What Sets Them Apart
The persistence model is the core distinction. E2B treats each sandbox as disposable — create, run code, extract results, destroy. Ideal for agents executing code snippets or processing data where no state survives between executions. Daytona treats environments as persistent workspaces where packages, files, and configuration survive across sessions, suitable for iterative development workflows.
E2B and Daytona at a Glance
Daytona's statefulness suits AI coding assistants working on projects over multiple interactions. Installed dependencies, configuration files, and accumulated codebases persist between sessions. The next time an agent connects, everything is intact. This is valuable for iterative development where context builds over time rather than isolated execution tasks.
E2B's ephemeral model provides stronger security. Destroyed sandboxes leave no residual state that could leak between sessions. Each execution starts from a clean template. For untrusted or user-submitted code, this clean-slate approach eliminates security concerns that persistent environments must manage explicitly.
Startup performance differs by architecture. E2B Firecracker microVMs boot in under 200 milliseconds with hardware isolation. Daytona Docker containers start in sub-90 milliseconds with container isolation. Both are fast enough for interactive workflows, but Daytona's containers provide slightly faster cold starts at the cost of weaker isolation boundaries.
Git Integration, Cost, and Lifecycle
Git integration is native to Daytona. Workspaces create from Git repositories maintaining full history, branches, and commit workflows. E2B can be configured with Git but it is not a core feature. For agents working within Git workflows creating branches and committing changes, Daytona provides a more natural environment.
Cost aligns with use cases. E2B charges per second of runtime with per-session overhead. Daytona prices by workspace hours with longer-lived sessions. Short frequent executions favor E2B's per-second billing. Long development sessions where an agent works for hours may favor Daytona's workspace model.
E2B's Desktop sandbox is a unique capability Daytona lacks. The graphical Linux environment enables visual application interaction and web browsing. Daytona focuses on headless development. For computer use scenarios, E2B is the only viable option.
IDE Support and Team Workflow
SDK maturity and framework integrations favor E2B with Python and JavaScript SDKs, LangChain integration, MCP server support, and comprehensive documentation. Daytona provides CLI and API but the SDK ecosystem for AI agent integration is less developed.
Self-hosting accessibility differs. Daytona self-hosts via Docker with configuration for various providers. E2B's self-hosting is restricted primarily to enterprise BYOC agreements. For teams needing sandbox infrastructure on their own servers without enterprise contracts, Daytona is more accessible.
The Bottom Line
E2B is better for agents executing isolated code where each execution starts clean. Daytona is better for AI coding assistants needing persistent environments where context accumulates. The persistence model is the decision axis — ephemeral for execution, stateful for development.