| Category | The AI Analytics Engine — you write down what data means, and it generates the stack under it | AI data analyst over the whole stack, with an ontology layer and a warehouse of its own |
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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 | Enterprise data teams, and the business users whose questions queue behind them |
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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 | Chat — threads with Ana in the TextQL app, in Slack, and from any MCP client |
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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 | .tql — a SQL-native semantic layer format, kept as reviewable files in a git-backed ontology repository |
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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 | Vendor-owned format, with the ontology itself kept as files in a repository you control |
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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 | Ana maps relationships across the connected sources, and .tql files are authored and reviewed on top |
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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 | Role-based access control with SSO and SCIM, and role and tenant rules declared in the ontology |
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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 | A purpose-built warehouse running inside your environment, so exploratory work over raw data does not wait on modeling |
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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 | Ana works from the ontology and its connectors, and picks its own tools per question rather than walking a fixed sequence |
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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 | Threads in the app, Slack, playbooks scheduled to email, embeds, a v2 REST API, and Ana itself as an 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 | Over 50 connectors — warehouses, BI tools and SaaS APIs — deployed as SaaS, in your VPC, or on-premises by Helm chart |
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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 tiered subscription carrying a monthly allowance of Agent Compute Units, with overage beyond it and no per-seat charge |
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