8 Best Semantic Layer Tools in 2027
Semantic layers and context layers for AI compared on what you have to build first, MCP support, open source licences and verified pricing.
- Pro plan, €29 per user per month, with Guard
- Rated 5.0 on G2 (2 reviews)
Updated 14 November 2026. Prices checked 14 November 2026 against each vendor’s own pricing page.
last_order_at > now() - interval '90 days'- dbt Semantic Layer$100/user/mo
Teams already modelling in dbt
- CubeFree / $40 per dev/mo
An open source semantic layer for any warehouse
- Snowflake semantic viewsUsage-based
Teams whose data is all in Snowflake
- Databricks metric viewsDBU rates
Teams whose data is all in Databricks
- AtScaleContact sales
Enterprises serving one model to many BI tools
- Wren AI$179/mo
Open source text-to-SQL on a semantic model
- See SaturnSQL ContextSaturnSQL Context€29/user/mo
AI on the databases you have, with nothing to model
- Looker (LookML)Contact sales
Teams already on Looker
What is a semantic layer?
A semantic layer defines your metrics, dimensions and joins once, in code, so every dashboard, notebook and AI tool that asks for “revenue” gets the same SQL and the same number. dbt’s MetricFlow, Cube, LookML, AtScale, and the semantic views in Snowflake and Databricks all work this way.
They all ask for the same thing first: a model. Someone writes semantic models, cubes, views or LookML and keeps them in step with the database. For governed reporting that is worth it. For getting AI tools to stop guessing at column names, there is a lighter option, a context layer, and this list includes one.
If you want AI to understand your data before you model anything
SaturnSQL Context gives Claude, ChatGPT and Cursor the comments already in your database plus your team’s notes, definitions and synonyms, over MCP. There is nothing to model or deploy, and you can export it to Ossie or dbt YAML later. It is not a semantic layer: the AI still writes the SQL.
Feature comparison
| SaturnSQL Context | dbt Semantic Layer | Cube | Snowflake semantic views | Databricks metric views | Wren AI | |
|---|---|---|---|---|---|---|
| Useful before you model anything* | ✓ | — | — | — | — | ~ |
| Governed metrics that compile to SQL* | — | ✓ | ✓ | ✓ | ✓ | ✓ |
| MCP server* | ✓ Paid | ~ | ✓ | ✓ | ~ | ✓ |
| Open source* | — | ~ | ✓ | — | — | ~ |
| Snowflake and Databricks | — | ✓ | ✓ | Snowflake only | Databricks only | ✓ |
| Apache Ossie import or export* | ✓ | ? | ✓ | ? | ? | ? |
| Starting paid price | €29/user/mo | $100/user/mo | $40/dev/mo | Usage-based | DBU rates | $179/mo |
✓ available · ✓ Paid = not on the free tier · Sales = Enterprise plan, through sales · ~ partial · ? not documented by the vendor · — not available
- Useful before you model anything: SaturnSQL starts from the comments already in the database. Wren’s agent scaffolds the model for you, but it is still a model to review.
- Governed metrics that compile to SQL: SaturnSQL definitions carry optional SQL as guidance; the AI still writes the query.
- MCP server: dbt’s remote MCP server works on every plan, but Semantic Layer tools need Starter or above. Databricks’ managed MCP servers are in Public Preview.
- Open source: dbt’s MetricFlow is Apache-2.0; the hosted Semantic Layer is proprietary. Wren’s core engine is Apache-2.0, with some features commercial. Databricks is open sourcing metric views into Apache Spark.
- Apache Ossie import or export: SaturnSQL imports Ossie on Pro and exports Ossie and dbt YAML on every plan. Cube imports and exports Ossie from CLI v1.8.3. Snowflake, Databricks and dbt Labs take part in the Ossie project; we did not find a shipped converter in their docs.
Prices checked 14 November 2026 against each vendor’s own pricing page: dbt Semantic Layer · Cube · Snowflake semantic views · Databricks metric views · AtScale · Wren AI · SaturnSQL Context · Looker (LookML).
