E2B vs Modal

Choose E2B for a persistent interactive sandbox an agent drives; choose Modal to run functions and containers at scale, especially GPU-bound workloads.

Editorial judgement last reviewed

Should I run agent code in E2B or Modal?

  • Both let an LLM run code that isn't yours, but for different halves of the problem.
  • E2BE2B logo is built around the sandbox as a session.
  • Create one, it boots in a fraction of a second, then you interact with it.
  • Write files, run shell commands, execute a Python cell, install a package, keep going.
  • The abstraction is a machine that stays alive while your agent thinks.
  • Exactly the shape of a code interpreter, data-analysis agent or autonomous coding loop.
  • Modal is built around the function as the unit.
  • Decorate a Python function, declare image and resources; Modal runs it with fast starts, autoscaling and good GPU access.
  • The abstraction is remote execution of your code, not a machine your agent lives in.
  • Modal exposes a working sandbox primitive, but its centre of gravity is deployment and batch compute.
  • If your product is an agent that writes and runs code, start with E2BE2B logo.
  • The SDK matches the model, session persistence is default, and you never think about images.
  • If your product runs your own workloads, inference, fine-tuning, video, ETL, GPU queues, Modal is a different tier.
  • Nothing in the sandbox category competes with Modal on GPU ergonomics.
  • Plenty of teams end up with both, a reasonable outcome rather than a failure to decide.

Which one should you pick?

Choose E2B if…

  • You're building a code interpreter, data-analysis agent or autonomous coding agent.
  • The agent needs state between steps: files written three tool calls ago must still be there.
  • You want an SDK that reads like "run this command in the box", not "deploy this function".
  • You want to hand an agent a filesystem and shell without your own container lifecycle.
  • Fast cold start on every new session matters, one per conversation not per deploy.
  • You want the open-source escape hatch: E2BE2B logo's infrastructure can be self-hosted.

Choose Modal if…

  • You need GPUs: Modal's availability, cold starts and per-second billing are the best here.
  • Your workload is your own code, deployed and scaled, not arbitrary LLM-written code.
  • You want serverless batch: fan out ten thousand invocations, pay only for compute used.
  • You want scheduled jobs, web endpoints and queues on the same platform as heavy compute.
  • Your team is Python-first: Modal's Python ergonomics are excellent, other languages second-class.
  • You want container images defined in code with layer caching that makes iteration bearable.

The session model: what happens between tool calls

  • An agent loop: the model asks to run a command, you run it, hand back output, repeat.
  • Between steps, seconds or minutes pass while the model thinks and a human reads.
  • E2BE2B logo's sandbox is designed for that gap.
  • It stays up for a configurable timeout; the next command lands in the same filesystem and packages.
  • The agent installs pandas once, writes a file and reads it back three steps later.
  • You can pause and resume sandboxes, parking a session rather than paying for it.
  • Modal's sandboxes can stay alive too, and the primitive is real.
  • But the platform's grain runs toward stateless input-output functions, and the tooling reflects that.
  • Build an agent loop on Modal and you'll spend a week building session management E2BE2B logo gives you day one.
  • Counter-case: if each step is genuinely independent and heavy, statelessness is correct and Modal wins.

Cold start, and why the number in isolation misleads

  • Both advertise fast starts, and both tell the truth about different things.
  • E2BE2B logo boots microVMs from a snapshot, targeting sub-second creation with a standard image.
  • That matters because you pay it on every new session, conversation, user and retry.
  • Modal's fast starts are about getting your container running, including dependencies.
  • Warm image and hot layer cache: genuinely quick.
  • Just changed a dependency: the image rebuilds and you wait far longer.
  • A large ML image with model weights is a different conversation entirely.
  • What predicts your experience is time from "I need compute" to "first command produced output".
  • A sub-second boot lacking your agent's three packages is really sub-second plus a pip install.
  • Our table measures cold start with a standard image; re-run it with yours before committing.

Isolation and running untrusted code

  • If an LLM wrote the code, treat it as untrusted.
  • True even for a well-behaved model: prompt injection turns an uploaded document into an instruction.
  • Both isolate at the microVM level, not container boundaries alone, the right posture.
  • Check the same short list either way for your threat model.
  • Whether outbound network can be restricted; whether filesystem is confined to the sandbox.
  • The ceiling on CPU, memory and wall-clock; whether a noisy tenant can affect yours.
  • E2BE2B logo's default posture assumes the code is hostile, its entire use case.
  • Modal's default posture assumes the code is yours, because usually it is.
  • Use Modal's sandbox primitive, not a plain function, for model-written code.
  • The controls that matter most are network egress restrictions and hard timeouts.
  • Wire those up before you ship, not after an agent writes an infinite loop.

Pricing shape and what it costs to be wrong

  • Both bill compute time by resource size, roughly per second.
  • The difference is what you pay for while nothing is happening.
  • On E2BE2B logo, an idle-but-alive sandbox is still allocated, so thinking time costs you.
  • Pausing sandboxes is the lever between a sane bill and a silly one.
  • Set aggressive default timeouts; agents crash and orphan sandboxes often.
  • On Modal, a function that isn't running costs nothing, excellent for spiky batch work.
  • The equivalent trap is keeping containers warm: it helps latency but bills idle time.
  • The costliest mistake is architectural, not a rate card.
  • An agent loop on a stateless platform re-creates state every step: reinstalls, re-uploads, paid in time and money.
  • Pick the primitive that matches the loop.
  • The hub covers DaytonaDaytona logo, Fly Machines, NorthflankNorthflank logo, RunloopRunloop logo, Cloudflare and others on the same fields.

Frequently asked questions

Can I run an AI agent's code on Modal?

  • Yes, use Modal's sandbox primitive rather than a plain function.
  • That keeps the code isolated and resource-capped.
  • You'll build more session management than on E2BE2B logo, worth it if you're already on Modal for GPU work.

Does E2B do GPUs?

  • GPU support exists but isn't the product's centre of gravity.
  • If your agent needs to train or serve a model rather than run scripts, Modal's GPU platform is a different class.

Which is cheaper for a coding agent?

  • Usually E2BE2B logo: a function platform re-creates environment state on every step.
  • If your steps are naturally independent and short, Modal's pay-only-while-running model can win.

Can I self-host either one?

  • E2BE2B logo publishes its infrastructure as open source, so self-hosting in your own cloud is documented.
  • Modal is a closed, hosted platform.
  • That matters if data residency or an air-gapped environment is a requirement.

What language support do they have?

  • Modal is Python-first by design: you can run any container, but ergonomics assume Python.
  • E2BE2B logo ships Python and TypeScript/JavaScript SDKs.
  • That matters when your agent orchestration lives in a Node backend.

Do I need either if I already run Kubernetes?

  • If you're running untrusted model-written code, yes.
  • Container isolation on a shared cluster is a weaker boundary than a microVM.
  • The sub-second-start machinery is not a weekend project.
  • If you're running your own trusted code, your existing cluster is fine.
All sandbox providers, compared field by field