Best sandboxes for reinforcement learning and agent evals
For RL training and evals, our top picks are Modal Sandboxes, Morph Cloud and E2B. We weight concurrency, snapshot/fork speed, reproducibility and GPU access.
What RL training and evals need from a sandbox
Training or evaluating agents on real tasks means running thousands of identical environments, resetting them constantly and comparing outcomes:
- Concurrency in the thousands to hundreds of thousands.
- Snapshots and forking to branch many rollouts from one prepared state.
- Fast reset and per-second billing so short episodes are cheap.
- Reproducibility — identical images and deterministic setup.
- Sometimes GPUs alongside the environment.
Best sandboxes for reinforcement learning and agent evals, ranked
Modal Sandboxes
Sandboxes on Modal’s serverless compute platform for running untrusted code at very high scale, with GPUs, snapshots and tunnels.
- gVisor or VM
- GPU
- Pause/resume
Why it’s here: Scheduler tested with over a million concurrent sandboxes, memory and filesystem snapshots, and GPUs in-sandbox — the most complete scale story.
Morph Cloud
VM-based cloud computers for AI agents built on “Infinibranch” snapshot, branch and restore, plus persistent Devboxes.
- VM
- Pause/resume
Why it’s here: Snapshot-and-branch infrastructure designed for running many parallel agent attempts from the same state.
E2B
Open-source cloud sandboxes for AI agents, each a Firecracker microVM, with pause/resume, snapshots and forking.
- Firecracker microVM
- Open source
- Self-hostable
- BYOC
- Pause/resume
Why it’s here: Snapshots of running sandboxes with forking into many copies, microVM isolation and concurrency add-ons up to 1,100 on Pro (more on Enterprise).
Daytona
Stateful sandboxes for AI-generated code — Linux containers, Linux and Windows VMs, macOS and GPU sandboxes — with snapshots, volumes and preview URLs.
- Containers or VMs
- BYOC
- GPU
- Pause/resume
Why it’s here: Fast creation, snapshots and fork on VM sandboxes, plus GPU sandboxes up to 8 GPUs.
Freestyle
Full Linux VMs for AI agents with live forking of running VMs, memory-preserving hibernation, VPCs and nested virtualization.
- VM
- Pause/resume
Why it’s here: Live forking of running VMs including memory, for branching rollouts from a warm state.
Kubernetes Agent Sandbox
Open-source Kubernetes controller and CRDs for isolated, stateful sandbox pods for AI agents, with first-class support on GKE.
- gVisor or Kata
- Open source
- Self-hostable
- BYOC
- GPU
- Pause/resume
Why it’s here: For labs with their own clusters: warm pools and Pod snapshots on Kubernetes, with GPUs and TPUs on GKE.
Side by side
| Sandbox | Isolation | Open source | Self-host / BYOC | Cold start (vendor) | Max session | Snapshots / pause | GPU | SDKs |
|---|---|---|---|---|---|---|---|---|
| Modal Sandboxes | gVisor or VM | No | No / Not documented | <1 s | Up to 24 h | Yes / Yes | Yes | Python, JavaScript/TypeScript, Go |
| Morph Cloud | VM | No | No / Not documented | <250 ms (restore) | Configurable | Yes / Yes | Not documented | Python, TypeScript |
| E2B | Firecracker microVM | Yes | Yes / Yes | Not documented | 1 h / 24 h (Pro) | Yes / Yes | No | Python, JavaScript/TypeScript |
| Daytona | Containers or VMs | No | Not documented / Yes | <90 ms | Unlimited | Yes / Yes | Yes | Python, TypeScript, Ruby, Go, Java |
| Freestyle | VM | No | No / Not documented | 65 ms | Unlimited (paid) | Yes / Yes | Not documented | TypeScript |
| Kubernetes Agent Sandbox | gVisor or Kata | Yes | Yes / Yes | <1 s (warm pool) | Not documented | Yes / Yes | Yes | Kubernetes CRDs, Python, kubectl |
Frequently asked questions
Which sandbox scales best for RL rollouts?
Cite this page
Agent Sandboxes. (2026). Best sandboxes for reinforcement learning and agent evals. agentsandboxes.org. Last reviewed November 8, 2026. https://agentsandboxes.org/best/rl-and-evals/
Found an error or a missing sandbox? Submit a correction — every entry links to the sources it relies on.