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Why Sandboxes

Prime Sandboxes are disposable Docker environments for AI-assisted coding, benchmarking, and quick experiments. They give agents and humans an isolated workspace without touching production infrastructure. Sandbox Dashboard
  • Prototype quickly – launch a clean runtime and iterate without worrying about teardown.
  • Benchmark safely – run untrusted code or model evaluations in a locked-down environment.
  • Run AI agents securely – disable network access to prevent code from reaching the internet.
  • Inject secrets safely – pass API keys, passwords, and tokens as encrypted secrets that never appear in logs or API responses.
  • Teach & demo – hand teammates a reproducible workspace that mirrors your setup.
  • Use custom images – push your own Docker images and use them in sandboxes with all your dependencies pre-installed.
  • Organize with labels – tag sandboxes by project, team, or environment for easy filtering and bulk cleanup.

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First Run

Make sure you have an API key with the right permissions on hand (prime login) before running these commands.
Track usage limits and billing inside the Prime Billing dashboard.

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Pricing

Sandboxes are billed while running:
  • CPU: $0.05 per core per hour
  • Memory: $0.01 per GB per hour
  • Disk: $0.001 per GB per hour
Example: 1 CPU core, 2 GB RAM, 10 GB disk → $0.08/hour.

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Limits

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Per-sandbox

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Per-account

Account limits apply across all running sandboxes. If you need higher limits, contact support.

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Container Images

Sandboxes support images from any Docker-compatible registry. When you pass an image reference to prime sandbox create, it resolves in this order:
  1. Fully-qualified references go to the registry you name — docker.io/pytorch/pytorch:latest, ghcr.io/org/image:tag, quay.io/org/image:tag.
  2. Prime Images — references that start with prime/, like prime/<username>/<image>:<tag>, prime/<team-slug>/<image>:<tag>, or the id forms (prime/<userId>/<image>:<tag>, prime/team-<teamId>/<image>:<tag>), resolve to a Prime Image you can access: your own, your team’s, or one that’s been published.
  3. Docker Hub — any other reference with no registry host and no prime/ prefix resolves as a Docker Hub image, so official images like ubuntu:22.04 or pytorch/pytorch:latest work as usual.
For private images, add registry credentials via the dashboard, then switch to Settings in the toggle. Then pass --registry-credentials-id when creating a sandbox. Use prime registry list to view saved credentials and prime registry check-image to verify an image is accessible.

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GPU Support

GPU-enabled sandboxes are on the roadmap. For now, sandboxes run on CPU-only machines and setting --gpu-count to a value greater than 0 will return an error. We’ll announce as soon as GPU tiers open up.

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Next Steps