AI Agent Sandboxes Compared: E2B vs Daytona vs Modal vs Vercel

The comparison problem nobody mentions

If you search for agent sandbox comparisons you will find a dozen articles with the same table: provider names down the left, a per-hour price in one column, a cold-start number in another, and a recommendation at the bottom.

That table cannot be built honestly, because the providers are not selling the same unit.

Two of them charge for every second the sandbox exists. One of them charges only for the seconds your code actually occupies a CPU, and says so explicitly. For an AI agent — a workload whose defining characteristic is spending most of its wall-clock life waiting on a model to respond — that distinction is worth more than the entire spread of the headline rates.

So this comparison starts with the meter, not the number.

How we compared

Every figure below was read directly from the provider's own pricing page or documentation on 6 September 2026, and linked at the point of use. Where a provider does not publish something, we say so rather than importing a number from another comparison article. We have not run these sandboxes against a benchmark harness and we make no first-person latency or performance claims.

Billing meters, before prices

Vercel's documentation is unusually direct about this: "The amount of time your code actively uses the CPU, measured in hours. Time spent waiting for I/O (such as network requests, database queries, or AI model calls) does not count toward Active CPU."

Read that sentence with an agent workload in mind. A coding agent that runs for twenty minutes might spend eighteen of them waiting on inference. Under a wall-clock meter you pay for twenty. Under Vercel's meter you pay for something closer to two.

We are not claiming that makes Vercel cheapest — the rate is higher, the memory meter is provisioned rather than active, and your actual idle ratio decides the outcome. We are claiming that any table putting these rates in one ranked column is producing a meaningless ordering, and that is most of the tables currently published.

The rates, with the meter attached

Sources: E2B pricing, Daytona pricing, Modal pricing, Vercel Sandbox pricing, Cloudflare Sandbox docs. Vercel’s rates are for the default iad1 region and vary by region.

Two findings fall out of this that contradict what is widely published.

E2B and Daytona really are at parity. Roughly $0.050 against $0.0504 per vCPU-hour, and $0.016 against $0.0162 per GiB-hour. That is close enough that price should not be your deciding factor between those two, and it is the one direct comparison in this set that survives scrutiny.

Modal is not at parity, and the error is widespread. Modal publishes Sandboxes and Notebooks as a separate, more expensive tier from its standard compute. The Sandbox rate is $0.1419 per core-hour — about 2.8 times Modal's own standard rate of $0.04716, and about 2.8 times E2B and Daytona. Comparison articles that place Modal at parity are quoting the standard compute rate, which is not what you pay when you run a sandbox. If you have been budgeting from one of those tables, your Modal line item is roughly a third of what it should be.

Modal is also the only provider here that runs GPUs inside the sandbox, at published rates including $3.949/hour for an H100 SXM5, $7.099/hour for a B300, $2.498/hour for an A100 80GB and $0.799/hour for an L4. If your agent needs a GPU, the CPU rate is not the number that decides anything.

What are you actually isolating against?

If you are running model-generated code, isolation is the property you care most about and the one that is hardest to verify. Here is what each provider states in its own documentation, which turns out to be a shorter list than expected.

This table is the most useful thing in the article and it took the longest to build, because three of the five do not answer the question in the obvious place.

The correction worth flagging: several published comparisons state that Vercel Sandbox uses shared-kernel containers. Vercel's own documentation says Firecracker microVM, and further notes that sandboxes support system-privileged processes including Docker, VPN clients and FUSE drivers — capabilities that are difficult to offer inside a shared-kernel container. Where a vendor's documentation contradicts a third-party comparison, the documentation wins.

For E2B and Modal, third-party comparisons consistently describe Firecracker microVMs for E2B and gVisor for Modal. Those may well be accurate. We could not confirm either from the vendors' own pages, so we are not putting them in the table. If isolation strength is a compliance requirement for you, ask the vendor in writing rather than trusting any comparison article, including this one.

Cloudflare's container model is worth calling out plainly rather than burying: containers share the host kernel, which is a materially weaker boundary than a microVM against a kernel-level escape. That may be entirely acceptable for your threat model. It should be a decision, not a surprise. If you are thinking about what untrusted model output can actually do, our guide to AI guardrails covers the layer above the sandbox.

How long can a sandbox actually run?

Session limits shape architecture more than most teams expect, because they determine whether a long agent task needs checkpointing.

