| Category | AI-native analytics engine with an integrated data stack | Warehouse-native AI — natural-language-to-SQL over Snowflake semantic views |
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| Who it is built for | AI product and analytics teams, plus anyone who works with data — spreadsheet users through ML engineers | Data teams already standardized on Snowflake |
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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 | Ask in English inside Snowflake; semantic views authored in SQL and YAML |
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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 | Semantic views, declared in SQL DDL and YAML, native to Snowflake |
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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, and Snowflake started the initiative now incubating as Apache Ossie |
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| How the model gets built | A coding agent with open-source MCP tools and agent skills, capturing context from where it already lives — run it in the editor your team already uses, or in Credible Workspaces, which packages the same skills in a familiar app | Autopilot generates a semantic view from your tables, hand-tuned afterwards |
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| Governance | Governance as code. Access rules are annotations in the model itself — versioned, reviewed and composable like any other code | Snowflake’s own role-based access control, inherited |
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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 | Whatever Snowflake gives you — result cache, dynamic tables, materialized views. Not a Cortex concern |
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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 | Cortex Search retrieves dimension values by hybrid keyword and vector search, re-ranked. Which semantic view to use is configured, not searched |
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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 | Snowflake APIs and Cortex Agents — inside Snowflake only |
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| Scale and portability | Built for globally distributed, high-availability workloads. Warehouse-agnostic on open-source Malloy | Scales with Snowflake, and runs only on data that is in Snowflake |
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| What you pay for | Usage, not seats — unlimited users on every plan, so adding people never changes the bill. Metered per organization on tokens, compute time, bytes served and storage, starting free. Your own agent’s tokens are never billed, and a query that runs on your own warehouse is not metered for the compute — only for the result it hands back | Snowflake credits — a flat AI credit rate for Cortex on top of the platform credits the account already burns |
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