The 8 best semantic layer tools
dbt Semantic Layer
Best for: Teams already modelling in dbtThe dbt Semantic Layer is the default choice if your models already live in dbt: metrics are defined in YAML on top of those models with MetricFlow, now Apache-2.0, and served to BI and AI tools through dbt’s APIs and its MCP server, from the $100 per user per month Starter plan.
Strong
- + Metrics sit next to the dbt models and tests they depend on, in git
- + MetricFlow is Apache-2.0 from v0.209.0
- + dbt’s MCP server runs locally or hosted, and can query Semantic Layer metrics
- + Snowflake, BigQuery, Databricks, Redshift, Postgres and Trino
Limits
- − APIs and integrations need a paid dbt platform plan; locally you only query from the CLI
- − Starter includes 5,000 queried metrics a month, Enterprise 20,000
- − You write and maintain semantic models before any AI tool benefits
Pricing: Developer is free but does not include the Semantic Layer. Starter is $100 per user per month with 5,000 queried metrics a month. Enterprise and Enterprise+ are custom. dbt Labs and Fivetran completed their merger on 1 June 2026. pricing page, checked 14 November 2026.
Cube
Best for: An open source semantic layer for any warehouseCube is the strongest open source semantic layer we checked: Cube Core is Apache-2.0 and works over about 30 data sources, and Cube Cloud adds a hosted MCP server on every plan, the free one included, with paid plans from $40 per developer per month.
Strong
- + Cube Core is Apache-2.0, with SQL, REST and GraphQL APIs
- + Hosted MCP server with about 30 tools on every Cube Cloud plan
- + About 30 data sources, including Snowflake, BigQuery, Databricks, Postgres and ClickHouse
- + Imports and exports Apache Ossie from CLI v1.8.3
Limits
- − You build a data model of cubes and views in YAML or JavaScript first
- − The free Cloud plan caps requests at 1,000 a day
- − Explorer and Viewer seats cost extra on Premium
Pricing: Cube Core is free and Apache-2.0. Cube Cloud Free is $0 with 1,000 requests a day, Starter $40 per developer per month, Premium $80 per developer per month plus $40 Explorer and $20 Viewer seats, Enterprise custom. pricing page, checked 14 November 2026.
Snowflake semantic views
Best for: Teams whose data is all in SnowflakeSnowflake semantic views are the natural pick when everything is in Snowflake: logical tables, relationships and metrics live as schema objects that Cortex Analyst and Snowflake’s managed MCP server use, billed per message on top of warehouse compute, but they work on Snowflake only.
Strong
- + Semantic views are native schema objects, governed with the rest of Snowflake
- + Managed MCP server, generally available since November 2025
- + Semantic View Autopilot builds views from YAML, Tableau or Power BI files
Limits
- − Snowflake only
- − Cortex Analyst is billed per message plus the compute for the SQL it generates
- − A semantic view must exist before Cortex Analyst can answer
Pricing: No separate licence. Cortex Analyst is billed per message processed at Snowflake’s published consumption rates, plus warehouse compute for the generated SQL. pricing page, checked 14 November 2026.
Databricks metric views
Best for: Teams whose data is all in DatabricksDatabricks metric views give Unity Catalog generally available, YAML-defined metrics that Genie and AI/BI use with no extra licence fee, and Databricks says the core is being open sourced in Apache Spark, but like Snowflake’s they work on one platform only.
Strong
- + Metrics governed in Unity Catalog with the rest of your data
- + No additional licence fee for AI/BI; standard DBU rates apply
- + Managed MCP endpoints for Genie, Databricks SQL and UC functions
Limits
- − Databricks only
- − Managed MCP servers are in Public Preview and must be enabled by an admin
- − Genie compute is billed separately
Pricing: No additional licence fee for AI/BI. Standard Databricks DBU rates apply, and Genie compute is billed separately. pricing page, checked 14 November 2026.
AtScale
Best for: Enterprises serving one model to many BI toolsAtScale is an enterprise universal semantic layer that computes metrics in your own warehouse across Snowflake, Databricks, BigQuery, Redshift and more, with its modelling language SML published as Apache-2.0 and an MCP server for agents, but pricing is consumption-based and not published.
