E2B vs Modal

These two get compared constantly because both let an LLM run code that isn't yours, but they were designed for different halves of that problem.

E2B is built around the sandbox as a session. You create one, it boots in a fraction of a second, and then you interact with it: write files, run shell commands, execute a Python cell, read stdout, install a package, take the result, run something else. The abstraction is a machine that stays alive while your agent thinks. That is exactly the shape of a code interpreter, a data-analysis agent, or an autonomous coding loop.

Modal is built around the function as the unit. You decorate a Python function, declare its image and resources, and Modal runs it in the cloud with fast container starts, autoscaling, and genuinely good GPU access. The abstraction is remote execution of your code, not a machine your agent lives in. Modal does expose a sandbox primitive for untrusted code, and it works, but the platform's centre of gravity is deployment and batch compute.

If your product is an agent that writes and runs code, start with E2B. The SDK matches the mental model, session persistence is the default rather than a workaround, and you spend no time thinking about images.

If your product runs your own workloads — inference, fine-tuning, video processing, scheduled ETL, a queue of GPU jobs — Modal is not just the better fit, it is a different tier of product. Nothing in the sandbox category competes with Modal on GPU ergonomics.

Plenty of teams end up with both, and that is a reasonable outcome rather than a failure to decide.

An agent loop looks like this: the model asks to run a command, you run it, you hand back the output, the model asks for the next thing. Between those steps, seconds or minutes pass while the model thinks and while a human reads.

E2B's sandbox is designed for that gap. The sandbox stays up for a configurable timeout, you hold a handle to it, and the next command lands in the same filesystem with the same installed packages and the same working directory. The agent installs pandas once. It writes a file and reads it back three steps later. You can also pause and resume sandboxes, which turns a long-running agent session into something you can park rather than something you must keep paying for.

Modal's sandboxes can be kept alive too, and the primitive is real. But the platform's grain runs toward stateless functions with an input and an output, and the tooling — the dashboard, the deploy flow, the docs — is oriented that way. If you build an agent loop on Modal you will spend the first week building the session-management layer that E2B hands you on day one.

The counter-case: if each of your agent's steps is genuinely independent and heavy — render this video, run this model over this batch — statelessness is correct and Modal's execution model is the better one.

Both vendors advertise fast starts, and both are telling the truth about different things.

E2B boots microVMs from a snapshot, targeting sub-second creation of a fresh sandbox with a standard image. That number matters because you pay it on every new agent session — potentially every conversation, every user, every retry.

Modal's fast cold starts are about getting your container running, including your dependencies. When the image is warm and the layer cache is hot, it is genuinely quick. When you've just changed a dependency and the image rebuilds, you are waiting a lot longer, and a large ML image with model weights is a different conversation entirely.

The measurement that actually predicts your experience is time from "I need compute" to "my first command produced output", with your real image and your real dependencies. A sub-second boot into an image that lacks the three packages your agent always installs is not sub-second in practice — it's sub-second plus a pip install. Our sandbox providers table measures cold start with a standard image so the numbers are comparable, but you should re-run it with yours before committing.

If an LLM wrote the code, treat it as untrusted. That is true even when the model is well-behaved, because prompt injection turns a document your user uploaded into an instruction your agent follows.

Both platforms isolate at the microVM level rather than relying on container boundaries alone, which is the right posture. What you should check for your own threat model is the same short list either way: whether outbound network access can be restricted or denied, whether filesystem access is confined to the sandbox, what the ceiling is on CPU, memory and wall-clock time, and whether one tenant's noisy workload can affect yours.

E2B's default posture assumes the code is hostile, because that is its entire use case. Modal's default posture assumes the code is yours, because usually it is — its sandbox primitive exists precisely for the case where it isn't, and you should use that primitive rather than a plain function if you're executing model-written code.

Either way, the controls that matter most in practice are network egress restrictions and hard timeouts. Wire those up before you ship, not after your first surprise bill from an agent that wrote an infinite loop.

Both bill for compute time by resource size, roughly per second. The difference is what you're paying for while nothing is happening.

On E2B, an idle-but-alive sandbox is still allocated, so a long agent session with lots of model thinking time costs you the gaps as well as the work. Pausing sandboxes is the lever, and using it well is the difference between a sane bill and a silly one. Set aggressive default timeouts; agents crash and orphan sandboxes more often than you'd like.

On Modal, a function that isn't running costs nothing, which is excellent for spiky batch work. The equivalent trap is keeping containers warm to avoid cold starts: that is a real setting, it does help latency, and it bills for the idle time.

The costliest mistake on either platform is architectural rather than a rate card. Building an agent loop on a stateless function platform means re-creating environment state on every step — reinstalling packages, re-uploading files — and you pay for all of it in both time and money. Pick the primitive that matches the loop.

For the wider field, the sandbox providers hub covers Daytona, Fly Machines, Northflank, Runloop, Cloudflare and others on the same fields.