Daytona vs E2B
Detailed side-by-side comparison to help you choose the right tool
Daytona
🔴DeveloperAI Infrastructure & Training
Open-source sandbox infrastructure for running AI-generated code safely. Sub-90ms startup, per-second billing, and stateful environments for AI agents and code interpreters.
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Starting Price
$0.0504/hr per vCPUE2B
🔴DeveloperAI Infrastructure & Sandboxes
Secure cloud sandboxes that let AI agents run untrusted code, install packages, and execute long-running tasks in isolated micro-VMs.
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Starting Price
FreeFeature Comparison
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Daytona - Pros & Cons
Pros
- ✓Sub-90ms sandbox startup is the fastest in the AI code execution space
- ✓Per-second billing means you pay only for actual compute time, not rounded-up minutes
- ✓$200 in free credits is generous enough to build and test a full agent workflow before spending anything
- ✓Stateful environments save time on multi-step agent tasks that need package installation and file persistence
- ✓Open-source core lets you self-host for full control over data and costs
- ✓MCP server support simplifies integration with modern AI agent frameworks
Cons
- ✗GPU pricing ($0.014/second = ~$50/hour) gets expensive fast for sustained ML workloads
- ✗Newer platform than E2B with a smaller ecosystem of examples and community resources
- ✗Enterprise and on-premise features require sales engagement with no public pricing
- ✗Documentation is functional but thinner than established competitors
- ✗No built-in file upload/download API comparable to E2B's convenience features
E2B - Pros & Cons
Pros
- ✓Strong isolation via Firecracker — safe enough for fully LLM-generated code
- ✓150ms cold-start is fast enough for interactive chat-style agents
- ✓Drop-in ChatGPT-style Code Interpreter SDK with persistent Jupyter kernels
- ✓Desktop sandbox makes browser-use and computer-use agents practical
- ✓Production-proven (Perplexity, Hugging Face) and well-instrumented
Cons
- ✗Per-hour pricing can balloon for long-running autonomous agents
- ✗Pro tier only includes 20 hours/mo — most teams burn through it
- ✗Smaller per-sandbox resource limits than running on your own GPU box
- ✗No GPU access on standard sandboxes (use Modal or RunPod for that)
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