Credible vs. Hex

Hex is where analysts write SQL, Python and R in one document, with AI cells throughout. It is built to consume a semantic model rather than be one — so it sits alongside Credible more naturally than against it.

Already decided? See how a migration works — the same method applies even without a Hex-specific guide yet.

How Hex describes itself

What Hex is

Hex is a collaborative notebook and data-app platform — SQL, Python, R, no-code and AI cells in one document — with a Notebook Agent, Magic AI, a semantic model agent on its Team tier, and the ability to import modeled data from external semantic layers including dbt MetricFlow, Cube and Snowflake semantic views.

Category

Credible: The AI Analytics Engine — you write down what data means, and it generates the stack under it
Hex: Collaborative notebooks and data apps, with AI agents

Who it is built for

Credible: The whole organization, not one central team — ops, finance and product; spreadsheet users through ML engineers
Hex: Data scientists and analysts working in SQL, Python and R

Primary interface

Credible: 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
Hex: A notebook — SQL, Python, no-code and AI cells in one document

Modeling language

Credible: Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources
Hex: SQL, Python and R, aimed at analysis rather than a shared governed model

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
Hex: Vendor-owned, and designed to consume external models — dbt MetricFlow, Cube, Snowflake semantic views

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
Hex: Per-notebook analysis, with a semantic model agent on the Team tier; can import an external model

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
Hex: Workspace permissions and data connection scoping; governance is inherited, not defined here

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
Hex: Caches cell and query results per notebook run; no shared materialization layer

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
Hex: Per notebook. Hex imports a model from an external semantic layer, and its agents work within that notebook’s context

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
Hex: Notebooks, published apps and embeds, for people rather than agents; imports a model rather than serving one

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
Hex: Scales with your warehouse for queries and Hex for compute

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
Hex: Per-editor seats plus compute, with AI metered as effort-based credits priced by task complexity

	Credible	Hex
Category	The AI Analytics Engine — you write down what data means, and it generates the stack under it	Collaborative notebooks and data apps, with AI agents
Who it is built for	The whole organization, not one central team — ops, finance and product; spreadsheet users through ML engineers	Data scientists and analysts working in SQL, Python and R
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	A notebook — SQL, Python, no-code and AI cells in one document
Modeling language	Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources	SQL, Python and R, aimed at analysis rather than a shared governed 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, and designed to consume external models — dbt MetricFlow, Cube, Snowflake semantic views
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	Per-notebook analysis, with a semantic model agent on the Team tier; can import an external model
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	Workspace permissions and data connection scoping; governance is inherited, not defined here
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	Caches cell and query results per notebook run; no shared materialization layer
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	Per notebook. Hex imports a model from an external semantic layer, and its agents work within that notebook’s context
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	Notebooks, published apps and embeds, for people rather than agents; imports a model rather than serving one
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 with your warehouse for queries and Hex for compute
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	Per-editor seats plus compute, with AI metered as effort-based credits priced by task complexity

A different premise

Where Credible takes a different approach

These are differences in what each product set out to be, not faults in Hex. 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.

A shared model under the notebooks

Logic written in a notebook is scoped to that notebook, which is exactly what you want while a question is still being explored. Once an answer is settled, it helps for the definition to live somewhere the next analyst inherits. Malloy puts definitions in one versioned model with imports and inheritance, and Malloy notebooks read from that model rather than redefining it. Hex can consume the same model, so Credible works as the layer under Hex as readily as instead of it.

Governance declared, not inherited

Hex scopes access at the workspace and data-connection level and inherits the rest from the warehouse, which is reasonable for a tool analysts drive. Credible declares row, column and role rules as annotations in the model, so they travel with the definitions wherever those definitions are consumed.

The same definitions answer everywhere

Hex delivers notebooks, apps and embeds to people, and does that well. Credible ships notebooks, reports and HTML data apps too, and serves the same governed context over MCP to agents, applications, embedded dashboards and APIs — so one definition answers in an analysis someone opened, in a product feature and in an automated workflow that nobody watched.

The model is built for you, and it is open

Hex has a semantic model agent on its Team tier that helps build a model inside Hex. Credible’s coding agent builds the model from context that already exists across your stack, and the result is open-source Malloy — a file in your repository, served by an open-source server, rather than a workspace artifact.

Other comparisons

Compare Credible with the rest

Capability claims about Hex were checked against public sources in August 2026. Products in this category change quickly — confirm anything decision-critical with the vendor. Sources: Notebook Agent and semantic model features — Hex blog; External semantic layer imports — Hex documentation; Hex product tiers and capabilities — Hex.

Ready when you are

Bring your own data and judge for yourself

See what a governed model looks like when agents, not dashboards, are the primary consumer.