Prime Intellect flipped Lab from beta to GA on May 7. It’s a full-stack platform for training self-improving agents — define a task, write a harness, evaluate, run RL on the reward signal, inspect rollouts, deploy a LoRA adapter, serve inference. All inside one product. Nobody else has commercialized this loop end-to-end; until now you stitched it together with rented H100s and a lot of YAML.
What you actually get
Six modules ship together: Hosted Training, Hosted Evaluations, Environments Hub, Adapter Deployments, Prime Inference, and Prime Sandboxes. Day one, Hosted Training covers 14 models from NVIDIA, OpenAI, Meta, and Qwen — 1B to 70B, dense and MoE, reasoning and non-reasoning modes. During beta, hundreds of teams ran 10,000+ training jobs across math, code, browser agents, games, customer support, and long-horizon enterprise workflows.
The API and how it bills
Prime Inference is OpenAI-compatible — swapping a trained LoRA adapter into your stack is a one-line endpoint change. Pricing is per-token, not per cluster-hour, so you only pay for tokens that actually move the model. That reframes RL economics: small teams can finally afford to iterate, and the “everyone gets their own frontier AI lab” pitch finally has the infrastructure to back it.
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