Credible vs. Looker

Looker is the governed enterprise BI standard, and since Next ’26 it serves its model to agents over a managed MCP server. Credible starts from the other end: the model is the product and BI is one surface on it — open, built by a coding agent, and served into whatever agent you already use.

How Looker describes itself

What Looker is

Looker is the governed enterprise BI standard, now positioned as agentic BI: Gemini grounds conversational answers and BI agents in the LookML semantic model, with a managed MCP server and native reads of BigQuery and Snowflake semantic definitions.

Category

Credible: AI-native analytics engine with an integrated data stack
Looker: Enterprise BI suite with a governed LookML semantic layer

Who it is built for

Credible: AI product and analytics teams, plus anyone who works with data — spreadsheet users through ML engineers
Looker: A central BI or analytics org serving the wider enterprise

Primary interface

Credible: 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
Looker: Its own UI — dashboards and Explores to consume, LookML authored in-app, with Gemini chat alongside

Modeling language

Credible: Malloy — a modeling and query language with imports, inheritance, and public and private members
Looker: LookML — a declarative modeling format, paired with Looker’s own query generation

What is open

Credible: 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
Looker: Vendor-owned — LookML and the platform are Google Cloud products, and Looker now reads BigQuery and Snowflake semantic definitions

How the model gets built

Credible: A coding agent with open-source MCP tools and agent skills, capturing context from where it already lives
Looker: Hand-authored LookML, maintained by data engineers

Governance

Credible: Governance as code. Access rules are annotations in the model itself — versioned, reviewed and composable like any other code
Looker: access_filter and access_grants, declared in LookML and keyed to user attributes managed in Looker

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 in-memory serving layer, which is how a team on Postgres gets fast serving without buying a warehouse to get it
Looker: Persistent derived tables, materialized as real warehouse tables on a TTL or trigger, plus aggregate awareness. Mature and warehouse-native

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
Looker: A walk through the model: get_models, then get_explores for a model, then get_dimensions and get_measures for an Explore, per Google’s own guide

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
Looker: The Looker UI, embeds and its API, plus a managed MCP server; also reads BigQuery and Snowflake semantic definitions

Scale and portability

Credible: 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
Looker: Runs in a Looker instance, with gravity toward BigQuery

What you pay for

Credible: 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
Looker: A platform fee plus per-user licences by role, with token overage announced for its AI features from late 2026

	Credible	Looker
Category	AI-native analytics engine with an integrated data stack	Enterprise BI suite with a governed LookML semantic layer
Who it is built for	AI product and analytics teams, plus anyone who works with data — spreadsheet users through ML engineers	A central BI or analytics org serving the wider enterprise
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 — dashboards and Explores to consume, LookML authored in-app, with Gemini chat alongside
Modeling language	Malloy — a modeling and query language with imports, inheritance, and public and private members	LookML — a declarative modeling format, paired with Looker’s own query generation
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 — LookML and the platform are Google Cloud products, and Looker now reads BigQuery and Snowflake semantic definitions
How the model gets built	A coding agent with open-source MCP tools and agent skills, capturing context from where it already lives	Hand-authored LookML, maintained by data engineers
Governance	Governance as code. Access rules are annotations in the model itself — versioned, reviewed and composable like any other code	access_filter and access_grants, declared in LookML and keyed to user attributes managed in Looker
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	Persistent derived tables, materialized as real warehouse tables on a TTL or trigger, plus aggregate awareness. Mature and warehouse-native
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	A walk through the model: get_models, then get_explores for a model, then get_dimensions and get_measures for an Explore, per Google’s own guide
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 Looker UI, embeds and its API, plus a managed MCP server; also reads BigQuery and Snowflake semantic definitions
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	Runs in a Looker instance, with gravity toward BigQuery
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	A platform fee plus per-user licences by role, with token overage announced for its AI features from late 2026

A different premise

Where Credible takes a different approach

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

Two ways for an agent to find a field

Looker’s MCP tools mirror its API, which is a coherent design: an agent calls get_models, then get_explores for a model, then get_dimensions and get_measures for an Explore. Google’s own guide describes that sequence, and it gives the agent a real map of the instance. Credible searches an index of the model instead — typed search targets come back as a ranked set of sources, dimensions and measures, so the agent asks for the handful of fields a question needs. The trade is coverage against round trips, and which one suits you depends mostly on how large the model is.

Who builds the model

LookML is authored and maintained by data engineers, and Looker is built to make that craft good — the review workflow, the IDE and the validator all assume a team that owns the model. Credible assumes you already have a coding agent and points it at the work: it builds and maintains the model from documentation, query history and knowledge stores already in your stack. The parts that make an agent good at this — the MCP retrieval tools and the agent skills — are open source in Malloy Publisher and need no Credible account, so they run inside whatever agent your team already uses. Same model at the end; a different answer to who does the work, and to who owns the expertise that makes them good at it.

A language alongside the modeling format

LookML made the model explicit and centrally governed, and a great deal of what this category now takes for granted starts there. It is deliberately declarative, which is what lets a team review a change before it ships — and the cost of being that explicit is volume. Definitions get restated, and a project that has run for a few years tends to carry LookML nobody is certain is still in use. Malloy is aiming at something adjacent: a language with imports, inheritance, and public and private members, so a definition is written once and reused rather than repeated. That composition is what keeps a large model comprehensible to people and safely extensible by an agent, which is the case we are optimizing for.

Materializing as one annotation

A persistent derived table means a derived_table block carrying its own SQL, a persist_for TTL or a datagroup_trigger, and usually a datagroup alongside — a second description of logic the model already holds, kept in step by hand. Credible materializes a source with a single annotation on that source: one definition, with its persistence sitting beside the logic it persists. A PDT is always a table in your warehouse; a Credible source can be that, or it can be held hot in our in-memory serving layer, decided per source. Same intent either way — the difference is how much you write to get there, and whether the warehouse has to be the thing that makes it fast.

Governance in the language itself

access_filter, access_grants, persistent derived tables and value_format are each their own mechanism, configured alongside the model — which is what lets Looker admins reason about them separately. Malloy folds annotations, access rules and materialization into the language, so they are versioned and reviewed in the same diff as the logic they protect.

Open source, and portable across warehouses

Looker reads Snowflake and BigQuery semantic definitions now, which meaningfully widens where it reaches, and its LookML model is the asset a Looker investment is built around. Credible is open core: Malloy is open source, we maintain Malloy Publisher, and the model runs across BigQuery, Snowflake, Postgres, MySQL, Trino, Presto and DuckDB. That is the bet we made — that the model should be a file you own, in a format anyone can read.

Other comparisons

Compare Credible with the rest

Capability claims about Looker were checked against public sources in August 2026. Products in this category change quickly — confirm anything decision-critical with the vendor. Sources: Looker MCP server and agentic BI — Google Cloud blog, Next ’26; Looker semantic layer and PDTs — Google Cloud documentation; Conversational analytics grounding in LookML — Google Cloud.

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.