• Modal: sandboxes have a default maximum lifetime of 5 minutes, configurable up to 24 hours via a timeout parameter. Beyond 24 hours, Modal's guide directs you to filesystem snapshots and restore.
• Vercel: default timeout is 5 minutes, extendable in code. The hard cap is 45 minutes on Hobby and 24 hours on Pro and Enterprise — but the cap applies to a session, not the sandbox, and resets whenever a persistent sandbox stops and resumes, making total lifetime effectively unbounded.
• E2B, Daytona, Cloudflare: limits are documented on separate pages we did not verify in this pass. Check them before designing around a long-running task.

Vercel also publishes concurrency and resource ceilings that matter at scale: 10 concurrent sandboxes on Hobby against 10,000 on Pro and Enterprise, maximum 4, 8 or 32 vCPUs by plan, and 64 GB of ephemeral NVMe storage per sandbox. E2B's Pro plan at $150/month lists up to 100 concurrent sandboxes.

Why is cold start missing from this comparison?

Because we could not verify a single cold-start number to the standard we apply to prices, and publishing an unverifiable ranking would be worse than publishing none.

Daytona's pricing page advertises "sub 90ms sandbox creation from code to execution," and its documentation repeats it. That is a vendor claim about its own product. It may be entirely accurate. It is not an independent measurement, and it is not measured against the same image, region or payload as anyone else's number.

The comparative spreads circulating in other articles — Vercel around 50ms, Modal around 100ms, E2B in the 150 to 500ms range — trace back to third-party benchmarks that do not publish their methodology, configuration or image sizes. Cold start is extremely sensitive to all three. A benchmark that boots a minimal Alpine image and one that boots a Python image with preinstalled ML libraries will disagree by an order of magnitude, and both would be reporting honestly.

If startup latency is a hard requirement for your product, the only reliable number is one you measure yourself, on your own image, in your own region. Every provider here offers free credits sufficient to do that in an afternoon: E2B gives $100 in one-time credits on Hobby, Daytona gives $200 on signup, and Vercel's Hobby tier includes 5 hours of Active CPU and 5,000 sandbox creations per month.

A note on adoption claims

E2B's marketing states that 88% of Fortune 100 companies have signed up, a figure repeated in a VentureBeat headline covering its $21M Series A. More recent E2B materials put the number at 94%.

The figure originates from E2B's own materials and we found no third-party verification of it. "Signed up" is also doing considerable work — it is not the same as deployed in production, and a single engineer creating a free account is presumably enough to count a company.

We are not saying the number is false. We are saying it is a vendor-reported metric with an undisclosed definition that has moved six points in the direction that flatters the vendor, and it should carry no weight in a procurement decision.

Which should you choose?

If your agent spends most of its time waiting on model calls: price Vercel Sandbox first. The Active CPU meter is designed for exactly this shape of workload, and the headline rate being 2.5x higher than E2B's is misleading when you are billed for a fraction of the wall-clock time. Model it against your own idle ratio before deciding.

If your sandboxes run CPU-bound work continuously: E2B or Daytona, where the wall-clock meter is not working against you and the two are at genuine price parity. Choose between them on isolation requirements and tooling rather than cost.

If your agent needs a GPU inside the sandbox: Modal, which is the only provider here that offers one. Accept the 2.8x sandbox-tier CPU premium as the cost of that capability, and budget from the Sandbox rate rather than the standard compute rate.

If you are already deep in the Cloudflare Workers ecosystem: Cloudflare Sandbox, with a clear understanding that container isolation shares the host kernel. Confirm that fits your threat model before running untrusted output in it.

If isolation strength is a compliance requirement: Vercel is the only provider in this set that states Firecracker microVM isolation plainly in its own documentation, which is a meaningful advantage when you have to evidence a control rather than assert it. Ask the others in writing.

Conclusion

The most useful thing we found while researching this piece was not a price. It was that the published comparisons of this category are structurally unreliable — they rank incompatible billing meters in a single column, quote Modal's wrong tier, and assert isolation models that contradict the vendors' own documentation.

The category itself is in good shape. E2B and Daytona have converged on price, Vercel has shipped a meter that genuinely suits agent workloads, Modal owns the GPU case, and Cloudflare has the ecosystem play. There is no dominant option and no obviously bad one.

Work out your idle ratio first. It determines which meter is in your favour, and that decides more than any other variable here. If you are building the agent rather than choosing where to run it, our guides to building an MCP server and to agent frameworks cover the layers above this one, and the frontier model comparison covers what will be generating the code you sandbox.

This comparison is an editorial synthesis of vendor pricing pages and official documentation read on 6 September 2026, linked inline at each claim. We did not deploy workloads, run latency benchmarks, or receive briefings from these teams. Where a vendor does not publish a figure we say so rather than sourcing it from secondary coverage, and where third-party comparisons contradict vendor documentation we follow the documentation. Rates and limits change; verify before relying on any figure here.