Prime Intellect is an open stack for training, evaluating, deploying, and improving AI models and agents. It combines RL environments, hosted evaluations and training, model inference, GPU compute, and isolated sandboxes for research and production workflows.
Prime Intellect is an AI infrastructure and research platform for teams building, evaluating, and improving models and agents. Its stack combines reinforcement-learning environments, hosted evaluations, managed training, inference, GPU compute, and isolated microVM sandboxes.
The core workflow connects environment development, evaluation, training, and deployment. Users can create and manage RL environments through the Prime CLI, benchmark models on hosted infrastructure, train models in managed workflows, and serve models through Prime-hosted infrastructure or a common inference gateway. The platform also provides access to community environments and compute resources for experimentation and larger-scale workloads.
The Prime CLI supports an init, develop, eval, and push workflow for turning tasks into RL environments. Prime Intellect’s open-source Verifiers library provides modular components for creating environments and training language-model agents.
Run model evaluations without managing the evaluation infrastructure. The service supports benchmarking across open-source models and provides public leaderboard functionality for comparing results.
Train large-scale models on RL environments through managed workflows with visibility and control over experiments. Prime Intellect also describes hands-on support from its applied research team.
Run Prime-hosted models, including GLM-5.3, on Prime infrastructure or access models from third-party providers through the Prime Inference Gateway. Both routes use one OpenAI-compatible API.
Use on-demand GPU capacity from 1 to 256 GPUs across clouds, with SLURM and Kubernetes orchestration, InfiniBand networking, and Grafana monitoring. Larger reserved clusters can be requested through quotes from more than 50 providers.
Run workloads in Linux microVMs with Docker Compose, background jobs, kernel-dependent workloads, and bring-your-own Docker images. Sandboxes support parallel rollouts and integrate with Verifiers and Prime-RL.
Research and engineering teams can encode tools, tasks, datasets, and reward logic as RL environments, run baseline evaluations, inspect results, and push revisions through the Prime CLI.
Teams can use the same environment for evaluation and RL training. Zapier used this workflow for AutomationBench, examining rollout traces and live metrics to detect reward hacking before continuing training.
Product teams can train focused agents for narrow workflows instead of relying only on general-purpose models. Ramp used a custom spreadsheet environment to train FastAsk for financial workbook retrieval.
Researchers can combine managed training, GPU capacity, and isolated microVM sandboxes for parallel rollouts and workloads that require Docker, background processes, or full Linux environments.
Teams can evaluate models on their own tasks, access Prime-hosted or third-party models through the OpenAI-compatible gateway, and request dedicated serving capacity when their latency, reliability, or custom-model requirements call for it.
The platform supports RL environment development, hosted model evaluations, managed training, model inference, GPU compute, and sandboxed agent workloads. The documented workflows focus on training and improving models and agents.
Yes. Prime Intellect’s case study materials describe using the same environment for baseline evaluation, reward debugging, and RL training. This was the workflow used by Zapier for AutomationBench.
Prime Sandboxes are isolated Linux microVM environments for agentic training. They support Docker Compose, background jobs, kernel-dependent workloads, parallel rollouts, and Docker images supplied by the user.
Prime-hosted models run on Prime infrastructure, while the Prime Inference Gateway provides access to models served by third-party providers. The site describes one OpenAI-compatible API for both routes.
The Sandboxes page lists usage-based rates of $0.02 per vCPU-hour, $0.0125 per GiB of memory-hour, and $0.0002 per GiB of disk-hour. The page states that these prices are valid through December 22; broader platform pricing was not available on the supplied pricing page.
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