E2B is the best sandbox for AI agents: sub-second starts, sessions that persist between tool calls, and an SDK built for exactly this. Daytona is the closest rival, Modal wins when the workload needs GPUs, and Cloudflare Sandboxes are the cheapest option if you already run Workers.
Editorial judgement last reviewedAn agent that writes code needs somewhere to run it, and that somewhere cannot be your server. Model-written code is untrusted code, not because the model is malicious, but because prompt injection turns a document your user uploaded into an instruction your agent follows. A microVM you can throw away is the only sane boundary.
The requirements are specific enough that general-purpose compute doesn't fit. Sandboxes get created constantly and unpredictably, they need to boot in under a second because a user is waiting, they need to survive the twenty seconds while a model thinks about what to do next, and they need to be capped so an infinite loop costs you cents rather than a support call.
Everything below is drawn from our sandbox providers comparison, which measures cold start with a standard image and records isolation model, session persistence, timeout ceilings and price per hour across sixteen providers.
We ranked on four things, in this order.
Cold start with a real image. Not the marketing number, time from create call to first command producing output. You pay this on every new session, which for an agent product means every conversation.
Session persistence. Whether files, installed packages and working directory survive between tool calls, and whether a session can be paused and resumed rather than killed. This is the single biggest determinant of how much code you write around the platform.
Isolation. microVM-level separation, not container namespaces, plus the ability to restrict outbound network access and cap CPU, memory and wall-clock time.
Cost shape at agent-like usage, many short sessions with significant idle time in the middle, which prices very differently from a steady batch workload.
Built for this exact job and it shows. Sandboxes boot from a Firecracker snapshot in a fraction of a second, the SDK's vocabulary is filesystem and shell rather than deploy and invoke, and sessions persist across tool calls by default, the agent installs a package once and it's still there four steps later. Pause and resume turn a long agent session into something you can park instead of pay for.
Python and TypeScript SDKs both first-class, which matters because agent orchestration usually lives in Node even when the sandboxed code is Python. The infrastructure is open source, so self-hosting inside your own cloud is a documented path rather than a sales conversation.
The limitation is GPUs: available, but not the product's centre of gravity. If your agent needs to serve a model rather than run scripts, look at Modal.
Very fast starts and a strong developer-environment heritage, which means the workspace primitives, snapshots, declarative images, persistent volumes, are more developed than most of this roster. If your agent needs a workspace that looks like a real dev machine, with tooling preinstalled and state that survives, Daytona
handles it well.
Worth benchmarking head-to-head with E2B on your own image rather than trusting either headline number, because the ranking flips depending on how heavy your dependencies are. Where Daytona pulls ahead is workloads that want a warm, richly provisioned environment rather than a minimal one.
A different class of product that overlaps this category at the edges. Modal's sandbox primitive genuinely isolates untrusted code, but the platform is built around functions, deployment and batch compute, and around GPUs, where nothing else in this roster is close on availability, cold start or per-second billing.
Pick Modal when the sandboxed work is heavy: inference, fine-tuning, video processing, anything with a model in it. Expect to build the session-management layer that E2B gives you for free, and budget a week for it.
If you're already on Modal for compute, using its sandboxes for agent code is a reasonable way to avoid a second vendor.
Runs next to Workers, which means no cross-cloud hop between your agent orchestration and its sandbox, and pricing that's hard to beat if your traffic is already on Cloudflare's network. Free egress and proximity to R2
make it attractive for agents that read and write a lot of data.
The trade is a younger product with fewer of the ergonomics, check the session lifetime ceiling and the available runtimes against what your agent actually needs before committing. For agents doing short, bounded tasks inside an existing Cloudflare stack, it's the cheapest credible option.
Same argument as Cloudflare's, from the other platform. If your product already deploys to Vercel
, running agent code in Vercel Sandboxes keeps everything under one account, one bill and one set of credentials, with an ergonomic fit to the Next.js applications that dominate this space.
Check the execution time ceiling carefully, platform sandboxes tend to inherit their parent platform's function limits, and an agent that wants to run a ten-minute test suite will hit them.
Here is the shape of an agent session: create a sandbox, run a command, hand output to the model, wait twenty seconds while it thinks, run another command. Repeat eight times. Total wall-clock: four minutes. Total compute actually used: forty seconds.
You are billed for the four minutes on every platform that keeps the sandbox allocated, and idle time routinely exceeds active time by three to one. This, not the hourly rate, is what determines your bill.
Three mitigations, in order of effect. Pause sandboxes between steps if the platform supports it, this is the single biggest lever and it's why the pause and resume capability is a ranking criterion rather than a footnote. Set aggressive default timeouts, because agents crash and orphan sandboxes more often than you'd like, and an orphaned pool is the classic first surprise invoice. And close sessions in a finally block, not on the happy path.
Measure your real session duration before choosing on price. Most teams underestimate it by a factor of two or three, which means the cheapest hourly rate is frequently not the cheapest platform.
The failure everyone imagines is agent code deleting a filesystem, and a microVM handles that: the filesystem is disposable and nothing outside it is reachable.
The failure that actually happens is network egress. Agent code that can make arbitrary outbound requests can exfiltrate anything in its context, including credentials you passed in as environment variables, and including data from earlier in the conversation. Prompt injection makes this reachable from a document, a web page or an email your agent read.
So check, for whichever provider you choose: can outbound network access be denied by default, and can it be allowlisted per destination? Can secrets be scoped to a single sandbox rather than a workspace? Is there a hard wall-clock ceiling that kills a runaway process regardless of what it's doing?
Then do the obvious things. Never put a long-lived production credential in a sandbox environment. Give the agent a scoped, short-lived token instead. Treat every byte that comes out of a sandbox as untrusted input to whatever consumes it next.
The sandbox providers hub records isolation model, network controls and timeout ceilings per provider so you can filter on this rather than reading twelve documentation sites.
Container namespaces are a weaker boundary than a microVM, and kernel escapes are a real category of vulnerability. For code an LLM wrote, use hardware-level isolation. You'd also be building the sub-second start machinery yourself, which is not a weekend project.
For agents inside an existing Cloudflare or Vercel stack, the platform-native sandboxes generally win because there's no cross-cloud hop. Otherwise the cheapest platform is whichever one lets you pause idle sessions, since idle time usually exceeds active time.
Only if the agent's own code needs them, training, fine-tuning or serving a model. Calling a hosted LLM API from inside a sandbox needs no GPU at all, and that covers the great majority of agent workloads.
E2B publishes its infrastructure as open source, and the roster includes a self-hosted Firecracker baseline for teams comparing against building it themselves. Most of the hosted platforms are closed, so self-hosting is a real differentiator if data residency binds you.
