Strength: Open-source transparency, self-hosted option, GPU support, and the broadest feature set (Git, LSP, Docker-in-Docker). Sub-90ms cold starts on managed cloud.
Weakness: The breadth of features means a steeper learning curve compared to E2B's focused SDK. The open-source version requires infrastructure expertise to run.
Best for: Teams that need self-hosted sandboxes, GPU access, or full development environment capabilities inside the sandbox. Also strong for compliance-sensitive workloads.
Pricing: 200ドル in free compute. Usage at 0ドル.0504/vCPU-hour + 0ドル.0162/GiB-hour (effectively ~0ドル.083/hour for 1 vCPU + 2GB).
Modal -- Best for GPU and ML Workloads
Modal is a Python-first serverless platform where sandboxes exist alongside a broader ML infrastructure stack. If your agent needs to execute code that involves GPU inference, model fine-tuning, or heavy data processing, Modal is the only option here that handles all of it natively.
It scales to 20,000 concurrent containers with sub-second cold starts and uses gVisor for isolation. Companies like Lovable and Quora run millions of executions through it. The tradeoff is the SDK model -- environments are defined through Modal's Python library rather than arbitrary container images.
Strength: Unmatched GPU support alongside sandboxing. If your coding agent generates ML code, Modal lets it run end-to-end without leaving the platform.
Weakness: Python-first means TypeScript is beta-only. gVisor isolation is lighter than Firecracker microVMs -- sufficient for trusted code, but not as strong for fully untrusted execution. No self-hosting or BYOC option.
Best for: Python-heavy coding agents running alongside ML workloads, data analysis pipelines, and teams already invested in the Modal ecosystem.
Pricing: Usage-based, billed per second. CPU from ~0ドル.119/vCPU-hour. GPU billed separately. No upfront commitment.
Fly.io Sprites -- Best for Persistent Sessions
Fly.io Sprites runs on Firecracker microVMs with a killer feature: 100GB persistent NVMe storage per sandbox and checkpoint/restore in around 300ms. The idle billing model stops charging when the environment is not in use, making it cost-effective for coding agents that need a warm environment between sessions.
This is the closest thing to giving your agent a persistent development machine. It can write files, install dependencies, checkpoint its state, and resume exactly where it left off.
Strength: Persistent state with 100GB NVMe, checkpoint/restore, and idle billing. The best option for agents that maintain long-running projects across multiple sessions.
Weakness: Cold starts of 1-12 seconds are the slowest on this list. No GPU support. No BYOC option. Still early-stage compared to E2B and Modal.
Best for: Long-running coding agent sessions, Claude Code-style persistent development environments, and teams building agents that work on multi-day projects.
Pricing: Pay-per-use based on CPU, memory, and storage. Idle sandboxes do not incur compute charges.
Blaxel -- Best for Ultra-Fast Cold Starts
Blaxel is the newest entrant on this list, but it leads on one critical metric: 25ms standby resume time. For applications where latency between agent requests matters -- interactive coding assistants, real-time code evaluation, or high-throughput eval pipelines -- those milliseconds add up.
Blaxel uses microVM isolation and supports both Python and TypeScript SDKs. Sessions run indefinitely with snapshot support for saving and restoring environment state.
Strength: The fastest cold start of any sandbox on this list at ~25ms. Unlimited session length. Snapshot support for stateful workflows.
Weakness: Newer platform with a smaller community and fewer case studies than E2B or Modal. No GPU support. No self-hosting option.
Best for: Latency-sensitive agent applications, high-throughput evaluation pipelines, and teams that need interactive-speed code execution.
Pricing: 200ドル in free credits. Usage at ~0ドル.083/vCPU-hour (comparable to E2B and Daytona).
How to Choose
The decision tree is simpler than it looks:
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Need GPU for ML workloads? Modal is the only real option.
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Need self-hosted or open-source? Daytona. Nothing else comes close.
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Need the fastest integration with existing AI frameworks? E2B has the best ecosystem.
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Need persistent state across sessions? Fly.io Sprites with 100GB NVMe.
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Need the lowest latency? Blaxel at 25ms resume.
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Budget-conscious? E2B (100ドル credits) and Daytona/Blaxel (200ドル credits) all have generous free tiers.
The Verdict
There is no single winner here -- the right sandbox depends entirely on what your agent does and where it runs. E2B is the safest default for most teams starting today: the SDK is mature, the integrations are broad, and 150ms cold starts are fast enough for almost everything. But if your requirements skew toward GPU, self-hosting, persistence, or ultra-low latency, one of the other four will serve you better.
The one thing all five agree on: if your coding agent runs in an unsandboxed environment, you are one hallucination away from a production incident. Pick one and ship.