Credible vs. Genloop

Genloop discovers a context graph from your data and sharpens it through use, answering in chat, Slack and MCP clients, and it will run air-gapped. Credible produces a model instead — open-source Malloy in your repository, reviewed as code and served to every surface.

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

How Genloop describes itself

What Genloop is

Genloop is an agentic analytics platform. It connects to structured, semi-structured and unstructured sources without moving them, builds a living context graph covering data, processes, decisions and people, and answers in plain language with its reasoning and a confidence score attached. A Review Center lets the data team approve what it learns, it deploys in cloud, VPC, on-premises or fully air-gapped with no external LLM calls, and Genloop reports the top score on the Spider 2.0-Snow text-to-SQL benchmark.

Category

Credible: The AI Analytics Engine — you write down what data means, and it generates the stack under it
Genloop: Agentic analytics on a context graph the platform discovers and keeps learning

Who it is built for

Credible: The whole organization, not one central team — ops, finance and product; spreadsheet users through ML engineers
Genloop: Business teams — finance, marketing, sales, operations — asking without a BI project first

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
Genloop: Chat — in Genloop, in Slack, and from MCP clients including Claude Code and ChatGPT

Modeling language

Credible: Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources
Genloop: None to author. The context graph is discovered from schema and documents, then refined by use

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
Genloop: Vendor-owned, and model-agnostic — it can run air-gapped, with no external LLM calls

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
Genloop: Auto-discovered, then corrected in a Review Center where the data team approves what it learned

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
Genloop: Role-based access enforced per conversation, with a review loop over what it learns; SOC 2 Type II and ISO 27001

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
Genloop: Reads in place and stores no copy, so performance stays with the source system

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
Genloop: The context graph, plus routing along investigation paths that have been run and verified before

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
Genloop: Chat, Slack, embedded analytics and MCP, with one shared context so a correction made in one place holds in the others

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
Genloop: Reads existing sources without migration; deployable in cloud, VPC, on-premises or air-gapped

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
Genloop: Credit tiers — a free monthly grant, then paid plans that each include a pool of credits

	Credible	Genloop
Category	The AI Analytics Engine — you write down what data means, and it generates the stack under it	Agentic analytics on a context graph the platform discovers and keeps learning
Who it is built for	The whole organization, not one central team — ops, finance and product; spreadsheet users through ML engineers	Business teams — finance, marketing, sales, operations — asking without a BI project first
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	Chat — in Genloop, in Slack, and from MCP clients including Claude Code and ChatGPT
Modeling language	Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources	None to author. The context graph is discovered from schema and documents, then refined by use
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 model-agnostic — it can run air-gapped, with no external LLM calls
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	Auto-discovered, then corrected in a Review Center where the data team approves what it learned
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	Role-based access enforced per conversation, with a review loop over what it learns; SOC 2 Type II and ISO 27001
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	Reads in place and stores no copy, so performance stays with the source system
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 context graph, plus routing along investigation paths that have been run and verified before
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	Chat, Slack, embedded analytics and MCP, with one shared context so a correction made in one place holds in the others
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	Reads existing sources without migration; deployable in cloud, VPC, on-premises or air-gapped
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	Credit tiers — a free monthly grant, then paid plans that each include a pool of credits

A different premise

Where Credible takes a different approach

These are differences in what each product set out to be, not faults in Genloop. 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 graph that learns, and a model you can read

Genloop bootstraps its context graph from your schema and documents and refines it with every interaction, with a Review Center where the data team approves what it learned — so nobody has to write a model before the first question gets answered. Credible has a coding agent do the modeling instead, and the output is Malloy in your repository: a file you can read, diff and revert, that states what a metric means whether or not anyone is currently asking. Two answers to the same question — where meaning lives once it has been worked out.

Open source, and an open format

Genloop is model-agnostic and will run inside your walls with no external LLM calls, which is the part of openness a regulated buyer feels first. The part we bet on is the format: Malloy is an open-source language, Malloy Publisher is the open-source server we build and maintain for it, and Credible is in the Apache Ossie ecosystem — so the model outlives whoever is serving it, and other tools can read it.

Governance declared, and versioned

Genloop enforces role-based access per conversation rather than at login, and routes corrections through a review loop before they change an answer — governance as an operating process the data team runs. Credible declares access rules as annotations in the model itself, so they are reviewed in the same diff as the logic they protect and travel with the definitions to every surface that consumes them.

What the model is asked to cover

Genloop reaches across structured, semi-structured and unstructured sources at once, and answering from a warehouse and a pile of documents in the same breath is a hard problem to take on. Credible points at governed structured data — seven warehouses on open-source Malloy — and puts the depth into what a definition means, who may see it, and how it is served to agents, applications, embeds, notebooks and data apps. Materialization is part of that depth and part of the difference: Genloop deliberately keeps no copy of your data, where Credible can hold a source hot in its own serving layer when you want that source served fast — your choice, per source.

Other comparisons

Compare Credible with the rest

Capability claims about Genloop were checked against public sources in August 2026. Products in this category change quickly — confirm anything decision-critical with the vendor. Sources: Platform architecture and the living context graph — Genloop; Governance, deployment options and certifications — Genloop; Spider 2.0-Snow result — Genloop, March 2026.

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.