> ## Site Index
> Fetch the site index at: https://www.credibledata.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Credible vs. Power BI

> Power BI is the most widely deployed BI tool there is. Credible is an open analytics engine serving one governed model to agents, apps and dashboards.

## How Power BI describes itself

Power BI is Microsoft’s BI platform, now part of Fabric: semantic models in DAX, VertiPaq and Direct Lake for performance, Copilot for natural language, and governance through Fabric and Entra ID.

## Credible and Power BI (Microsoft) compared

| Dimension | Credible | Power BI |
| --- | --- | --- |
| Category | The AI Analytics Engine — you write down what data means, and it generates the stack under it | Microsoft BI, now part of Fabric, with semantic models in DAX |
| Who it is built for | The whole organization, not one central team — ops, finance and product; spreadsheet users through ML engineers | Microsoft-standardized enterprises, from analysts to finance teams |
| 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 | Its own UI — Power BI Desktop to author, the service to consume, with Copilot alongside |
| Modeling language | Malloy — a modern programming language for data: imports, inheritance, public and private members, and queries that compose into new sources | DAX and M — highly expressive, with an implicit evaluation-context model that takes years to master |
| 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 — DAX, M and the semantic model are Microsoft formats |
| 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 | Hand-authored in Power BI Desktop; Copilot answer quality depends on descriptions and synonyms you write |
| 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 | Row-level security in the model, plus Fabric and Entra ID permissions |
| 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 | In-memory VertiPaq import, DirectQuery, dual storage and aggregation tables; Direct Lake reads Parquet from OneLake without importing. Mature, and the main performance lever |
| 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 | List and inspect — browse workspaces, list datasets, fetch the model definition, then run DAX |
| 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 Power BI service, embeds, Fabric and the XMLA endpoint; strongest inside Microsoft |
| 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 Fabric capacity, and the semantic model is a Microsoft format |
| 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-user licences plus reserved Fabric capacity, with Copilot drawing on that same shared capacity |

## Where Credible takes a different approach

### DAX is powerful, and it is hard — for people and for agents

DAX is very expressive, and in experienced hands it does things simpler modeling formats cannot. The cost is its evaluation model: row context, filter context and the context transition that CALCULATE performs are the concepts Power BI teams spend years getting right, and a measure that reads correctly can still aggregate wrong. That difficulty lands on agents too — an agent writing DAX has to reason about the same implicit context a person does, and gets it subtly wrong in the same places. Malloy makes that structure explicit instead: aggregation is computed in the context the source and its joins define, so a measure means the same thing wherever it is used. And it is open source, served by the open-source Malloy Publisher, so the model is a file in your repository rather than a platform artifact.

### Where the AI sits in the architecture

Copilot brings natural language to a platform designed around reports, and it answers well when the semantic model carries good descriptions and synonyms — Microsoft documents that prep work carefully. Credible starts at the other end: capturing that meaning is the product rather than a prerequisite for it, and the result is served to any agent over MCP with the governance attached.

### Where the answer shows up

Power BI answers questions in Power BI, and for a company that lives in Teams and Excel that is close to where people already are. Credible is served over MCP into whatever agent your team uses, and into AI assistants, Slack, internal apps and customer-facing products, with results carrying an interactive UI resource so the client renders a real chart rather than narrating rows. Credible Workspaces, notebooks, reports, dashboards and data apps are all there if you want a UI from us, and each one reads the model rather than holding it.

### One model over Microsoft data and everything else

A Power BI semantic model is a Microsoft artifact, which is a feature when the estate is Microsoft — the identity, the storage and the governance are all one thing. Credible does not ask you to leave any of that: DuckDB reads Parquet directly out of Azure Data Lake, which we run in production, so a model can cover Microsoft-resident data and BigQuery, Snowflake, Postgres, MySQL, Databricks and Trino in the same breath. The performance answer travels with it: VertiPaq keeps a Power BI model fast by holding it in memory, and Credible does the same thing for a materialized source in its own serving layer — so speed does not depend on which estate the data happens to sit in.

Capability claims about Power BI were checked against public sources in August 2026. Sources: Fabric and Power BI capabilities — Microsoft documentation; Copilot requirements and semantic model preparation — Microsoft Learn; VertiPaq and Direct Lake performance — jamesserra.com, alphabold.com.