Kater vs Lightdash
Lightdash is the closest tool to Kater. Both keep the model as code, and both let agents work in it. The difference is where things live. Lightdash metrics are YAML, and its charts and dashboards live in the app. In Kater, every layer is a SQL or text file in git.
Reviewed October 2026.
Select Kater if
Stay with Lightdash if
Lightdash metrics are YAML with SQL snippets, in your dbt project or in Lightdash’s own files. A metric cannot use a window function, and a level-of-detail metric must be built in the dbt model first.
In Kater Kater SQL is your warehouse’s SQL with DIMENSION, MEASURE and EVAL. Your agent writes it, and anyone who reads SQL can review it.
Lightdash is built for analysts who know the dbt project. People outside the data team struggle to find their way.
“not super intuitive for BUs”
In Kater Business users read dashboards and apps, or ask their own agent in Slack, Claude or ChatGPT.
Charts and dashboards can be downloaded and uploaded as YAML. Lightdash recommends the app as their source of truth, and an upload can overwrite edits made in the app.
“each chart can have up to 400-500 rows of yml... so even if it’s structured data, a single dashboard will eat up your context quickly”
In Kater In Kater the files are the source of truth. The portal reads them, and every save is a commit.
| Feature | Kater | Lightdash |
|---|---|---|
| Building with agents | ||
| Source of truth | Yes. Files in git: models, queries, charts, dashboards and apps. Every save is a commit. | In part. The semantic layer is YAML in git. For charts and dashboards, Lightdash recommends the app as the source of truth, with YAML download and upload. |
| Coding agents | Yes. Claude Code, Codex and Cursor edit the files through the Kater MCP, or in a local copy. | Yes. An MCP server that edits charts and dashboards, and agents that edit YAML and open pull requests. |
| Checks on changes | Yes. A write through the MCP compiles first and is refused on error. Every change has a diff, History and Undo. | In part. A validator and preview projects. The validator does not catch SQL errors, and its CI check does not block a merge by default. |
| Answers in your own AI | Yes. People ask in Slack, Claude or ChatGPT. The agent sees only what that person can see. | Yes. AI agents in the app and in Slack, and an MCP server. In Slack, an agent uses its creator’s access unless an admin requires sign-in. |
| Metrics and trust | ||
| Semantic layer | Yes. Kater SQL: your warehouse’s SQL with DIMENSION, MEASURE and EVAL. Defined once, read everywhere. | Yes. YAML with SQL snippets, in dbt or in Lightdash’s own files. |
| Built on any SQL | Yes. Any query in your warehouse’s SQL, with its CTEs and window functions. Paste the SQL dbt compiled, and wrap its columns. | In part. A metric is YAML with a SQL snippet, and cannot use a window function. Full queries live in the SQL Runner. |
| Measures at any grain | Yes. A measure runs again at each query’s grouping and filters. EVAL asks for another slice: the grand total, all regions, or the same month last year. | In part. Level-of-detail metrics are not supported. The docs say to compute them in the dbt model. |
| Period comparisons | Yes. Compare with writes last month or the same month last year. When Kater proves the numbers stay the same, it can become a reusable measure. | In part. In beta, on one chart at a time. A comparison cannot be saved to YAML as a metric. |
| Joins that fan out | Yes. Relationships keep measures from double counting through a join. Kater checks each join on your warehouse. | In part. Primary keys on joins prevent double counting. The docs list cases it does not cover, and recommend wide tables built in dbt. |
| Reuse a query’s SQL | Yes. A measure written in one query moves into the shared model. Kater runs both and checks that every number stays the same. | In part. A SQL Runner query becomes a virtual view outside your dbt project, or a pull request to dbt that you merge and run. |
| Tests on numbers | Yes. Tests check a query’s rows, or compare a measure with SQL you trust. They run after each sync. | In part. dbt tests check the models. The validator checks field references, not numbers. |
| Lineage | Yes. Lineage traces each field through every CTE to the warehouse columns it reads. | In part. Authors can list a metric’s drivers by hand, in YAML. |
| Dashboards and apps | ||
| Build without code | Yes. Select fields, a chart and a place on the dashboard, all in the browser. | Yes. Explore tables and build charts by clicking. |
| Apps and reports | Yes. HTML apps, reports and decks on live queries, built by your agent. Export to PDF, PowerPoint, Word or Excel. | In part. Data apps, in beta: React apps that AI writes. The pricing page lists them as an add-on. |
| Version history | Yes. Every save is a commit. History keeps every version, and Undo reverts any change. | In part. Chart and dashboard versions, kept 30 days on Cloud Pro and 60 on Enterprise. A dashboard restores whole. |
| Platform | ||
| Row-level security | Yes. Row-level security, access filters and grants, with access by folder and file. | In part. User attributes filter rows and hide fields. They do not apply in the SQL Runner, agent SQL mode or custom SQL fields. |
| Warehouses | In part. Snowflake and DuckDB today. | Yes. Snowflake, BigQuery, Databricks, Redshift, Postgres, DuckDB, ClickHouse, Trino and Athena. |
| Open source | No. Not open source. Your content is files you can clone and keep. | Yes. The core is open source under MIT. To host AI agents, the MCP or data apps yourself, you need an Enterprise license. |
Lightdash does some things better today. Here is each one, and why teams still select Kater.
