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Metabase vs Looker: what each is built for, and when neither fits

Metabase vs Looker compared: what each is built for, what it needs before the first answer, and a file-first option when neither fits.

The short answerMetabase is an open-source BI tool that teams can self-host or run in the cloud, with a visual query builder; Looker is Google Cloud's enterprise BI platform built on a LookML model. Metabase suits start-ups and small teams that want to get going with a free, self-hosted or low-cost cloud BI tool; Looker suits organisations that want one governed set of metric definitions shared across teams and tools. When the need is one desk's question answered from the exports it already has, a file-first tool may fit better than either.

Metabase and Looker are both good at what they are built for. Metabase is an open-source BI tool that teams can self-host or run in the cloud, with a visual query builder; Looker is Google Cloud's enterprise BI platform built on a LookML model. This guide sets them side by side, fairly, and ends with the case where a team needs neither.

At a glance

Metabase Looker Covirage
Built for Start-ups and small teams that want to get going with a free, self-hosted or low-cost cloud BI tool; Companies with data already in a SQL database or warehouse who want non-technical colleagues to explore it Organisations that want one governed set of metric definitions shared across teams and tools; Companies with a SQL data warehouse, particularly BigQuery users already on Google Cloud The person waiting for the answer, in a big or medium company
How it gets data Metabase connects directly to databases and sends queries to them, with officially maintained drivers for PostgreSQL, MySQL, MariaDB, BigQuery, Snowflake, Redshift, MongoDB, Oracle, SQL Server, Athena, Databricks, Druid, SQLite, Presto, Vertica and others, plus community drivers. CSV files can be uploaded, and optional paid storage is available for uploaded spreadsheets. Live query: Looker generates SQL from the LookML project and submits it to the customer's database connection (BigQuery, Snowflake, Redshift, PostgreSQL, SQL Server, Oracle and many other dialects). SQL Runner also allows direct SQL against those connections. Files and sheets now; scheduled delivery by API, S3, Azure, SFTP or email on Business; connections built with your team on Enterprise
Needs before the first answer A supported database or warehouse holding the data (or CSV uploads for small datasets); Hosting: Metabase Cloud, or a server the team runs and upgrades itself for self-hosting; Saved models and metrics that give Metabot and users a curated starting point A SQL database or warehouse holding the data; LookML developers to model tables, joins and measures before business users explore; Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting The exports your systems already produce. No warehouse, no modelling language, no data team
AI Metabot can create a chart in the query builder from a natural-language request, generate SQL in the native editor, fix SQL errors, analyse existing visualisations and generate code transforms. It draws on tables, saved questions, metrics, models and dashboards in the Metabase instance, searching a limited set of content unless told where to look. Users are advised to check AI-generated results. Most customers supply their own AI provider key; Metabase also offers a metered AI service. Conversational Analytics uses Gemini for Google Cloud to interpret a natural-language question, map it to fields in the LookML model, and compose a Looker query through the model's Explores rather than writing free-form SQL. It then analyses the query results to answer the question and can create visualisations. Administrators must enable the Gemini in Looker setting. The AI model chooses a measure and writes the explanation; our tools do the arithmetic and check every total
Published pricing Open Source: free, self-hosted. Starter: $100 a month including 5 users, then $6 per user a month. Pro: $575 a month including 10 users, then $12 per user a month. Enterprise: custom pricing starting at $20,000 a year. Yearly billing saves 10%, and Starter and Pro offer a 14-day free trial. Optional usage-based extras include Metabase's AI service at $3.75 per million tokens (1 million included) and storage for uploaded data from $2 per million rows. Platform editions (Standard, Enterprise and Embed) and user licences (Developer, Standard and Viewer) are sold on annual commitments priced through Google Cloud sales; no list prices are published. Conversational Analytics is metered in data tokens, with monthly allowances included per platform tier (for example 60M input and 1.2M output tokens on Standard); overage is listed at $3.00 per 1M input and $20.00 per 1M output tokens, though Google states billing is not yet enforced during a promotional period. Data Studio (formerly Looker Studio) is free, with a paid Pro tier offering a 30-day trial. Three tiers on one sheet, shared in a conversation

Metabase in brief

Metabase is a business intelligence and embedded analytics tool with a free open-source edition and paid cloud and self-hosted plans. It connects to a company's databases so people can ask questions with a point-and-click query builder or SQL, then save the results as charts and dashboards. Its AI assistant, Metabot, turns natural-language requests into query-builder charts or SQL.

  • Metabase was open-sourced on 21 October 2015, after testing with companies in the Expa portfolio.
  • Metabase's open-source edition is released under the AGPL, with commercial editions under the Metabase Commercial Software License.
  • Metabase Starter costs $100 a month including 5 users, with additional users at $6 a month each.
  • Metabase Pro costs $575 a month including 10 users, with additional users at $12 a month each.

Looker in brief

Looker is Google Cloud's enterprise business intelligence platform. Data teams describe their database in LookML, Looker's modelling language, and Looker then generates SQL from that model and runs it against the connected database to power Explores, dashboards, scheduled deliveries and embedded analytics. It is separate from Google's free report builder, formerly Looker Studio and now called Data Studio.

  • Looker was founded in 2012 in Santa Cruz, California by Lloyd Tabb and Ben Porterfield.
  • Google announced its $2.6 billion acquisition of Looker on 6 June 2019 and completed it in February 2020.
  • Looker's documentation describes it as a tool that generates SQL queries from a LookML project and submits them against a database connection.
  • Looker platform editions are sold on annual commitments through Google Cloud sales, with no published list price.

Where Metabase fits best

  • Start-ups and small teams that want to get going with a free, self-hosted or low-cost cloud BI tool.
  • Companies with data already in a SQL database or warehouse who want non-technical colleagues to explore it.
  • Product teams adding multi-tenant embedded analytics to their own application.

Where Looker fits best

  • Organisations that want one governed set of metric definitions shared across teams and tools.
  • Companies with a SQL data warehouse, particularly BigQuery users already on Google Cloud.
  • Product teams embedding governed analytics into customer-facing applications.

Where they differ

Data

Metabase. Metabase connects directly to databases and sends queries to them, with officially maintained drivers for PostgreSQL, MySQL, MariaDB, BigQuery, Snowflake, Redshift, MongoDB, Oracle, SQL Server, Athena, Databricks, Druid, SQLite, Presto, Vertica and others, plus community drivers. CSV files can be uploaded, and optional paid storage is available for uploaded spreadsheets.

Looker. Live query: Looker generates SQL from the LookML project and submits it to the customer's database connection (BigQuery, Snowflake, Redshift, PostgreSQL, SQL Server, Oracle and many other dialects). SQL Runner also allows direct SQL against those connections.

AI

Metabase. Metabot can create a chart in the query builder from a natural-language request, generate SQL in the native editor, fix SQL errors, analyse existing visualisations and generate code transforms. It draws on tables, saved questions, metrics, models and dashboards in the Metabase instance, searching a limited set of content unless told where to look. Users are advised to check AI-generated results. Most customers supply their own AI provider key; Metabase also offers a metered AI service.

Looker. Conversational Analytics uses Gemini for Google Cloud to interpret a natural-language question, map it to fields in the LookML model, and compose a Looker query through the model's Explores rather than writing free-form SQL. It then analyses the query results to answer the question and can create visualisations. Administrators must enable the Gemini in Looker setting.

What each needs before the first answer

Metabase:

  • A supported database or warehouse holding the data (or CSV uploads for small datasets).
  • Hosting: Metabase Cloud, or a server the team runs and upgrades itself for self-hosting.
  • Saved models and metrics that give Metabot and users a curated starting point.

Looker:

  • A SQL database or warehouse holding the data.
  • LookML developers to model tables, joins and measures before business users explore.
  • Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting.

When neither fits

Both assume a team that will build and maintain a model of the data before questions get answered. Some teams have a narrower need: one desk, one recurring question such as "what is driving my costs" or "where is my revenue coming from", and exports that already exist. For them, Covirage reads those files, answers the question with a bridge that sums to the change, and is set up for them within a week. The AI model chooses the measure and explains it; our tools do the arithmetic.

See the full pages on Metabase alternatives and Looker alternatives, or how to evaluate an analytics vendor.

Sources

Facts about other products were checked on 24 September 2026 from the pages below. Product names are trademarks of their owners; Covirage is not affiliated with them.

Questions people ask

What is the main difference between Metabase and Looker?

Metabase is an open-source BI tool that teams can self-host or run in the cloud, with a visual query builder; Looker is Google Cloud's enterprise BI platform built on a LookML model.

Which is easier to start with, Metabase or Looker?

It depends on what is in place. For Metabase: A supported database or warehouse holding the data (or CSV uploads for small datasets); Hosting: Metabase Cloud, or a server the team runs and upgrades itself for self-hosting; Saved models and metrics that give Metabot and users a curated starting point. For Looker: A SQL database or warehouse holding the data; LookML developers to model tables, joins and measures before business users explore; Looker administrators to manage connections, users and, for AI features, the Gemini in Looker setting. Check both against your team and your data before comparing licence prices.

Is there an alternative to both?

For a desk that needs to explain why its numbers moved, from exports it already produces, Covirage answers from the file and is set up for you within a week. It does not replace a platform for the whole organisation.