OnCell vs E2B
Both OnCell and E2B provide sandboxed environments for AI agents. The difference is what happens after the code runs.
The core difference
E2B gives you ephemeral sandboxes. Your agent executes code, gets a result, and the sandbox disappears. If you need to persist files, database state, or search indexes between sessions, you manage that yourself — S3, Postgres, Pinecone, whatever.
OnCell gives you persistent sandboxes. Each user gets their own environment with built-in storage, database, and search that survives across sessions. Your agent reads files, queries the database, and searches content — all locally, all 0ms. When the user leaves, the environment pauses. When they come back, it resumes in 200ms with everything intact.
Feature comparison
| Feature | OnCell | E2B |
|---|---|---|
| Sandbox type | Persistent — survives across sessions | Ephemeral — destroyed after use |
| File storage | Built-in (local NVMe, 0ms) | In-sandbox only, lost on teardown |
| Database | Built-in (SQLite, 0ms) | Not included |
| Search / RAG | Built-in (full-text + vector) | Not included |
| Streaming (SSE) | Built-in (ctx.stream()) | Not included |
| Pause / resume | Auto-pause on idle, 200ms resume | No — sandbox destroyed |
| Per-user isolation | One environment per user | One sandbox per execution |
| State persistence | Automatic — files, DB, search | Manual — bring your own storage |
| Custom domains | Built-in with automatic SSL | Not available |
| Crash recovery | Journal-based, resumes from last step | Restart from scratch |
| Code execution | Shell, Node.js, Python | Shell, any language |
| Pricing | $0.10/hr active, $0.003/hr paused | $0.10/hr |
When to use E2B
E2B is the right choice when you need stateless code execution — run a script, get a result, throw away the sandbox. If your agent generates a chart, runs a data transformation, or executes untrusted code with no need to persist anything, E2B is simpler.
When to use OnCell
OnCell is the right choice when each user needs their own persistent environment. If your users have files, conversation history, search indexes, or database state that needs to survive across sessions, OnCell handles all of that without extra infrastructure.
Common use cases: AI coding agents, AI data analysis platforms, AI tutoring systems, customer support agents with knowledge bases, and any AI app where users accumulate state over time.
Try OnCell
One API call creates a sandboxed environment with storage, database, and search.