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Prime Intellect

Prime Intellect

Train, deploy, and improve custom AI models on an integrated compute, training, inference, and sandbox stack.

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Recommended for

    • Pre-Seed
    • Seed
    • Series A
    • Scaleup

SSIE

Stage Fit Recommendations

Following data shows why Prime Intellect is recommended for listed teams. The Fit Score represents the percentage fit of Prime Intellect to each company stages, as calculated by the SSIE.

Stage

Fit Score

Fit Reasons

  • Pre-Seed

    $0–$10k MRR

    73
    • Train and deploy custom AI models
    • Use 365,000+ prebuilt reinforcement-learning tasks
    • Dedicated clusters include 24/7 engineer support
  • Seed

    $10k–$100k MRR

    72
    • Serve open models through an OpenAI-compatible API
    • Fine-tune models with SFT and reinforcement learning
  • Series A

    $100k–$500k MRR

    73
    • Role-based access control for compute workloads
    • Dedicated clusters include Slurm and Kubernetes
  • Scaleup

    $500k–$2M+ MRR

    87
    • Dedicated cluster capacity available by quote
    • Self-healing clusters replace failing nodes
    • Dedicated clusters provide single-tenant GPUs
    • Hosted evaluations benchmark model performance
    • Scale to tens of thousands of concurrent sandboxes
    • Run 1 to 256 GPUs by the hour
Prime Intellect

Description

Prime Intellect is a platform for training, deploying, and continuously improving custom AI models on an integrated compute, training, inference, and sandbox stack. Teams train specialist models by fine-tuning with supervised fine-tuning (SFT), then reinforcing them with reinforcement learning against rewards defined from real outcomes. Hosted training uses Prime-RL, an open-source asynchronous reinforcement learning framework, on managed compute and sandboxes. Users create hosted evaluations to benchmark model performance, and observability tools capture, view, and annotate every trace from evals and training runs. The platform scales thousands of microVM sandboxes with native Docker and full reinforcement learning support. Built on open source, the stack works with open models of any size, with status tracking, duration logs, and environment details for each run.

Submitted By

Added On

Nov 3, 2026

Status

Unclaimed

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Free. No credit card.
Reviewed by a human. SSIE Fit Scoring.

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