Strong
- + One model served to many BI tools and agents
- + SML, its YAML modelling language, is Apache-2.0
- + MCP server that queries published semantic models
Limits
- − No published price or free tier
- − Agents only see models you have published
Pricing: Not published. AtScale prices on consumption of deployed semantic objects, through sales. pricing page, checked 14 November 2026.
Wren AI
Best for: Open source text-to-SQL on a semantic modelWren AI pairs an Apache-2.0 semantic engine with text-to-SQL and an MCP server, and its agent scaffolds the MDL model for you, with hosted plans from $179 a month for unlimited users, billed annually.
Strong
- + Core engine, SDK and skills are Apache-2.0
- + MCP server in the open source engine and every Cloud plan
- + 20+ sources, including BigQuery, Snowflake, Postgres, ClickHouse and Databricks
Limits
- − Row and column security and the GenBI interface are commercial
- − MCP calls draw from the same credit pool as the team
- − Cloud prices are for annual billing
Pricing: Open source engine free. Essential Cloud $179 a month and Enterprise Cloud $559 a month, billed annually, unlimited users. Enterprise Plus by quote. pricing page, checked 14 November 2026.
SaturnSQL Context
Best for: AI on the databases you have, with nothing to modelSaturnSQL Context is not a semantic layer but the no-modelling option: it sends the comments already in your database, plus notes, definitions and synonyms your team writes in plain language, to Claude, ChatGPT and Cursor over MCP, so answers improve on day one, though the AI still writes the SQL and nothing compiles a metric.
Strong
- + Reads table and column comments on PostgreSQL, Redshift, MySQL, SQL Server, Oracle, BigQuery and ClickHouse
- + Notes, definitions with optional SQL, descriptions and synonyms, edited in the app, over REST or by the AI tool
- + Imports Apache Ossie YAML, and exports it and dbt schema.yml on every plan
- + Guard’s access rules apply: hidden tables and columns stay out of a person’s context
Limits
- − No metrics engine: definitions guide the AI, they don’t compile
- − No Snowflake or Databricks
- − No lineage, and no relationships between tables
Pricing: Context comes with the Pro plan, €29 per user per month with Guard and everything in Starter. Pro is coming soon, with a waitlist. Database comments reach AI tools from Starter at €19. pricing page, checked 14 November 2026.
Looker (LookML)
Best for: Teams already on LookerLookML is Looker’s modelling layer and worth using if you already pay for Looker, which now has a managed MCP server in preview at no extra charge, but Looker’s prices are not published and it is a whole BI platform to adopt for the model.
Strong
- + LookML models and Explores that every Looker user and tool shares
- + Managed MCP server in preview, plus the open source MCP Toolbox
Limits
- − No published price
- − MCP tools are off by default, and calls count against API quotas: 1,000 a month on Standard
- − You adopt Looker to get LookML
Pricing: Not published. Standard, Enterprise and Embed editions each include 10 standard and 2 developer users. pricing page, checked 14 November 2026.
Other options
- Kaelio ktxFree (Apache 2.0)
Kaelio’s ktx is the closest open source relative of SaturnSQL Context: an Apache-2.0 CLI and MCP server that builds a Markdown wiki and semantic-layer YAML from your warehouse, dbt, LookML, Metabase and docs with your own LLM key, committed to git, while a hosted ktx Cloud has no published price. Pricing.
- Power BI semantic models$14/user/mo
Power BI semantic models hold tables, relationships and DAX measures, and Microsoft now ships a Power BI authoring MCP server and a Fabric IQ MCP server for asking questions, with Power BI Pro at $14 per user per month. Pricing.
- Dremio$0.20/DCU
Dremio is a lakehouse query engine with an AI semantic layer and a hosted MCP server per Dremio Cloud project, priced at $0.20 per DCU, and its site now says Dremio is part of SAP. Pricing.
Semantic layer or context layer?
