AI Agent Sandbox Pricing: 1,000 Sessions on E2B, Daytona, Modal and Vercel

One thousand ten-minute agent sessions on a 2 vCPU, 4 GiB machine cost $27.60 on E2B and exactly $27.60 on Daytona, because the two publish identical per-second rates. The same workload costs $39.66 on Modal. On Vercel it costs anywhere from $18.40 to $56.80, depending on how hard your agent works the CPU. Choosing an AI agent sandbox is therefore less about the rate card and more about what your agent does while it waits.

Direct answer. For a 2 vCPU, 4 GiB AI agent sandbox running ten minutes, E2B and Daytona both cost $0.0276 per session at published rates. Modal costs $0.0397. Vercel Sandbox costs $0.0184 at 10% CPU activity and $0.0568 at full load, because it bills CPU only while code executes but bills memory for the whole session.

What an AI agent sandbox actually meters

A sandbox is a short-lived, isolated computer that your agent uses to run code it wrote itself. Coding agents need one to execute tests. Data agents need one to run pandas against a file a user uploaded. The point is that untrusted, model-generated code never touches your production hosts.

All four vendors in this comparison meter three things: CPU, memory and time. The difference lies in which of those three they multiply together. E2B, Daytona and Modal charge for allocated resources for every second the sandbox exists. Vercel charges memory for every second, but CPU only for the seconds when code is actually running.

That distinction sounds academic until you watch what an agent session does. Most of the wall-clock time in an agent loop is spent waiting for the model to respond. The sandbox sits idle while the LLM thinks, then runs a command for two seconds, then idles again. If you are already tracking how vendors meter the cost of a single agent task, the sandbox is the line item where idle time is most visible.

My position is simple. The headline rate on each vendor's pricing page is the least informative number in this category. The billing basis, allocated or active, decides the bill far more than a fraction of a cent per hour does.

The published sandbox rate card for four vendors

Every rate below was taken from the vendor's own pricing page or documentation on 30 October 2026. Hourly figures are converted from per-second rates, or the reverse, where a vendor publishes only one.

Sources: E2B pricing page, Daytona pricing page, Modal pricing page and sandbox guide, Vercel Sandbox pricing and overview docs, all retrieved 30 October 2026. Daytona storage is free for the first 5 GiB, then $0.000108 per GiB-hour. E2B lists storage as free. Vercel rates apply to the default iad1 region only.

E2B and Daytona are price-matched

E2B's $0.000014 per vCPU-second is exactly Daytona's $0.0504 per vCPU-hour divided by 3,600. The memory rates match the same way. Whether that is coincidence or deliberate price-matching, the effect for you is that compute price cannot be the deciding factor between them.

The differences sit elsewhere. E2B charges a $150 monthly plan fee for its Pro tier and gives Hobby users a one-time $100 credit. Daytona lists no plan fee and includes $200 in free compute. Daytona also charges for storage above 5 GiB, which E2B does not.

Modal's sandbox premium is disclosed, not hidden

Modal publishes a list CPU rate of $0.0000131 per core-second for its general functions. Its pricing page then states plainly that sandboxes are billed at non-preemptible rates of 3x list price. A developer who reads only the headline function rate will underestimate the sandbox bill by two thirds.

Modal also prices per physical core, which it defines as 2 vCPUs. Converted, Modal's sandbox CPU costs about $0.071 per vCPU-hour against $0.0504 at E2B and Daytona. That is a 41% CPU premium on a like-for-like basis.

Vercel splits CPU from memory

Vercel's sandbox page lists Active CPU at $0.128 per hour and provisioned memory at $0.0212 per GB-hour. Each vCPU includes 2 GB of memory, so a 2 vCPU sandbox is always a 4 GB sandbox. You cannot buy a lean 2 vCPU, 1 GB box the way you can on E2B or Modal.

