Comparison: Tangle vs e2b, Daytona, Modal
Honest comparison. We use these tools too; we describe what each platform actually does today, not strawmen of last year's state.
At a glance
| Capability | Tangle | e2b | Daytona | Modal |
|---|---|---|---|---|
| Code exec with structured results (image / dataframe / json / html) | ✓ | ✓ | partial | ✓ |
| Persistent kernel per session (variables across calls) | ✓ | ✓ | ✓ | ✓ |
| Multi-language (Python, JS, TS, Bash) | ✓ | ✓ | ✓ | Python-first |
| Shell exec, fs., git. on the same sandbox | ✓ | ✓ | ✓ | partial |
| Snapshot + restore | ✓ | ✓ | ✓ | ✓ |
| Public template gallery | ✓ | ✓ | ✓ | ✓ |
| Fleets (coordinator + N workers, shared workspace) | ✓ | : | : | partial (Function fan-out) |
Durable session replay (Last-Event-ID, browser-reload-safe) | ✓ | : | : | : |
| Browser-safe WebSocket gateway | ✓ | partial | : | : |
| Hosted MCP endpoint (paste-into-Claude) | ✓ | : | : | : |
| Local MCP server bridge (stdio for Cursor / Zed) | ✓ | : | : | : |
| OAuth / API integration runtime (Activepieces-style catalog) | ✓ | : | : | : |
Per-call dedup on runCode via idempotencyKey (in-flight collapse + cached result within 15 min) | ✓ | : | : | : |
Exactly-once dedup on outer agent loop (dispatchPrompt({sessionId, turnId})) | ✓ | : | : | : |
| TEE attestation, sealed-secret encryption | ✓ | : | : | : |
| Framework tool packs (Anthropic / OpenAI / Vercel AI / Mastra) | ✓ | partial | : | : |
The above is maintained against what each platform's docs say today. If you find something inaccurate, open an issue: we'd rather correct the table than win a row dishonestly.
When to pick what
- You want the smallest surface, only Python data science: any of the four work. e2b's Python notebook UX is the most polished.
- You're building an agent product across many models / frameworks: Tangle's tool packs and MCP surfaces remove the per-framework glue code.
- You're running large parallel campaigns (hundreds of experiments): Tangle's fleet primitive is built for this; Modal's Function fan-out is comparable for stateless workloads.
- You're deploying agents that need OAuth-bound external actions (GitHub, Slack, Stripe, etc.): Tangle's integrations runtime is the only one of the four that ships this as a first-class primitive.
- You're deploying into TEE / confidential compute: Tangle is the only one with platform-level attestation today.
- You're long-running and need browser reload to not lose progress: Tangle's durable session gateway is the differentiator.
What we don't (yet) ship
- A polished notebook UI in the dashboard. The web app has a terminal and an agent view; "click cells, see plots inline" is a v2 surface.
runCodealready returns the right structured results, so a notebook layer is purely client-side work. - True Jupyter kernels. Our custom kernel shim speaks the same display protocol (matplotlib base64 PNG, pandas frames, JSON via
display()), so the result-type contract matches Jupyter. We'll swap to first-class IPyKernel under the hood when the operational complexity earns its keep. - Per-call cost metering on
runCode. Costs are tracked per sandbox-second today. A breakdown perrunCodecall is on the roadmap once enough customers report needing it.
Migrating from e2b
The mental model is the same: spawn a sandbox, run code in a kernel, get typed results. The headline mapping:
| e2b | Tangle |
|---|---|
Sandbox.create(...) | client.create({ publicTemplateId: "python-data-science" }) |
sandbox.runCode(code) | box.runCode("python", code, { sessionId }) |
result.results[0].png | result.results.find(r => r.type === "image").data |
sandbox.commands.run("...") | box.exec("...") |
sandbox.filesystem.write/read | box.write(path, content) / box.read(path) |
Tool packs and MCP bridges are additive on top of this surface; you don't have to use them to use the sandbox.
Migrating from Modal
Modal is excellent for stateless GPU functions and batch jobs. If your workload is "fire-and-forget a Python function, get a result" Modal is the right tool. If it's "an agent needs an interactive sandbox with files, git, browser, and persistent kernel state across many tool calls," that's where Tangle's surface shines.
For long-running parallel campaigns, both work: Tangle gives you a coordinator and shared workspace; Modal gives you per-call container reuse and very fast cold start. Pick by what you need most: shared state (Tangle) or function granularity (Modal).