# Kater vs Looker

> Looker put the model in git. Kater puts everything in git, in SQL.

LookML gave data teams one governed model, and that idea was right. But LookML is a language of its own, and the dashboards people use live in Looker’s database. In Kater, the model is SQL, and every query, dashboard and app is a file your agent can change.

URL: https://kater.ai/kater-vs-looker/
Reviewed October 2026.

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- [Ask for a free migration](https://kater.ai/request-demo/): We do the migration for free. Tell us what you want to move. We do the migration with you, on your warehouse, and we do not charge for it. Then compare the numbers and decide.

## The short version

### Select Kater if

- You like Looker’s governed model, but not LookML or its price.
- You want dashboards and apps in git, not only the model.
- Your team builds with Claude Code, Codex or Cursor.
- Your warehouse is Snowflake or DuckDB.

### Stay with Looker if

- You run on BigQuery or another warehouse that Kater does not connect to yet.
- You embed Looker in your own product.
- You are committed to Google Cloud, BigQuery and Gemini.
- Your LookML project is large, stable and well staffed.

## Why teams leave Looker

### 1. LookML is a second language.

Every new field or join waits for a LookML developer. Business users find Looker hard to use, so they queue behind the few people who know LookML.

> “Someone with knowledge of Looker can understand it, but a person without any technical background finds it somewhat advanced or difficult to comprehend.” ([Data engineer, PeerSpot review, April 2026](https://www.peerspot.com/products/looker-reviews))

**In Kater:** The model is Kater SQL: your warehouse’s own SQL with a few additions. Your agent already writes it, and anyone who reads SQL can review it.

### 2. Only the model is in git.

LookML lives in git. The dashboards and Looks that people build in the browser live in Looker’s database. An agent cannot search them, and nobody can review a change to them as a diff.

> “UDD not part of git tracking is a big inconvenience for multi-instance deployments.” ([Looker developer, Google Cloud community, 2023](https://discuss.google.dev/t/why-user-defined-dashboard-in-not-part-of-git/122932))

**In Kater:** Every layer is a file: models, queries, charts, the dashboard and the apps. Every save is a commit, with History and Undo.

### 3. The price keeps going up.

Looker sells a platform edition plus named users, on annual terms, by quote only. Teams report large increases at renewal.

> “When we started, it was affordable, but now they have revised the prices. It is expensive now.” ([Looker customer, PeerSpot review, 2023](https://www.peerspot.com/products/looker-reviews))

**In Kater:** A scoped monthly subscription. Kater does not charge per viewer or per dashboard.

### 4. The core product moves slowly.

Users say product work and support slowed after Google bought Looker in 2020. Google now ships AI features quickly, but many are still in preview.

> “It is insane to be buying Looker in 2025. Hasn’t gotten a product update in three years.” ([r/BusinessIntelligence, June 2025](https://www.reddit.com/r/BusinessIntelligence/comments/1lcyr5u/advice_on_selfserve_bi_tools/myx6p0n/))

**In Kater:** Kater ships every week. The changelog lists each release.

## Kater vs Looker at a glance

### Building with agents

| Feature | Kater | Looker |
| --- | --- | --- |
| Source of truth | Yes. Files in git: models, queries, charts, dashboards and apps. Every save is a commit. | In part. LookML is in git. Dashboards and Looks built in the browser live in Looker’s database. |
| Coding agents | Yes. Claude Code, Codex and Cursor edit the files through the Kater MCP, or in a local copy. | In part. The VS Code extension and Looker Skills support coding agents for LookML. Query and dashboard skills are planned. |
| Checks on changes | Yes. A write through the MCP compiles first and is refused on error. Every change has a diff, History and Undo. | Yes. The LookML validator, and CI on pull requests. |
| Answers in your own AI | Yes. People ask in Slack, Claude or ChatGPT. The agent sees only what that person can see. | Yes. Conversational Analytics in Looker and Gemini Enterprise. The managed MCP server is in preview. |

### Metrics and trust

| Feature | Kater | Looker |
| --- | --- | --- |
| Semantic layer | Yes. Kater SQL: your warehouse’s SQL with DIMENSION, MEASURE and EVAL. Defined once, read everywhere. | Yes. LookML, a modeling language of its own, with SQL inside. |
| Tests on numbers | Yes. Tests check a query’s rows, or compare a measure with SQL you trust. They run after each sync. | Yes. LookML data tests, run in the IDE or in CI. |

