Credible vs. building it yourself

A coding agent, a connection string, and the schema in the prompt. It works in the demo. In production it guesses what the data means, returns any row to anyone, and resends the schema on every question. Credible gives the agent a model of your domain, enforces who sees what on every query, and sends it only the slice it needs. No warehouse, no pipelines, no data team.

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What it looks like

What building it yourself means

A coding agent, a connection string and the schema in the prompt. The agent reads the tables, writes SQL for each question, and you wrap the result in an API or an app. Most builders start here because every piece is already on the laptop: Postgres or MySQL, an LLM and a framework. What the data means lives in the prompt or in the agent’s guesses, access control is whatever the connection string allows, and every schema change is a prompt change.

Category

Credible: The AI Analytics Engine — you write down what data means, and it generates the stack under it
building it yourself: Text-to-SQL you assemble: a coding agent, a database connection and an API wrapper

Who it is built for

Credible: The whole organization, not one central team — ops, finance and product; spreadsheet users through AI engineers
building it yourself: A developer who already has a database and a coding agent, and needs an answer this week

Primary interface

Credible: The agent you already use, over MCP — plus Credible Workspaces, data apps, 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
building it yourself: Your own app or API, with the agent writing SQL behind it

Modeling language

Credible: Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources
building it yourself: SQL, written by the agent per question; what things mean lives in the prompt, or nowhere

What is open

Credible: 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
building it yourself: All yours, and all yours to maintain — nothing shared beyond the libraries you picked

How the model gets built

Credible: 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
building it yourself: No model. The agent reads the schema and guesses the rest, again on every question

Governance

Credible: 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
building it yourself: Whatever the connection string allows. Row and column rules are app code written per app, and an audit trail is one more thing to build

Materialization and caching

Credible: 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
building it yourself: None unless you build it — every question is a live query against the production database

How an agent finds the right data

Credible: 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
building it yourself: The schema in the prompt, whole or the part you chose — grows with the database, and so does the token bill

Where the model can be used

Credible: 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
building it yourself: The one app or API you wrapped it in; another agent means another integration

Scale and portability

Credible: 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
building it yourself: Scales as far as the database and the prompt do; changing agent or framework means rewriting the prompts and the SQL

What you pay for

Credible: 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
building it yourself: LLM tokens on every question, including the schema you resend, plus the load on the database and the engineer who maintains it all

	Credible	building it yourself
Category	The AI Analytics Engine — you write down what data means, and it generates the stack under it	Text-to-SQL you assemble: a coding agent, a database connection and an API wrapper
Who it is built for	The whole organization, not one central team — ops, finance and product; spreadsheet users through AI engineers	A developer who already has a database and a coding agent, and needs an answer this week
Primary interface	The agent you already use, over MCP — plus Credible Workspaces, data apps, 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	Your own app or API, with the agent writing SQL behind it
Modeling language	Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources	SQL, written by the agent per question; what things mean lives in the prompt, or nowhere
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	All yours, and all yours to maintain — nothing shared beyond the libraries you picked
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	No model. The agent reads the schema and guesses the rest, again on every question
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	Whatever the connection string allows. Row and column rules are app code written per app, and an audit trail is one more thing to build
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	None unless you build it — every question is a live query against the production database
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 schema in the prompt, whole or the part you chose — grows with the database, and so does the token bill
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 one app or API you wrapped it in; another agent means another integration
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	Scales as far as the database and the prompt do; changing agent or framework means rewriting the prompts and the SQL
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	LLM tokens on every question, including the schema you resend, plus the load on the database and the engineer who maintains it all

A different premise

Where Credible takes a different approach

These are differences in premise, not a claim that building it yourself cannot work — it does, at a cost this page tries to state plainly. 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.

The schema is not the meaning

A schema says there is an orders table with a status column. It does not say that revenue excludes refunds, that “active” means signed in this month, or that two tables double-count when joined the obvious way. Text-to-SQL fills those gaps by guessing, and the guess sounds right. Credible has your agent draft a data model from your answers — what each metric means, how the tables relate, which window is “last month” — and every query is written against the model, not the tables, so a wrong join is a type error rather than a plausible number.

Who sees what, enforced rather than hoped for

An agent holding a database credential can return any row to anyone, so row-level rules, per-user access and an audit trail become code you write, and rewrite, in every app and for every agent. Credible declares access rules as annotations in the model and enforces them at one gateway on every query, logged to a permanent audit trail: the agent only gets back what the caller is allowed to see, whichever agent it is.

The slice, not the schema — and the same slice for every agent

Stuffing the whole schema into every prompt costs tokens and accuracy, and the bigger the database the worse it gets. Credible’s retrieval matches the question by meaning against an index of the model and returns the entities, definitions and suggested queries that answer it: a small request in, a small context out. The model is a plain file in your repository, served over MCP, so Claude, ChatGPT, Gemini, Cursor, Codex and the agent you build all read the same thing, and switching agents changes nothing underneath.

Fast without a warehouse to babysit

Hand-rolled SQL hits the production database on every question, and a popular question is a repeated scan. Credible materializes a source into its own storage with one annotation, keeps the rollups behind common questions warm, and refreshes incrementally, with no orchestrator to run. The database you connected is read, not loaded, and a question a team asks every Monday is answered from a small table instead of a scan.

Other comparisons

Compare Credible with the rest

Claims about the tools named here were checked against their public documentation in October 2026. Sources: PostgreSQL documentation — row security policies and roles; Model Context Protocol specification; Malloy and Malloy Publisher documentation.

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