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# Mistral launches Forge, a platform for training custom AI models on enterprise data

**[Collections](https://daily.dev/sources/collections)** · 2 min read · 2 upvotes · 0 comments

## Summary

Mistral has launched Forge, an enterprise platform enabling companies to train AI models from scratch on their own proprietary data, announced at Nvidia GTC. Unlike fine-tuning or RAG approaches, Forge supports full pre-training and post-training pipelines, allowing models to genuinely internalize internal knowledge, codebases, and compliance policies. It supports dense and mixture-of-experts architectures, includes reinforcement learning for agentic use cases, and notably allows agents to manage their own training jobs via natural language. Early adopters include ASML, Ericsson, the European Space Agency, and several government agencies. Mistral positions Forge against OpenAI and Anthropic in the enterprise market, emphasizing data sovereignty and domain-specific model quality, while reporting it is on track to exceed $1B ARR this year.

## Content

Mistral has announced Forge, a platform that lets enterprises train AI models from scratch on their own proprietary data. The announcement came at Nvidia GTC.

## What Forge actually does

Most enterprise AI tools today rely on fine-tuning existing public models or bolting on retrieval-augmented generation (RAG). Forge takes a different approach: companies can run full pre-training and post-training pipelines on their own data, meaning the resulting model genuinely internalizes internal knowledge, codebases, compliance policies, and workflows rather than just referencing them at query time.

The platform supports both dense and mixture-of-experts architectures, and includes reinforcement learning pipelines aimed at agentic use cases. One notable design choice is that agents can manage their own training jobs using plain English instructions, which is either very convenient or slightly unsettling depending on your perspective.

For non-English and highly domain-specific data, Mistral argues full training outperforms fine-tuning, which tends to struggle when the base model has little prior exposure to the relevant language or terminology.

## Who's already using it

Early partners include ASML, Ericsson, the European Space Agency, DSO National Laboratories Singapore, Home Team Science and Technology Agency (HTX) Singapore, and Reply. The target customer list reads like a who's-who of organizations with data they really don't want leaving their own infrastructure: governments, financial institutions, manufacturers, and large tech companies.

Mistral also provides forward-deployed engineers to help customers build data pipelines and run evaluations, which suggests this isn't a self-serve product yet.

## The competitive angle

Mistral is positioning Forge directly against OpenAI and Anthropic in the enterprise market. The pitch is reduced dependency on third-party providers and models that actually reflect how a specific organization operates, rather than how the internet at large does. The company says it's on track to exceed $1 billion in annual recurring revenue this year.

## Similar posts on daily.dev

- [Mistral AI inks a deal with global consulting giant Accenture](https://daily.dev/posts/mistral-ai-inks-a-deal-with-global-consulting-giant-accenture-mmwunwjdw) · TechCrunch · 1 upvotes · 0 comments

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