### Dashboards and apps

| Feature | Kater | Looker |
| --- | --- | --- |
| Build without code | Yes. Select fields, a chart and a place on the dashboard, all in the browser. | Yes. Business users build queries and dashboards in Explores. |
| Custom apps | Yes. HTML apps, reports and decks on live queries, built by your agent. Export to PDF, PowerPoint, Word or Excel. | In part. Extensions and embedded apps, built by developers. |
| Paginated reports | Yes. Report templates for PDF, PowerPoint and Word, on live queries. | Yes. Paginated reports are generally available. |

### Platform

| Feature | Kater | Looker |
| --- | --- | --- |
| Warehouses | In part. Snowflake and DuckDB today. | Yes. BigQuery, Snowflake, Redshift, Databricks, Postgres and many more. |
| Embedding | No. No embedding (for now). It is in development. | Yes. A mature embedding SDK. |
| Row-level access | Yes. Row-level security, access filters and grants, with access by folder and file. | Yes. Access filters and user attributes. |

## Where Looker is ahead, and why teams still select Kater

### More warehouses

**Looker:** Looker connects to BigQuery, Redshift, Databricks, Postgres and dozens more. Kater connects to Snowflake and DuckDB today.

**Why teams still select Kater:** Kater goes deep on each warehouse it supports. Queries run live, and the compiler checks each SQL dialect before a query runs. Have a warehouse we do not support? Request it. [Request a warehouse](https://kater.ai/request-integration/)

### Embedding

**Looker:** Looker’s embedding SDK is mature and widely used.

**Why teams still select Kater:** Kater has no embedding (for now). It is in development. Until it ships, apps export to PDF, PowerPoint, Word and Excel for readers outside Kater. [Ask about early access](https://kater.ai/request-demo/?intent=embedding-early-access)

### The size of Google

**Looker:** Looker comes with Google’s scale, a long track record and a large partner network. A vendor that size also sets one roadmap for thousands of customers.

**Why teams still select Kater:** Kater is ready for enterprise: SOC 2 Type II, row-level security, access filters, grants and SSO. And you work with the people who build it. You decide how you pay, and you help decide what agentic BI becomes. [Read about security](https://kater.ai/security/)

## How pricing compares

**Looker:** Looker sells a platform edition (Standard, Enterprise or Embed) plus Developer, Standard and Viewer users, on annual terms. Google lists no prices. The Standard edition lists at $60,000 a year on AWS Marketplace, before users. Conversational Analytics will be metered in data tokens after its promotional period.

**Kater:** A 30-day free trial with every feature, then a subscription quoted for your team. Kater does not charge per viewer or per dashboard. Kater runs no chat of its own, so answers run in the agent your team already uses.

## Moving from Looker

LookML and Kater SQL describe the same things, so most of a Looker project maps one to one. Your agent reads your LookML files and writes the Kater SQL. You review each diff, and tests compare the numbers.

| In Looker | In Kater |
| --- | --- |
| Connection | Connection, on Snowflake or DuckDB |
| View, with dimensions and measures | Kater dataset, with DIMENSION and MEASURE in SQL |
| Derived table | Custom SQL dataset |
| Explore and its joins | Semantic model, with RELATIONSHIP joins |
| Percent of total, or a measure in another context | A measure with EVAL and CLEAR, REMOVE or SET |
| Look or user-defined dashboard | Query and chart files, and a dashboard.yaml |
| Data test | Test, with expected rows or a same_as check |
| Access filter and user attribute | Access filter and grant, in Kater’s permissions |

Ask your agent to migrate one dashboard. Kater rebuilds it as a private draft on your warehouse, and you compare the numbers before you publish. [How a switch works](https://kater.ai/switch-to-kater/#how)

## Questions

### Is Kater a Looker alternative?

Yes. Kater gives you a governed semantic layer, dashboards, apps and AI answers on one model. The model is written in SQL, and every layer is a file in git.

### Can our agent convert LookML to Kater SQL?

Yes. Give your agent the LookML files. It writes Kater datasets and semantic models, and Kater compiles each file as it saves it. A same_as test then compares each measure with SQL you trust.

### Does Kater work with BigQuery?

Not today. Kater connects to Snowflake and DuckDB. If you run on BigQuery, tell us.

### Do we lose Looker’s governance?

No. The data team owns the shared layer. A field enters it only through a promotion that the data team approves, and every change has a diff and an undo.

### Can we keep Looker and add Kater?

Yes, for as long as you want. Both read the same warehouse. Keep Looker for its dashboards, and add Kater to give your team’s agent one definition for each metric, your context and your access rules. When you want, rebuild one dashboard in Kater, compare the numbers with Looker, then move the next one.

## Compare another tool

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- [All comparisons](https://kater.ai/switch-to-kater.md)