Why marketing and data teams are switching from Supermetrics to Windsor.ai

Windsor.ai starts at $19/month billed annually for 3 data sources, 75 accounts and unlimited users. Supermetrics starts at €39/month billed yearly for 1 user, 2 data sources with 2 accounts each and 1 destination, and Growth costs €159/month for 2 users and 7 data sources with 7 accounts each. See the Windsor.ai pricing page and how data sources and accounts are counted.

Yes. Make a copy of the report and, in the copy dialog, replace each Supermetrics data source with the matching Windsor.ai connector, so the charts switch together instead of one by one. Both read the same platform APIs, so metrics match, but field names can differ, so check charts with calculated fields after the switch.

Install the Windsor.ai Sheets add-on and recreate each query by choosing the data source, accounts, fields and schedule. There is no automatic import of Supermetrics queries, so start with the sheets your reports depend on.

Yes. Export your custom fields and ask Claude, connected to Windsor.ai, to recreate them as Windsor.ai custom fields; it checks the field names on each connector and shows you every formula before creating it. See the custom field migration guide.

Omnicom Media Group Hong Kong moved its pipeline for about 1,900 ad accounts on 11 platforms from Supermetrics to Windsor.ai, loading into BigQuery. The team removed about 40 hours a week of manual CSV exports and script maintenance, and data now reaches BigQuery in under two hours. Chris Kwok, Head of Data & Analytics, said: “Windsor.ai turned our tangled web of scripts into a single, dependable pipeline.” Read the Omnicom case study.

Yes, as far back as each platform’s API allows. Windsor.ai reads history directly from the platforms, so a new connection can return past years without importing anything from Supermetrics.

Yes. The blended Windsor.ai connector returns spend, clicks and impressions under the same names for every ad platform, with a data source column, so one table or chart can show Google Ads, Meta Ads, TikTok Ads and LinkedIn Ads together.

Yes. Windsor MCP connects all your sources to Claude, ChatGPT, Copilot, Gemini and other assistants on every plan, and on platforms that support write actions the assistant can make changes after you confirm them.

Yes. Windsor.ai supports 20+ destinations, including Data Studio, Power BI, Snowflake, BigQuery and Google Sheets, all available on every plan without upgrade requirements.

Yes. Every paid Windsor.ai plan includes unlimited users at no extra cost. On Supermetrics, Starter includes 1 user and Growth 2, with more users as an add-on.

Yes. Windsor.ai offers a free forever plan; no credit card required. If you need more sources or accounts, paid plans start at $19/month.

Over 450,000+ marketers and data professionals, from freelance marketers and digital agencies to data teams and enterprises, use Windsor.ai to centralize their marketing, sales, CRM, and e-commerce data for advanced analytics and AI workflows.

Not as a fixed catalog, and that’s deliberate. A predefined schema library only covers the use cases someone already anticipated when building it. If yours doesn’t fit, you’re stuck reshaping a template that wasn’t made for you.

Instead, Windsor’s MCP lets you describe what you need (the platform, the fields, how you’ll query it) in plain language, and it drafts the table structure for you: which columns to include, how to partition it, how to cluster it. You review the design, then apply it in the Windsor dashboard in a couple of clicks.

Connect the same data sources in Windsor, then ask its MCP to help you replicate, or improve on, your existing table design instead of rebuilding it from a template. A few examples:

I'm migrating my Facebook Ads reporting from Supermetrics to Windsor. My old BigQuery table was partitioned by day and clustered by campaign_id. Help me set up an equivalent destination task in Windsor: which fields to pull, and how to set partitioning and clustering to keep query costs down.

Design a BigQuery table structure for my Google Ads spend and conversions data, refreshed daily, that I can filter fast by account and campaign. What fields should I include, and what partitioning and clustering do you recommend?

I have five Supermetrics-fed tables (Facebook, Google, TikTok, LinkedIn, GA4) that I want to consolidate into one blended reporting table in Snowflake. Help me design the schema and pick the fields from each source so they join cleanly on date and account.

Here's the column list from my old Supermetrics BigQuery export: [paste column names]. Help me map these to the closest Windsor fields and recommend a table structure for the same report.