| Category | The AI Analytics Engine — you write down what data means, and it generates the stack under it | Open-source BI for dbt teams, positioned as agentic BI |
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| Who it is built for | The whole organization, not one central team — ops, finance and product; spreadsheet users through ML engineers | Analytics engineers and data teams who already work in dbt |
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| Primary interface | The agent you already use, over MCP — plus Credible Workspaces, dashboards, notebooks and reports for the people who want a UI. Every one of them reads the same model, and the model lives in your repository | Its own UI — a metrics catalog, charts and dashboards — with AI agents in the app, in Slack and in Teams |
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| Modeling language | Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources | YAML — metrics and dimensions defined in your dbt project, or in Lightdash YAML if you do not have one |
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| What is open | Open core. Malloy is open source, and we build and maintain Malloy Publisher — the open-source server for Malloy models, and where our agent skills and MCP retrieval tools are open source too, runnable in any agent with no Credible account. Your model is code in your repository, and Credible is in the Apache Ossie ecosystem for semantic interchange | Open core — core BI features are MIT-licensed and self-hostable; advanced security, customization, support and Lightdash Cloud are commercial |
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| How the model gets built | Model first: you write down what data means, and the engine derives the pipelines, storage and serving from it. Open-source MCP tools and agent skills help you capture that meaning from where it already lives, running in your coding agent | Hand-authored YAML beside your dbt models, with agents that propose changes back to the layer for review |
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| Governance | One annotation on the source, in the same file as the logic it governs — no grant objects to declare, no rule to re-attach on every surface that exposes the data, no attribute table kept in step by hand. A row filter or an authorize gate over attributes the server resolves from verified identity, and everything reading that source inherits it: dashboards, notebooks and agents alike | Project and space permissions plus user attributes for row-level access, and an agent answers as the person asking |
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| Materialization and caching | One annotation on the source, in the same file as the logic it materializes — no derived-table block, no rollup definitions, no refresh triggers, no orchestration run to schedule. Optional per source: query your own warehouse directly, or hold a source hot in Credible’s own storage, which is how you get fast serving without buying or banging your head against a warehouse to get it | Caching declared in the context layer alongside the metrics; heavier materialization stays upstream in dbt |
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| How an agent finds the right data | Search. Typed targets — source, dimension, measure, view, even a dimensional value — are matched against an index of the model and come back ranked, so an agent asks for what it needs instead of picking a model and touring it | The agent selects models and metrics out of the semantic layer, with knowledge documents you upload as extra context, and a router sends a question to the team agent scoped to it |
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| Where the model can be used | One model served to every surface — agents over MCP, plus APIs, an SDK, embedded dashboards, notebooks and HTML data apps. Results carry an interactive UI resource (MCP Apps, the official extension), so a client that supports it renders a real chart or table instead of the model re-narrating rows | The Lightdash UI, Slack and Teams, a REST API, a Python SDK, an SDK for embedding dashboards, agents and data apps, and the Lightdash MCP server |
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| Scale and portability | Globally distributed, high-availability serving that scales horizontally — workers scale out under load rather than being sized in advance. Warehouse-agnostic on open-source Malloy, and the model is portable because the language is | Self-hosted or Lightdash Cloud, over BigQuery, Snowflake, Redshift, Databricks, Postgres, Trino and ClickHouse |
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| What you pay for | Usage, not seats — unlimited users on every plan, so adding people never changes the bill. Three meters, pooled per organization and starting free: tokens, compute time and storage. Your agent’s own LLM tokens are never billed to you, and a query that runs on your own warehouse is not metered for the compute — only for the result it hands back | A flat platform fee for unlimited users on Cloud, nothing if you self-host, with embedded loads metered separately |
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