The moment an AI agent writes code, you have a problem: something has to run it, and running model-generated code on your own machine is how you lose a machine. A code sandbox is the answer — a disposable, isolated microVM that boots in milliseconds, executes whatever the model produced, hands back stdout, and dies.

The two names you'll meet first are E2B and Daytona. Both call their entry tier free. Neither is free the way an open-source library is free, and the difference is worth 60 seconds of your attention before you wire one into a production agent loop.

What "Free" Actually Means Here

Sandboxes burn real CPU-seconds on someone else's hardware, so nobody gives them away on a recurring basis. In practice "free" collapses into three deals:

• One-time credits — a fixed dollar grant that never comes back (E2B, Daytona). Great for building; useless as a production budget.
• Recurring credits — a monthly allowance that resets (Modal's $30/month Starter). Small, but it survives launch day.
• Self-hosted, unlimited — an open-source runtime on hardware you already pay for. The only tier with no meter attached.

Every "free code sandbox" roundup blurs these together. They are not the same product.

E2B Free Tier: $100, 20 Concurrent Sandboxes

E2B runs Firecracker microVMs — the same isolation primitive behind AWS Lambda — and is the default choice in most agent tutorials. Its Hobby plan is explicit about what you get:

• $100 of usage in credits (one-time, not monthly)
• Up to 20 concurrently running sandboxes
• Up to 1-hour sandbox session length — Pro raises this to 24 hours

Two commands and five lines get you running code:

pip install e2b-code-interpreter python-dotenv

from e2b_code_interpreter import Sandbox

sbx = Sandbox.create()
execution = sbx.run_code("print('hello world')")
print(execution.logs)

files = sbx.files.list("/")
print(files)

The 1-hour cap is softer than it looks. Sandboxes have no fixed default lifetime — you set a timeout at creation and can reset the clock at runtime with set_timeout(), which is the standard pattern for interactive apps (bump the timer on every user message). For work that must outlive an hour, pause and resume preserves full sandbox state indefinitely and restarts the runtime window.

Daytona Free Tier: $200 and Sub-90ms Starts

Daytona attacks the metric that actually hurts in an agent loop — cold start. It advertises sandboxes "spinning up in under 90ms from code to execution", and at 72k+ GitHub stars it's the more visible project of the two. Its pricing page includes $200 in free compute with no credit card, plus a startup program worth up to $50,000 in credits.

pip install daytona

from daytona import Daytona, DaytonaConfig

daytona = Daytona(DaytonaConfig(api_key="YOUR_API_KEY"))
sandbox = daytona.create()
response = sandbox.process.code_run('print("Hello World!")')
print(response.result)

Two honest caveats that most comparison posts skip. First, Daytona's public pricing page does not publish a Linux vCPU-hour rate — the only per-unit figure printed on it is $0.0858/vCPU/h for Windows sandboxes. Third-party blogs quote a Linux number, but you can't verify it at the source, so treat the $200 as "generous but unquantifiable in hours until you're logged in and metering."

Second, the isolation is not equivalent. Daytona's own isolation docs describe the default Container sandbox as an "isolated container with dedicated namespaces and enforced resource limits" — kernel shared with the host — and reserve "full virtual machine with its own kernel" for its separate VM sandbox class. E2B runs Firecracker microVMs on every sandbox by default. If the code you're running is fully untrusted, that distinction matters more than either free tier.

E2B vs Daytona vs Modal vs Self-Hosted

How Far Does $100 Actually Go?

E2B is the only one of the three publishing per-second rates, so its credits are the ones you can do arithmetic on. From the pricing page: $0.000028/s for the default 2 vCPUs and $0.0000045/GiB/s for memory. A default 2 vCPU / 1 GiB sandbox therefore costs $0.0000325/s, or about $0.117 per hour. That means:

• ~855 sandbox-hours of continuous runtime on the $100 Hobby grant
• ~102,000 executions if a typical agent step runs 30 seconds and the sandbox is torn down after

That is a lot of development and a genuinely useful pilot — and it is still a fixed budget that ends. Compare it with Modal, whose Sandboxes bill at $0.00003942/core/sec (~$0.142/core-hour) against a recurring $30/month: roughly 210 core-hours every month, forever, which we broke down in the Modal free-credits guide. Higher unit price, lower ceiling, but it never hits zero.

The Genuinely Free Path: Self-Host

If your agent runs code all day, the hosted math never works. Three open-source options remove the meter entirely:

• E2B infra — e2b-dev/infra is Apache-2.0, deployed with Terraform on AWS, GCP, Azure, or plain Linux. It's the real production stack, and it expects real infrastructure skill (Nomad, Consul).
• microsandbox — Apache-2.0, written in Rust, boots a libkrun microVM in ~320ms with no shared host kernel. A single binary with Python, JS, Rust, and Go SDKs and no Kubernetes requirement — the easiest true self-host.
• Daytona — self-hostable, but AGPL-3.0: if you modify it and expose it over a network, you must publish your changes. Fine internally, a real decision if it's inside a commercial product.

Put microsandbox on an always-free 4-core ARM VPS and you have unmetered code execution for $0/month.

Limits to Know

• Credits expire quietly. One-time grants aren't a plan. Instrument your spend before you ship, not after the first agent loop runs away.
• Concurrency bites before credits do. E2B's 20-sandbox free ceiling is the constraint you hit first with a multi-agent crew, not the dollars.
• Egress and storage bill separately from CPU and RAM on every hosted provider — the per-second rate is not the whole invoice.
• Self-hosting a microVM needs nested virtualization, which many cheap VPS plans don't expose. Check before you commit to a host.

Frequently Asked Questions

Is there a truly free code sandbox for AI agents?

Only if you self-host. Every hosted provider offers one-time or monthly credits, not an unlimited free tier — the closest thing to permanent free hosted execution is Modal's $30/month Starter credit, and the only unmetered option is running an Apache-2.0 runtime like microsandbox or E2B infra on your own server.

Do E2B or Daytona need a credit card to start?

No. Both grant their starting credits ($100 and $200 respectively) on signup with no payment method, which is why they're the standard choice for prototyping an agent that executes code.

Can I just use Docker instead?

You can, but a plain container shares the host kernel — an escape reaches your machine. E2B and microsandbox run microVMs with a separate kernel by default, which is the isolation level you want for code an LLM wrote and nobody reviewed. Daytona gets you there too, but only on its VM sandbox class, not the default container one.

Which one should I start with today?

Daytona if you want the biggest grant and the fastest cold start; E2B if you want microVM isolation on every sandbox by default and the shortest path from pip install to working code. Both take under five minutes, so the honest answer is to try both on their free credits before committing.

The Verdict

Treat hosted sandbox credits as what they are: a generous, finite trial. E2B's $100 buys roughly 855 sandbox-hours, microVM isolation on every sandbox, and rates you can actually do arithmetic against; Daytona's $200 buys more and starts faster, but its default sandbox is a container and its Linux rate isn't public. Both are the right way to build an agent that runs code. When that agent starts running all day, the answer stops being a free tier and becomes an Apache-2.0 runtime on hardware you control — which, on an always-free VPS, is the only version of this that is free forever.

Once the sandbox is chosen, the agent itself still needs somewhere to live — see free hosting for AI agents.

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