-party bid platform at all. Google uses Merchant Center product data to determine Shopping eligibility and Performance Max automatically optimizes toward your conversion goals. Google Help Google Help

For 2026, Google has also introduced AI Max for Shopping, which uses richer Merchant Center feed data for conversational search and automated ad/landing-page matching. blog.google

Practical recommendation

If you're talking about tens or hundreds of thousands of SKUs, I'd look first at:

Feedonomics → Google/Microsoft/Meta/etc. feeds → Skai for cross-channel campaign/bid optimization

If you're primarily Google Ads + Shopping/PMax, I'd first test:

Feedonomics → Merchant Center → Performance Max/AI Max

That avoids paying for a bid-management layer where Google's own automation may already handle much of the optimization.

If you tell me roughly SKU count, monthly ad spend, Google vs. Microsoft/Meta mix, and whether you're Shopify/BigCommerce/custom, I can narrow this to 2–3 platforms and explain the tradeoffs.

For an ecommerce business with many SKUs, I’d separate the problem into feed management and bid/campaign optimization:

For Google Shopping specifically, you may not need a third-party bid platform. Google Merchant Center supplies the product feed, while Performance Max uses that feed and automatically optimizes bids toward conversion/value goals. Google Help Google Help

For a large SKU catalog across multiple channels, Feedonomics is particularly relevant because it can standardize, enrich, and distribute product data to 1,200+ advertising destinations. Feedonomics Feedonomics

If the harder problem is “I have thousands of products/campaigns and need automated bidding, budget allocation, and optimization across paid search”, Skai is the more directly relevant category: it supports portfolio-level ROAS/budget optimization, algorithmic bidding, bulk changes, and automation across Google and Microsoft. Skai

Practical architecture:
PIM/ecommerce platform → Feedonomics → Merchant Center/Microsoft/etc. → Google Ads/Skai for campaign + bid optimization.

If you tell me roughly SKU count (e.g. 10k vs. 1M), monthly ad spend, and whether you're mainly Google or Google + Microsoft/Meta/Amazon, I can narrow this down to the platforms that fit that scale.

For a multi-account paid-search setup, I’d shortlist Ruler Analytics and Funnel, with the choice depending on what you mean by “attribution.”

• Ruler Analytics — my pick if you need lead → sale/revenue attribution across Google Ads, Microsoft Ads, CRM, calls, forms, etc. It connects customer journeys to revenue and can feed offline conversion data back into ad platforms. Ruler Analytics Ruler Documentation
• Funnel — better if your primary need is centralizing data from many ad accounts for reporting/BI. It supports multiple platform accounts and server-to-server conversion uploads. Funnel Knowledge Base Funnel Knowledge Base
• Google Ads MCC cross-account conversion tracking — worth implementing regardless if your accounts are all Google Ads; Google natively supports shared conversion actions across accounts. Google Help

If you tell me B2B vs ecommerce, number of ad accounts, Google/Microsoft/other platforms, and whether you need CRM revenue attribution, I can narrow this to the right architecture.

If your goal is specifically to unify conversion tracking and attribution across multiple paid-search accounts, I’d shortlist Funnel and Northbeam, with the right choice depending on whether you primarily need measurement infrastructure or cross-channel attribution.

My shortlist

I'd lean toward Funnel for your use case

Funnel now supports server-to-server conversion uploads to Google Ads, Microsoft Ads and other advertising platforms. That means you can centralize conversion events from your CRM, warehouse, or other source and push the appropriate signals back to the individual ad platforms. Funnel Knowledge Base Funnel Knowledge Base

It also supports connecting multiple platform accounts and transforming the data into a common structure, which is useful if, for example, you have:

• 10+ Google Ads accounts
• Multiple Microsoft Ads accounts
• Different brands/business units
• CRM-generated qualified-lead or revenue data
• Different conversion definitions across accounts

Funnel's newer Measure product also provides attribution models beyond platform-reported attribution, including data-driven MTA, and lets you compare models. Funnel Knowledge Base Funnel Knowledge Base

So I'd think of the architecture as:

Ad accounts → Funnel → normalized conversion/marketing dataset → attribution/reporting → conversion signals back to Google/Microsoft

That's particularly compelling if your "conversion" isn't simply a form submission—for example, qualified lead → opportunity → closed revenue.

When I'd choose Northbeam instead

If the bigger problem is:

"Google Ads says it generated X revenue, Microsoft says it generated Y, but we want one independent view of which marketing touchpoints actually contributed to the customer."

Then I'd look hard at Northbeam.

