| Category | AI-native analytics engine with an integrated data stack | Visual analytics suite, repositioned around Tableau Next and Agentforce |
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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 | Analysts and business users who work visually |
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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 — drag-and-drop visual authoring, with Pulse and Agentforce agents alongside |
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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 | Calculated fields and Tableau Semantics, purpose-built for Tableau |
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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, with a stated intent to open the Tableau Next semantic layer |
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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 | Hand-built data sources and calculations; Tableau Semantics adds a shared layer |
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| Governance | Governance as code. Access rules are annotations in the model itself — versioned, reviewed and composable like any other code | Row-level security and permissions in the tool, plus Salesforce identity |
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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 | Hyper extracts, refreshed on a schedule, or live connections. Mature |
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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 | List and describe the published data sources, then query them; the MCP server is hosted on Tableau Cloud |
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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 Tableau UI, embeds, VizQL Data Service and an MCP server hosted on Tableau Cloud; Tableau Next agents run on the Salesforce platform |
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| Scale and portability | Built for globally distributed, high-availability workloads. Warehouse-agnostic on open-source Malloy | Scales with Tableau Cloud or Server; Tableau Next is tied to the Salesforce platform |
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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 | Per-seat by role, with Creator, Explorer and Viewer priced differently |
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