Isolation technology: microVM, gVisor or container

Price is the comparison everyone runs first. Isolation is the one that matters if your agent ever executes something hostile. A sandbox exists to contain code you did not write and cannot review, including code an attacker smuggled in through a prompt injection against your agent.

There are three broad isolation models in this group. Each puts the boundary between your sandbox and its neighbours in a different place.

Firecracker microVMs: E2B and Vercel

E2B's homepage states that every session receives a hardware-isolated Firecracker microVM rather than a shared-kernel container. Vercel's documentation says each sandbox runs in a secure Firecracker microVM with its own filesystem and network. Firecracker is the virtualisation layer AWS built for Lambda. Each sandbox gets its own Linux kernel, so an escape requires breaking out of a hardware virtualisation boundary.

gVisor or VM: Modal

Modal's sandbox guide says sandboxes run on one of two runtimes. gVisor is a container runtime from Google that intercepts system calls in user space, so sandboxed code never talks to the host kernel directly. The second option runs the sandbox in a virtual machine with its own Linux kernel. If you leave the runtime parameter unset, Modal chooses for you, which means you should set it explicitly if your threat model depends on it.

Containers by default: Daytona

Daytona's documentation says sandboxes run as Linux containers by default, described as an isolated container with dedicated namespaces and enforced resource limits. A full virtual machine class with its own kernel is available, and Daytona ties pause, resume, fork and hot snapshots to that VM class. Containers share the host kernel. That is the faster and cheaper boundary, and it is also the thinner one.

I would not run untrusted, internet-facing agent code in a shared-kernel container if a VM option sits on the same account. The price difference between these vendors is pennies per thousand sessions. The cost of a container escape is not measured in pennies.

Pricing 1,000 agent sessions of a fixed length

To compare like for like, every vendor gets the same workload. Each session runs for 10 minutes, or 600 seconds of wall-clock time. Each sandbox has 2 vCPUs and 4 GiB of memory, which is E2B's stated default on its pricing page and Vercel's default of 2 vCPUs with 2 GB each. On Modal that is 1 physical core.

The working is shown so you can change any input. For E2B, the per-second cost is 2 times $0.000014 plus 4 times $0.0000045, which is $0.000046. Multiply by 600 seconds and you get $0.0276 per session, or $27.60 per 1,000.

Zan Digital calculation, 30 October 2026. Excludes plan fees, free credits, storage, data transfer and Vercel's $0.60 per million creations, which adds $0.0006 per 1,000 sessions. Vercel memory is quoted in GB and treated here as equal to GiB. Vercel active CPU is assumed to bill per vCPU-hour. Daytona's default 3 GiB disk sits inside its 5 GiB free storage allowance.

Every figure in that table is under $60 per thousand sessions. If your agent runs 1,000 sessions a month, the spread between the cheapest and most expensive scenario is $38.40. That is less than one engineer-hour at most companies, and far less than the model tokens consumed in those same sessions.

So my view is that most teams should not pick a sandbox vendor on price at this volume. Pick on isolation, limits and developer experience. Price starts to decide the choice around a million sessions a month, where the same spread becomes $38,400. At that scale it belongs in your enterprise agent cost model as a real line.

The utilisation crossover that decides Vercel sandbox cost

Vercel's bill has a fixed part and a variable part. Memory is provisioned for the whole session, so 4 GB for ten minutes costs $0.0141 no matter what. CPU costs $0.0427 per session if both vCPUs run flat out, and nothing if they sit idle.

That gives two break-even points on our workload. Vercel matches E2B and Daytona at about 32% CPU utilisation. It matches Modal at about 60%. Below 32%, Vercel is the cheapest of the four. Above 60%, it is the most expensive.

Where real agents tend to sit

No vendor publishes the CPU utilisation of a typical agent session, and I have no first-party measurement to offer. What can be said is structural. A coding agent that waits on a frontier model for most of each turn will spend most of its wall-clock time idle. A data agent running a long pandas job or a test suite will not.