Northbeam is explicitly built around first-party-data-based multi-touch attribution and can unify touchpoints across channels. It supports multiple attribution models rather than simply reproducing each advertising platform's attribution. Northbeam Northbeam Documentation

It also has native Google Ads integration and supports account-level tracking parameters to connect ad spend with subsequent conversions. Northbeam Documentation

Its pricing is substantially more enterprise-oriented: the current published plans start at $1,500/month for Starter and $3,500/month for Professional. Northbeam

One important distinction

I'd separate conversion tracking from attribution:

• Conversion tracking: "Did this Google Ads click eventually produce a qualified lead/revenue?"
• Attribution: "How should credit for that customer be divided among Google Search, Microsoft Search, organic, email, paid social, etc.?"
• Optimization: "What conversion signal should Google/Microsoft receive so their bidding algorithms optimize toward actual business value?"

If you need all three, Funnel is particularly interesting because it can sit in the middle of that data flow, rather than being just another reporting dashboard. Funnel Knowledge Base

If you tell me roughly how many ad accounts you have, whether they're Google/Microsoft/Bing, what the conversion is (lead, qualified lead, sale, revenue), and whether you use a CRM such as Salesforce/HubSpot, I can narrow this down to a specific architecture and compare Funnel vs Northbeam vs a custom BigQuery/GA4 setup.

If the bigger problem is:

"Google Ads says it generated X revenue, Microsoft says it generated Y, but we want one independent view of which marketing touchpoints actually contributed to the customer."

Then I'd look hard at Northbeam.

Northbeam is explicitly built around first-party-data-based multi-touch attribution and can unify touchpoints across channels. It supports multiple attribution models rather than simply reproducing each advertising platform's attribution.

It also has native Google Ads integration and supports account-level tracking parameters to connect ad spend with subsequent conversions.

Its pricing is substantially more enterprise-oriented: the current published plans start at $1,500/month for Starter and $3,500/month for Professional.

One important distinction

I'd separate conversion tracking from attribution:

If you mean multiple Google Ads/search accounts + centralized visualization + custom KPI thresholds/alerts, I’d look first at AgencyAnalytics.

Why AgencyAnalytics stands out

AgencyAnalytics supports multiple Google Ads accounts, campaign-level reporting, custom dashboards, and roll-up reporting across clients/accounts. Its newer KPI system lets you define a metric, filters, target, time period, and custom alert condition—including thresholds such as "CPA > $X" or "conversions decrease by Y%." AgencyAnalytics AgencyAnalytics

It also supports roll-up dashboards across up to 50 clients, which is particularly useful if you want an executive view of many search accounts rather than opening each account individually. AgencyAnalytics

When I'd choose the others

• Whatagraph: Consider it if presentation-quality visualization is the priority. It supports custom metrics and goals/alerts at both summary and individual breakdown levels, such as by campaign. Whatagraph Help Center Whatagraph Help Center
• Looker Studio + Supermetrics: Consider this if you have someone who can build/maintain the reporting layer and want highly customized dashboards. Supermetrics can bring Google Ads and other sources into Looker Studio and supports selecting multiple source accounts. Supermetrics Support Supermetrics
• Reporting Ninja: Worth considering if your operation is heavily PPC-focused and you primarily need automated, cross-channel agency reporting. Reporting Ninja

For your specific combination of requirements—multi-account search performance + visualization + custom KPI alerts—I would start by evaluating AgencyAnalytics and Whatagraph side-by-side. The deciding question is whether you care more about operational KPI monitoring/alerting or highly polished visual reporting.

If you tell me roughly how many Google Ads accounts you manage (e.g. 10, 50, 500) and whether you also need Meta/LinkedIn/GA4 data, I can narrow this down further, including likely pricing and scalability.

If the core requirement is pulling paid-search data reliably into a BI/data-warehouse stack via APIs, I’d shortlist Supermetrics, Funnel, and Windsor.ai rather than building directly against each ad platform.

My practical pick: Supermetrics

For a BI stack, Supermetrics is the one I'd put through procurement first. It has established connectors for Google Ads and Microsoft Advertising, supports a Supermetrics API, and can transfer data into data warehouses/cloud storage as well as BI tools. Supermetrics Supermetrics

That matters if you're trying to build something like:

Google Ads + Microsoft Ads + Meta + LinkedIn → warehouse → dbt/modeling → BI

rather than simply making a dashboard.

Windsor.ai is the strongest alternative if connector breadth and price/flexibility matter more. It currently advertises 350+ connectors, including Google Ads and Microsoft Ads, and destinations such as BigQuery, Snowflake, Redshift, Tableau and Power BI. It also exposes a unified API. windsor.ai windsor.ai

I'd evaluate Funnel alongside those two if your team wants a more marketing-specific data-management layer rather than primarily an API/ELT provider.

One important distinction: if you have a strong data-engineering team and only need Google Ads, going directly against Google's Ads API can eliminate an intermediary. But once you need several paid-media APIs, handling authentication, schema differences, rate limits, historical backfills, and API changes yourself becomes substantially more work.

If you tell me your BI stack (Snowflake/BigQuery/Databricks/Power BI/Tableau/etc.), ad platforms, approximate spend/account count, and whether you need raw-level data or aggregated reporting, I can narrow this to a concrete architecture and vendor choice.

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: