Sign in

Blog · Alternatives and comparisons

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

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

The short answerLooker models the database in LookML and queries it live; Power BI builds semantic models, authored mainly in a desktop application for Windows and published to the Power BI service. Looker suits organisations that want one governed set of metric definitions shared across teams and tools; Power BI suits organisations already using Microsoft 365, Teams and Azure that want reporting inside the same estate. When the need is one desk's question answered from the exports it already has, a file-first tool may fit better than either.

Looker and Power BI are both good at what they are built for. Looker models the database in LookML and queries it live; Power BI builds semantic models, authored mainly in a desktop application for Windows and published to the Power BI service. This guide sets them side by side, fairly, and ends with the case where a team needs neither.

At a glance

Looker Power BI Covirage
Built for 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 Organisations already using Microsoft 365, Teams and Azure that want reporting inside the same estate; Teams with analysts who can build and maintain a semantic model for others to report from The person waiting for the answer, in a big or medium company
How it gets data 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. Connects to online services, databases (including SQL Server, PostgreSQL, Snowflake and Redshift), files and big-data engines. Microsoft recommends importing data into the semantic model's in-memory cache by default; DirectQuery leaves data in the source and queries it per visual; live connections reuse an existing published semantic model or Analysis Services model; Direct Lake reads Fabric lakehouse tables. Scheduled refresh of on-premises sources may require an on-premises data gateway. 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 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 A semantic model (tables, relationships and DAX measures) built by someone comfortable with Power Query and DAX; Power BI Desktop on Windows for most report authoring; An on-premises data gateway where data sits behind the firewall The exports your systems already produce. No warehouse, no modelling language, no data team
AI 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. Copilot in Power BI offers chat over a report (the report Copilot pane, generally available), a standalone cross-item Copilot and app-scoped Copilot (both in preview). For business users it summarises reports and answers questions, using the semantic model when the question relates to its data. For authors it can create and edit report pages, add narrative summary visuals, write DAX queries and suggest descriptions for measures; Copilot in web modelling (preview) reviews and edits semantic models. Microsoft advises preparing the semantic model for AI first, as unprepared models can lead to generic or inaccurate answers. The AI model chooses a measure and writes the explanation; our tools do the arithmetic and check every total
Published pricing 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. Free account for personal report creation (sharing requires a paid licence). Power BI Pro is $14.00 per user per month and Premium Per User is $24.00 per user per month, both paid yearly. Power BI Embedded and Fabric capacity (reserved or pay-as-you-go) are variably priced. Copilot requires paid Fabric capacity (F2 or higher) or Premium (P1 or higher); a Pro or PPU licence alone is not enough. Three tiers on one sheet, shared in a conversation

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.

Power BI in brief

Power BI is Microsoft's business intelligence product for building semantic models, reports and dashboards. Authors typically design reports in the Power BI Desktop application for Windows and publish them to the Power BI service, where colleagues view and share them in the browser, on mobile or in Microsoft Teams. It is also part of Microsoft Fabric, which provides the capacity that its Copilot features run on.

  • Power BI was first released to the general public on 24 July 2015.
  • Power BI Pro is listed at $14.00 per user per month and Premium Per User at $24.00 per user per month, both paid yearly.
  • Power BI Pro is included with Microsoft 365 E5 and Office 365 E5.
  • Copilot in Power BI requires paid Fabric capacity at F2 or higher or Power BI Premium at P1 or higher; trial capacities and free SKUs are not supported.

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 Power BI fits best

  • Organisations already using Microsoft 365, Teams and Azure that want reporting inside the same estate.
  • Teams with analysts who can build and maintain a semantic model for others to report from.
  • Companies wanting a low per-user entry price for publishing and sharing reports widely.

Where they differ

Data

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.

Power BI. Connects to online services, databases (including SQL Server, PostgreSQL, Snowflake and Redshift), files and big-data engines. Microsoft recommends importing data into the semantic model's in-memory cache by default; DirectQuery leaves data in the source and queries it per visual; live connections reuse an existing published semantic model or Analysis Services model; Direct Lake reads Fabric lakehouse tables. Scheduled refresh of on-premises sources may require an on-premises data gateway.

AI

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.

Power BI. Copilot in Power BI offers chat over a report (the report Copilot pane, generally available), a standalone cross-item Copilot and app-scoped Copilot (both in preview). For business users it summarises reports and answers questions, using the semantic model when the question relates to its data. For authors it can create and edit report pages, add narrative summary visuals, write DAX queries and suggest descriptions for measures; Copilot in web modelling (preview) reviews and edits semantic models. Microsoft advises preparing the semantic model for AI first, as unprepared models can lead to generic or inaccurate answers.

What each needs before the first answer

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.

Power BI:

  • A semantic model (tables, relationships and DAX measures) built by someone comfortable with Power Query and DAX.
  • Power BI Desktop on Windows for most report authoring.
  • An on-premises data gateway where data sits behind the firewall.

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 Looker alternatives and Power BI 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 Looker and Power BI?

Looker models the database in LookML and queries it live; Power BI builds semantic models, authored mainly in a desktop application for Windows and published to the Power BI service.

Which is easier to start with, Looker or Power BI?

It depends on what is in place. 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. For Power BI: A semantic model (tables, relationships and DAX measures) built by someone comfortable with Power Query and DAX; Power BI Desktop on Windows for most report authoring; An on-premises data gateway where data sits behind the firewall. 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.