Omni pairs a spreadsheet-style analyst experience with a governed semantic layer, built by people who built Looker, and it serves external agents over MCP. Credible is an open analytics engine where BI is one surface on the model rather than the whole product — and that ordering is the real difference.
How Omni describes itself
Omni positions itself as Looker done right: the speed of a spreadsheet with the governance of a semantic layer, built by people who built Looker. It has added an AI layer — agent skills, a modeling agent, and an MCP server external assistants can query.
Credible and Omni compared across 12 dimensions.
| Credible | Omni | |
|---|---|---|
| Category | AI-native analytics engine with an integrated data stack | Modern cloud BI with a built-in semantic layer |
| Who it is built for | AI product and analytics teams, plus anyone who works with data — spreadsheet users through ML engineers | Analytics teams that want fast, self-serve BI |
| Primary interface | The agent you already use, over MCP — plus Workspaces, notebooks, reports and data apps for the people who want a UI. All of them consume the same model rather than being the place it lives | Its own UI — spreadsheet plus dashboard building, modeling in the interface with code underneath |
| Modeling language | Malloy — a modeling and query language with imports, inheritance, and public and private members | Omni’s own modeling format, in the LookML tradition |
| What is open | Open core. Malloy is open source, and we build and maintain Malloy Publisher, the open-source server for Malloy models. Your model is code in your repository, and Credible is in the Apache Ossie ecosystem for semantic interchange | Vendor-owned, with bi-directional dbt sync so existing dbt models come along |
| How the model gets built | A coding agent with open-source MCP tools and agent skills, capturing context from where it already lives | UI and code together, with a modeling agent suggesting metrics and join paths |
| Governance | Governance as code. Access rules are annotations in the model itself — versioned, reviewed and composable like any other code | Row and column security configured inside the tool |
| 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 in-memory serving layer, which is how a team on Postgres gets fast serving without buying a warehouse to get it | Query-result caching with schedule-based refresh; heavier materialization is left to your warehouse or dbt |
| 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 | Three steps the client walks in order: pickModel, then pickTopic within it, then getData |
| 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 Omni UI, embedded analytics, and an MCP server external assistants can query |
| Scale and portability | Built for globally distributed, high-availability workloads. Warehouse-agnostic on open-source Malloy — BigQuery, Snowflake, Postgres, MySQL, Trino, Presto and DuckDB, which also reads Parquet straight out of object storage including Azure Data Lake — so the model travels | Single-vendor cloud BI, running in Omni’s cloud |
| What you pay for | Usage, not seats — unlimited users on every plan, so adding people never changes the bill. Metered per organization on tokens, bytes processed, bytes served and hot 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 scan — only for the result it hands back | Sales-led — a platform fee plus role-based seats, with no public price list |
A different premise
These are differences in what each product set out to be, not faults in Omni. Credible was built the other way around from a BI tool: capture what your data means, enrich it into a governed 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.
Omni’s modeling format is its own, in the LookML tradition, and the team behind it knows that tradition better than almost anyone. Credible went open core: 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. It is a slower way to build a product and we think it is the right one, because a model written in the open outlives whoever is serving it.
Omni’s modeling agent suggests metrics and join paths to a person working in its UI, and it does that well. Credible’s coding agent builds the model itself with open MCP tools and skills, drawing on context already in your stack — and because the output is Malloy in your repository, it arrives as a diff you review like any other code. Same goal, different assumption about who is at the keyboard.
Omni serves agents well, and it does so as a BI product: the UI is the center of gravity and the model supports it, which is what makes the analyst experience as good as it is. Credible is arranged the other way round — the model is the product, and Workspaces, notebooks, reports, data apps, agents and applications are all consumers of it. You get a BI surface either way; the difference is whether the model exists to serve it or the other way about.
Governance as annotations in the model, globally distributed high-availability serving, and materialization you declare with one annotation and we hold hot in memory, rather than a cache you operate. Omni leaves the heavier end of this to your warehouse or dbt, which keeps its own product focused. We took it on because serving context to production agent traffic is the job we picked — and because it means the warehouse does not have to be the thing that makes a query fast.
Other comparisons
Capability claims about Omni were checked against public sources in August 2026. Products in this category change quickly — confirm anything decision-critical with the vendor. Sources: Omni Series C — Omni, BusinessWire, TechCrunch; Omni MCP server and AI features — Omni docs, knowi.com; Omni modeling layer and dbt sync — Omni documentation.
Ready when you are
See what a governed model looks like when agents, not dashboards, are the primary consumer.