# Kater vs dbt Semantic Layer

> dbt put metrics in YAML. Kater puts them in the SQL you already have.

The dbt Semantic Layer defines metrics for other tools to query. Kater defines them in SQL, and adds the dashboards, apps and answers that read them. You do not have to replace your dbt metrics. When you move one, you do not redefine it. Paste the SQL dbt compiled, and wrap its columns in MEASURE.

URL: https://kater.ai/kater-vs-dbt-semantic-layer/
Reviewed October 2026.

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## The short version

### Select Kater if

- You want metrics written in SQL, not YAML.
- You want to bring your dbt models as they are, without redefining a metric.
- You want dashboards, apps and answers in Slack, Claude or ChatGPT on the same metrics.
- You want tests that check the numbers a metric returns.
- Your warehouse is Snowflake or DuckDB.

### Stay with the dbt Semantic Layer if

- You want your metrics to stay inside your dbt project.
- Your BI tools already query the Semantic Layer through its integrations.
- Your warehouse is BigQuery, Databricks, Redshift, Postgres or Trino.

## Why teams leave the dbt Semantic Layer

### 1. Metrics are YAML.

Metrics are YAML in your dbt project, with SQL in expression strings and Jinja in filters. In January 2026, dbt Labs changed the spec to make metrics easier to define.

> “My issue with DBT is it is a mix of SQL, yaml, jinja2 flow controls (and metrics is whole another thing).” ([Hacker News, September 2026](https://news.ycombinator.com/item?id=49554584))

**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.

### 2. Each query of a metric counts toward a limit.

The APIs and integrations need a paid dbt plan. Each metric in a successful request counts toward a monthly allowance: 5,000 on Starter and 20,000 on Enterprise.

> “It’s out of reach due to cost for many folks” ([Hacker News, October 2024](https://news.ycombinator.com/item?id=41984352))

**In Kater:** Kater does not charge per query, per viewer or per dashboard. Its subscription is quoted for your team.

### 3. No test checks a metric’s numbers.

dbt tests run on models, columns, sources, snapshots and seeds. Validation checks that a metric is well formed, not that its numbers are right.

> “The piece we could not find much documentation on was how to test the metrics in the dbt Semantic layer.” ([dbt Community Forum, July 2025](https://discourse.getdbt.com/t/testing-strategies-for-dbt-semantic-layer/19811))

**In Kater:** A Kater test checks a query’s rows, or compares a measure with SQL you trust, such as your dbt mart. Tests run after each sync.

### 4. dbt has no dashboard that reads them.

The Semantic Layer serves metrics to other tools. To see a chart, you connect a BI tool through an integration. dbt Charts, in public beta since September 2026, runs SQL and does not read the Semantic Layer yet.

**In Kater:** Dashboards and apps in Kater read the same measures. People also ask their own agent in Slack, Claude or ChatGPT.

## Kater vs dbt Semantic Layer at a glance

### Building with agents

| Feature | Kater | dbt Semantic Layer |
| --- | --- | --- |
| Source of truth | Yes. Files in git: models, queries, charts, dashboards and apps. Every save is a commit. | In part. Metrics are YAML in your dbt project. Charts and dashboards live in whichever BI tool reads them. |
| Coding agents | Yes. Claude Code, Codex and Cursor edit the files through the Kater MCP, or in a local copy. | Yes. Agents edit the YAML in your dbt project. The dbt MCP server lists metrics and queries them. |
| 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. dbt validates each semantic model and metric. It checks the definitions, not the numbers. |
| Answers in your own AI | Yes. People ask in Slack, Claude or ChatGPT. The agent sees only what that person can see. | In part. Agents query metrics through the dbt MCP server, with a dbt platform token. dbt Wizard answers in the dbt platform, in preview. |

### Metrics and trust

| Feature | Kater | dbt Semantic Layer |
| --- | --- | --- |
| Semantic layer | Yes. Kater SQL: your warehouse’s SQL with DIMENSION, MEASURE and EVAL. Defined once, read everywhere. | Yes. YAML in your dbt project, with SQL in expression strings: simple, ratio, cumulative, derived and conversion metrics. |
| 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. Each semantic model sits on one dbt model. Other SQL must become a dbt model first. |
| Tests on numbers | Yes. Tests check a query’s rows, or compare a measure with SQL you trust. They run after each sync. | No. dbt tests check the models a metric reads. No test checks the numbers a metric returns. |
| Lineage | Yes. Lineage traces each field through every CTE to the warehouse columns it reads. | In part. Catalog shows lineage between models and metrics. Column-level lineage needs Enterprise. |

### Dashboards and apps

| Feature | Kater | dbt Semantic Layer |
| --- | --- | --- |
| Dashboards | Yes. Dashboards on the same measures, built by your agent or in the browser. | No. None that read the metrics. A BI tool queries them through an integration. |
| Apps and reports | Yes. HTML apps, reports and decks on live queries, built by your agent. Export to PDF, PowerPoint, Word or Excel. | No. None. Exports write a saved query to a table in your warehouse on a schedule. |
| Build without code | Yes. Select fields, a chart and a place on the dashboard, all in the browser. | In part. dbt Insights queries metrics in the browser. It is in beta, on Enterprise plans. |