That is why measuring your own workload matters more than reading this table. Instrument one sandbox for a day, log active CPU seconds against wall-clock seconds, and you will know which side of the 32% line you sit on. The same logic applies to inference provider rate cards, where the cheapest per-token price does not always produce the cheapest bill.

Modal's request floor changes the picture

Modal charges the higher of what you request and what you use. By default each sandbox requests only 0.125 cores and 128 MiB, and can burst beyond that when the host has spare capacity. If you keep the default request, an idle Modal sandbox costs about $3.46 per 1,000 ten-minute sessions at the floor. Modal then behaves like a usage-billed product, not a reservation.

The catch is the phrase about spare capacity. Bursting is not a guarantee. If your agent needs 2 vCPUs reliably, you reserve them and pay the $39.66 figure. Teams that accept occasional slowdowns can pay much less.

Limits that cost more than the per-second rate

Per-second rates get the attention. Session caps, concurrency ceilings and plan fees decide whether a vendor works for your agent at all. These are the limits published by each vendor on the pages reviewed.

Sources: vendor pricing pages and documentation retrieved 30 October 2026. Startup claims are each vendor's own marketing statements and were not independently tested. Vercel's Pro plan fee is not part of the sandbox pricing page and is excluded.

Free credits cover more sessions than you might expect

At our fixed spec, E2B's one-time $100 credit covers about 3,600 sessions. Daytona's $200 covers about 7,200. Modal's $30 monthly credit covers about 750 reserved sessions every month. Vercel's Hobby allowance is the tightest: at 25% CPU activity, its 5 included CPU-hours run out after about 60 sessions. For a prototype, the free tier usually matters more than the rate, a pattern covered in how free tiers meter compute.

Daytona claims sub-90ms creation for its container class. Modal claims under one second, including on large custom images. E2B and Vercel publish no millisecond figure on the pages reviewed. None of these claims comes with a stated methodology, region or image size, so ranking them would be false precision. Notice also that Daytona's fastest figure applies to containers, the thinner isolation boundary.

Where this comparison is weakest

A cost table this tidy hides several assumptions, and some of them could reverse a conclusion.

First, the 2 vCPU, 4 GiB spec suits E2B and Vercel because it is their default shape. Daytona's default sandbox is 1 vCPU and 1 GB, and Modal's default request is far smaller. A team that runs lean sandboxes would see E2B and Daytona fall to a fraction of $27.60, and Vercel cannot follow because it pairs 2 GB with every vCPU.

Second, E2B's own documentation disagrees with itself. The pricing page names 2 vCPUs and 4 GiB as the default, while its billing FAQ and SDK example use 512 MiB. If your template really runs at 512 MiB, your E2B bill is lower than shown here.

Third, the Vercel calculation assumes active CPU bills per vCPU-hour and treats GB as GiB. Both are reasonable readings of the documentation, and neither is stated in exactly those words. Vercel rates also apply only to the iad1 region, and other regions are priced differently.

Fourth, and most important, sandbox cost is a small share of total agent cost. Model tokens dominate almost every agent bill. A team that switches sandbox vendors to save $10 per thousand sessions, and in doing so adds latency that triggers extra model turns, can easily lose money. That trade-off is the same one behind the build versus buy decision for coding agents.

The competing view, that per-second price is the primary selection criterion, genuinely wins at very high volume with uniform workloads. A reinforcement learning team spinning up millions of identical sandboxes should optimise the rate. Most product teams are not that team.

Frequently asked questions

Where to start

Measure one number before you compare any vendor: the share of each agent session your sandbox CPU is actually busy. Log active CPU seconds and wall-clock seconds for a day of real traffic. If the ratio is under a third, test Vercel Sandbox. If it is higher, E2B and Daytona will cost less on the same spec.

Then check the isolation setting you are actually running, not the one on the marketing page. On Modal, set the runtime parameter explicitly. On Daytona, decide whether untrusted code belongs in the VM class. Neither change costs anything to make, and both are easier before launch than after an incident.

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