Choose a semantic layer when a number has to match to the cent in every tool, when analytics engineers already own a dbt project, or when one warehouse holds everything and its native semantic views come with it.
Choose a context layer when the problem is that AI tools guess: they pick orders over orders_v2, sum cents as euros, or don’t know what your team calls an active customer. SaturnSQL Context starts from the table and column comments in PostgreSQL, Redshift, MySQL, SQL Server, Oracle, BigQuery and ClickHouse, and lets anyone who knows the data add notes, definitions and synonyms in plain language, in the app or by asking the AI tool to save them. Paired with Guard, hidden tables and columns stay out of each person’s context.
The two are not exclusive. Context imports and exports Apache Ossie YAML, the open semantic model format Cube also reads and writes, and exports dbt schema.yml, so a model you already have can start your context, and the descriptions you write now can seed a semantic layer later. If you are weighing a data catalog instead, see Atlan alternatives.
How we judged
We ranked on how well each tool gets correct answers out of AI tools and BI: what you must build before it helps, whether it governs metrics, which warehouses it covers, MCP support, licence and price. SaturnSQL Context is ranked seventh because it is a different kind of tool, best for one job, and has no metrics engine. Every price, licence and feature was checked against the vendor’s own site, docs or repository on 14 November 2026 and is linked from its entry. Where a vendor does not document something, the table says unknown.
Frequently asked questions
What is a semantic layer?
A semantic layer defines business metrics, dimensions and joins once, in code, and every tool that queries through it gets the same SQL and the same number. dbt’s MetricFlow, Cube, LookML, AtScale and the semantic views in Snowflake and Databricks are all semantic layers. AI tools use them by asking for a metric instead of writing SQL from scratch.
What is the best semantic layer tool?
If your models are in dbt, the dbt Semantic Layer. For an open source layer over any warehouse, Cube. If all your data is in Snowflake or Databricks, their native semantic views or metric views. For one model served to many BI tools in a large company, AtScale. If you want AI to understand your existing databases without modelling first, SaturnSQL Context, which is a context layer rather than a semantic layer.
What is the best open source semantic layer?
Cube, whose Core is Apache-2.0 and works over about 30 data sources. dbt’s MetricFlow is Apache-2.0 from v0.209.0, though the hosted dbt Semantic Layer is proprietary. Wren AI’s core engine is Apache-2.0, AtScale publishes its modelling language SML as Apache-2.0, and Databricks says it is open sourcing metric views into Apache Spark. Kaelio’s ktx is an Apache-2.0 context layer rather than a semantic layer.
Do I need a semantic layer for AI?
Only for numbers that must match exactly every time. A semantic layer makes an AI query a governed metric instead of writing SQL, which is the right call for finance reporting. For most questions, an AI fails because it lacks meaning, such as amounts in cents or which table is current, and a context layer of descriptions, notes and definitions fixes that without modelling.
What is the difference between a semantic layer and a context layer?
A semantic layer compiles metrics: you model them, and tools query them. A context layer describes the data in plain language so an AI can write the SQL itself. A semantic layer gives identical numbers but takes weeks to build; a context layer is useful in minutes but guides the AI rather than guaranteeing the result.
What is Apache Ossie?
Apache Ossie, formerly Open Semantic Interchange (OSI), is an open YAML format for semantic models, incubating at the Apache Software Foundation under the Apache-2.0 licence since July 2026. Snowflake is a founding member, and Cube, AtScale, Kyvos and Databricks have joined. Cube imports and exports it, and so does SaturnSQL Context.
How much does a semantic layer cost?
Cube Core, MetricFlow, Wren AI’s engine and ktx are free to self-host. Cube Cloud starts free, then $40 per developer per month. dbt Starter, which includes the Semantic Layer, is $100 per user per month. Wren AI Cloud starts at $179 a month for unlimited users, billed annually. Snowflake and Databricks charge by usage. AtScale and Looker do not publish prices. SaturnSQL Context comes with the Pro plan at €29 per user per month.
Give AI tools the meaning behind your schema, with nothing to model.
