Sigma put a spreadsheet over the warehouse and it works — including agents that run inside your own warehouse. Credible is the open analytics engine underneath, serving governed meaning to any surface rather than one interface.
Already decided? Migrate from Sigma walks through what carries over.
How Sigma describes itself
Sigma is warehouse-native BI with a spreadsheet interface: live queries against your cloud warehouse, writeback and automation, and Sigma Agents that run inside your warehouse within its existing security and governance.
Credible and Sigma compared across 12 dimensions.
| Credible | Sigma | |
|---|---|---|
| Category | The AI Analytics Engine — you write down what data means, and it generates the stack under it | Warehouse-native BI with a spreadsheet interface |
| Who it is built for | The whole organization, not one central team — ops, finance and product; spreadsheet users through ML engineers | Business users who would rather work in a spreadsheet |
| 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 spreadsheet over live warehouse queries |
| Modeling language | Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources | Spreadsheet formulas over Sigma’s in-product model |
| 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 model, running on your own warehouse for compute |
| 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 | Built in the spreadsheet interface as people work |
| 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 | Inherits warehouse permissions, with row-level security configured in Sigma |
| 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 | Pushes queries to the warehouse live and caches results; materialization is the warehouse’s job |
| 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 | A Search tool alongside Analyze and Build, so finding data is a search rather than a walk |
| 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 Sigma UI and embeds; Sigma Agents run inside your own warehouse |
| 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 | Compute is your warehouse, which scales well; the model lives in Sigma |
| 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 | Sales-led role-based seats, with the AI features bundled into the seat rather than metered |
A different premise
These are differences in what each product set out to be, not faults in Sigma. Credible was built the other way around from a BI tool: collect what your data means, write it down as a data model in open-source Malloy, and serve that meaning over MCP to the agent you already use — with a UI of our own available, but never assumed.
Sigma puts building in reach of anyone who can use a spreadsheet, and the logic lives where the person is — in formulas and workbooks. That is the whole reason it gets adopted. Credible takes the other side: one versioned, reviewable model in Malloy with imports and inheritance, so a definition does not fork per workbook. Which is right depends on whether your harder problem today is access or agreement.
Sigma runs compute on your own warehouse, which is a genuinely good architecture, and its model lives in Sigma. Credible is open core — open-source Malloy across seven warehouses, served by Malloy Publisher, which we maintain in the open — so the meaning is a file you keep whatever else changes.
Sigma Agents work inside Sigma’s world, which is a coherent design when the spreadsheet is where the work happens and lets them run agents inside your warehouse boundary. Credible delivers the same governed context to agents over MCP and to APIs, embedded dashboards, notebooks and data apps, so one definition serves all of them and people can stay in whichever surface they already work in.
Access rules declared as annotations in the model, globally distributed high-availability serving, and materialization as one annotation, held hot in our serving layer rather than a cache you operate. Sigma pushes compute to the warehouse on purpose, which is why it has so little to run. We took it on because production agent traffic is the load we set out to carry, and because a source can then be served fast without the warehouse being the thing that makes it fast.
Other comparisons
Capability claims about Sigma were checked against public sources in August 2026. Products in this category change quickly — confirm anything decision-critical with the vendor. Sources: Sigma Series E — SiliconANGLE, Sigma; Sigma Agents and ARR — Sigma announcements; Warehouse-native architecture and security model — Sigma documentation.
Ready when you are
See what a governed model looks like when agents, not dashboards, are the primary consumer.