### Platform

| Feature | Kater | dbt Semantic Layer |
| --- | --- | --- |
| Warehouses | In part. Snowflake and DuckDB today. | Yes. Snowflake, BigQuery, Databricks, Redshift, Postgres and Trino. |
| Price per query | Yes. No charge per query, per viewer or per dashboard. | In part. Starter includes 5,000 queried metrics a month, and Enterprise 20,000. |
| Open source | No. Not open source. Your content is files you can clone and keep. | In part. MetricFlow is open source. The APIs and integrations need a paid dbt plan. |

## Where the dbt Semantic Layer is ahead, and why teams still select Kater

### Integrations with BI tools

**dbt Semantic Layer:** Power BI, Tableau, Google Sheets, Excel, Hex, Mode, Sigma and others query dbt metrics through an integration.

**Why teams still select Kater:** Kater has no connector to other BI tools. It has its own dashboards and apps on the same measures, and any agent reads them through the Kater MCP. Keep your BI tool beside it.

### Open source

**dbt Semantic Layer:** MetricFlow, the engine under the Semantic Layer, is open source under Apache 2.0. You can run it from the command line for free.

**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.

### Metrics beside your models

**dbt Semantic Layer:** Your metrics live in the same dbt project as your models, and ship in the same deploy.

**Why teams still select Kater:** Kater reads the tables dbt builds and the SQL it compiles. Your dbt project keeps running as it does today.

### More warehouses

**dbt Semantic Layer:** The dbt Semantic Layer connects to BigQuery, Databricks, Redshift, Postgres and Trino. 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/)

## How pricing compares

**dbt Semantic Layer:** The APIs and integrations need a paid dbt plan. Starter is $100 per user a month, with 5,000 queried metrics a month. Enterprise is quoted, with 20,000. Each metric in a successful request counts. MetricFlow is free to run from the command line.

**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 the dbt Semantic Layer

You do not have to move your dbt metrics. Keep them in dbt, and add Kater beside them. When you move one, you do not redefine it. Paste the SQL dbt compiled for its model into Kater, and wrap the columns in DIMENSION and MEASURE. The CTEs keep their dbt names, so lineage traces every measure back through each model it reads.

| In the dbt Semantic Layer | In Kater |
| --- | --- |
| dbt model | The SQL dbt compiled, pasted into a Kater model |
| Semantic model and its dimensions | Semantic model, with DIMENSION on each column |
| Entity | RELATIONSHIP join, checked on your warehouse |
| Simple metric | MEASURE around its aggregate, such as SUM(spend) |
| Ratio and derived metrics | A MEASURE that reads other measures with EVAL |
| Offset window, such as the same month last year | EVAL with SET, such as SET(month = CURRENT(month) - INTERVAL 1 YEAR) |
| Saved query | Query file |
| dbt test | Kater test, including same_as checks against your dbt marts |

`modeling/kater_objects/semantic_models/marts/AD_REPORTING__CAMPAIGN_REPORT.sql`:

```sql
-- 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
```

A model from Fivetran’s ad_reporting dbt package, in Kater. The CTEs hold the SQL dbt compiled, under their dbt names. DIMENSION and MEASURE wrap the columns the model already had, so lineage traces Spend back through each CTE to the Facebook, Google and TikTok tables.

Ask your agent to move one model at a time. A same_as test compares its numbers with your dbt mart, so you know they match. [How a switch works](https://kater.ai/switch-to-kater/#how)

## Questions

### Do we have to replace our dbt metrics?

No. Keep them in dbt for the tools that read them, and add Kater beside them. Move a metric to Kater when you want to.

### How do we move a dbt metric to Kater?

Paste the SQL dbt compiled for its model into Kater, and wrap the columns in DIMENSION and MEASURE. A ratio or derived metric becomes a MEASURE that reads other measures with EVAL. A same_as test then checks the numbers against your dbt mart.

### Does Kater keep lineage to our dbt models?

Yes. The CTEs keep their dbt names, and lineage traces each measure through every CTE to the columns it reads. Your agent reads the same SQL, so it sees where each number comes from.

### Do we need dbt for Kater?

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.

### Can we keep the dbt Semantic Layer and add Kater?

Yes, for as long as you want. Both read the same warehouse. Keep your dbt metrics for the BI tools that query them, and add Kater for dashboards, apps and answers in your team’s own agent. Move a metric when you want, and compare its numbers with dbt first.

## Compare another tool

- [Kater vs Tableau](https://kater.ai/kater-vs-tableau.md)
- [Kater vs Power BI](https://kater.ai/kater-vs-power-bi.md)
- [Kater vs Looker](https://kater.ai/kater-vs-looker.md)
- [Kater vs Sigma](https://kater.ai/kater-vs-sigma.md)
- [Kater vs Metabase](https://kater.ai/kater-vs-metabase.md)
- [Kater vs Hex](https://kater.ai/kater-vs-hex.md)
- [Kater vs Omni](https://kater.ai/kater-vs-omni.md)
- [Kater vs Lightdash](https://kater.ai/kater-vs-lightdash.md)
- [All comparisons](https://kater.ai/switch-to-kater.md)