You can host Lightdash’s open-source core yourself at no cost.
Why teams still select Kater
Kater runs the platform for you, so there is nothing to host or upgrade. Your content is still files in git that you can clone and keep.
If your metrics already live in dbt YAML, Lightdash reads them as they are.
Why teams still select Kater
You do not redefine your dbt metrics in Kater. Paste the SQL dbt compiled for a model, and wrap its columns in DIMENSION and MEASURE. The CTEs keep their dbt names, so lineage traces each measure back to the models it came from. A same_as test checks each one against your dbt mart.
Lightdash Cloud Pro has one monthly price with no charge for each seat.
Why teams still select Kater
Kater does not charge per viewer either. Its subscription is quoted for your rollout, and you decide how you pay.
Lightdash embeds dashboards and charts in your own product, priced per load after the first 1,000.
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 accessLightdash connects to BigQuery, Databricks, Redshift, Postgres, ClickHouse, Trino and 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 warehouseLightdash
Lightdash is free to host yourself. Cloud Pro is $3,000 a month, with no charge for each seat.
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 dbt is easier than moving from a BI tool, because you do not redefine anything. Your dbt models keep running. Paste the SQL dbt compiled for a model into Kater, and wrap its columns in DIMENSION and MEASURE. The CTEs keep their dbt names, so lineage traces every measure back through each model it reads, and your agent sees where each number comes from.
| In Lightdash | In Kater |
|---|---|
| dbt model and its YAML metrics | The SQL dbt compiled, with each column wrapped in DIMENSION or MEASURE |
| Explore and joins | Semantic model, with RELATIONSHIP joins |
| Virtual view or SQL Runner query | Custom SQL dataset, in your warehouse’s SQL |
| Saved chart | Query and chart files |
| Dashboard | dashboard.yaml |
| Data app | HTML app on live queries |
| Space | Folder, with Viewer, Editor and Manager access |
| dbt test | Kater test, including same_as checks against your dbt marts |
-- The SQL dbt compiled for ad_reporting__campaign_report.
WITH __dbt__cte__int_ad_reporting__campaign_report AS (
SELECT ... FROM kater_dataset.facebook_ads__campaign_report
UNION ALL
SELECT ... FROM kater_dataset.google_ads__campaign_report
UNION ALL
SELECT ... FROM kater_dataset.tiktok_ads__campaign_report
), base AS (
SELECT * FROM __dbt__cte__int_ad_reporting__campaign_report
)
SELECT
DIMENSION('platform', base.platform) AS "Platform",
DIMENSION('union__campaign_name', base.campaign_name) AS "Campaign Name",
MEASURE('sum__spend', SUM(spend)) AS "Spend",
MEASURE('sum__conversions', SUM(conversions)) AS "Conversions"
FROM base
GROUP BY 1, 2
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 worksYes. Both keep BI as code. Kater writes metrics in SQL, keeps dashboards and apps in git by default, and does not need dbt.
No. Schema sync writes a file for each warehouse table, and you model on top of it in Kater SQL. If you use dbt, Kater reads the tables it builds.
No. Paste the SQL dbt compiled for a model into Kater, and wrap its columns in DIMENSION and MEASURE. The CTEs keep their dbt names, so lineage traces each measure back to the dbt models it came from.
Yes. A same_as test compares a Kater query with read-only SQL you write against your dbt marts.
Yes, for as long as you want. Both read the same warehouse. Keep Lightdash 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 Lightdash, then move the next one.
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.