E2B vs Modal vs Daytona in 2026: What Agent Sandboxes Get Right That Agent-Operated Deploys Still Don't

Every AI coding agent that writes and runs its own code needs somewhere safe to do it. By late 2026, three names dominate that answer: E2B, Modal Sandboxes, and Daytona. They look interchangeable from a README — all three spin up an isolated machine from an SDK call, run untrusted code, and bill by the second.

They are not interchangeable. Pick E2B or Daytona over Modal and comparable CPU time costs about 30% less. Pick Daytona for its open-source runtime and you inherit a June 2026 closed-core turn that changed what "open" means. And pick any of the three for executing code, then ask your agent to deploy an app behind a stable URL, and you discover the thing none of them sells: sandboxes are session-scoped by design, and a deployment is everything a session is not.

The full comparison table lands in the next section. Then the isolation models, the worked pricing math, the Daytona governance twist, and finally the deploy gap — the concrete checklist between "run this snippet" and "hand back a running HTTPS URL."

E2B vs Modal vs Daytona at a glance​

Rates and plan details below are checked against the vendors' own pricing pages via September 2026 comparisons by Tech Insider and Upstash.

Verdict per dimension: isolation depth goes to E2B, raw creation speed to Daytona, platform breadth to Modal, and unit price is a tie between E2B and Daytona. Every one of those needs a paragraph of qualification, which is what the rest of this post is.

Isolation is three designs, not a ranking​

E2B builds on Firecracker, the microVM technology AWS open-sourced in 2018 and still uses to isolate Lambda functions. Each sandbox boots a minimal Linux kernel with just enough device emulation to run the workload. That is a genuine hardware-virtualization boundary: a compromised sandbox must escape a hypervisor, not just a shared kernel. For fully adversarial input — code you did not write, from a tenant you do not trust — it remains the strongest of the three answers.

Modal takes the middle path with gVisor, Google's user-space kernel, which intercepts and re-implements system calls instead of passing them to the host kernel. Google built it for exactly this shape of problem — running untrusted code inside Cloud Run without full-virtualization overhead. It is lighter than a microVM and stronger than a plain container, and its maturity shows: the sandbox is one primitive inside a much larger platform that also covers GPU inference, scheduled functions, queues, and volumes.

Daytona defaults to container-based isolation and spends its differentiation budget on speed and control instead: a claimed sub-90ms path from API call to executing code, plus workspace persistence and IDE-style integration aimed at full development environments rather than pure ephemeral task execution. Containers are the weakest boundary of the three against a truly adversarial tenant — but for agent-generated code your own harness produced and partially controls, the boundary that matters is speed of iteration, and that is the trade Daytona chose.

The honest read, borrowed from Upstash's 15-provider comparison: these are three designs for three threat models, not a leaderboard. Adversarial multi-tenant code wants Firecracker. Your own agent's tool calls are fine in any of the three.

The pricing math, worked​

Start with the unit rates, all checked against September 2026 comparisons of the vendors' own pricing pages. E2B and Daytona charge identical base compute: $0.0504 per vCPU-hour and about $0.0162 per GiB-hour. Modal's Sandbox pricing sits notably higher at roughly $0.071 per vCPU-hour equivalent and $0.024 per GiB-hour — a ~40% premium Modal justifies with a broader platform spanning GPU inference, training, and general serverless functions rather than sandboxes alone.

Now put a typical agent workload on those rates: 1,000 tasks per day, each consuming 10 minutes of a 1 vCPU / 2 GB sandbox. That is 10,000 sandbox-minutes a day, or about 5,000 sandbox-hours a month. At E2B/Daytona rates ($0.0504 + 2 × $0.0162 = $0.0828/hr), the month costs roughly $414 in pure compute. The same shape on Modal (~$0.071 + 2 × $0.024 = $0.119/hr) lands near $595. Same tasks, same isolation outcome for agent-generated code, ~44% more money — before Modal's $250/mo Team tier or E2B's $150/mo Pro base fee enter the picture.

But the bigger billing story is not the 40% gap between vendors. It is the wall-clock vs active-CPU divide across the market. E2B, Modal, and Daytona all bill wall-clock on their standard rates: the meter runs while your agent waits on a model response, which for chat-style coding agents is most of the runtime. Vercel Sandbox ($0.128 per active vCPU-hour) and Cloudflare Sandbox instead meter only CPU actually consumed. Upstash's worked example puts an hour of mostly-waiting agent work at about $0.017 on active-CPU billing — 2.6x to 19x cheaper than the same hour on wall-clock providers. If your agents are idle-heavy, the billing model matters more than the vendor.

Free tiers tell you who each vendor is hunting. E2B's Hobby plan is scoped around concurrency (20 free concurrent sandboxes) rather than credits — it wants teams running real agent fleets, not experimenting. Daytona's flat $200 credit is the most generous on-ramp and burns on GPU time too, which makes it the cheapest way to prototype a GPU-touched agent. Modal's $30/mo credit is modest but sits inside a platform where the same credit also buys inference and training.

Daytona's June 2026 closed-core turn​

The row in the table that aged worst this year is Daytona's self-hostability. In June 2026, Daytona moved core development to a private codebase; the public daytonaio/daytona repository is explicitly no longer maintained, frozen at v0.190.0 under AGPL-3.0. The client libraries live on as Apache-2.0 in a separate repo, and the hosted service plus enterprise BYOC continue — but the thing teams adopted Daytona for, a fully open runtime they could audit and self-host, is now a snapshot, not a project.

The community response was immediate: Nightona, a fork of the last AGPL release, now carries the "fully open sandbox runtime" torch as a community project. And Cursor's documentation still lists self-hosted Daytona machines as a supported path — pinned, in practice, to the frozen release.

This matters beyond Daytona. E2B's runtime remains open and self-hostable, while Modal never offered that (open SDK, closed engine). Before June, the market had two open runtimes; now it has one plus a community fork. If your threat model or data-residency story depended on self-hosting the sandbox layer itself — not just pointing an SDK at a vendor's cloud — your 2026 shortlist just got shorter, and any comparison written before June that calls Daytona "fully open-source" is now wrong.

Funding context sharpens the picture. E2B's $21M Series A (Insight Partners, July 2025) rides on 88%-of-Fortune-100 enterprise penetration. Daytona's $24M Series A (FirstMark, February 2026) funded the pivot from human dev-environments to agent infrastructure. And Modal just operates at a different scale: a $355M Series C at $4.65B in May 2026, with September reports (via Reuters) of talks for ~$750M at roughly $15B. Modal is being priced as AI compute infrastructure, not as a sandbox vendor — which explains both the premium rates and the breadth.

The deploy gap: everything a sandbox URL is not​

Here is the moment this comparison has been building toward. Your agent has been running snippets in a sandbox. Now the job changes from "run this and return output" to "deploy this app and hand back a running HTTPS URL." Every item on the checklist below is something no code-execution sandbox ships, because a sandbox is session-scoped by design:

• A URL that outlives the session. Sandbox preview URLs are bound to the sandbox lifecycle — Vercel's own SDK docs show the public URL as a method on a live sandbox object (sandbox.domain(port)), and it stops serving when the sandbox stops. A deployment's URL must survive restarts, re-deploys, and the agent logging off.
• A custom domain. Sandboxes hand out generated hostnames. Mapping app.yourcompany.com with managed TLS issuance and renewal is PaaS surface, not sandbox surface.
• Durable state. E2B's pause/resume and Daytona's persistent sessions keep state across pauses, not across months. A deployed app's database and uploads must persist across months with backups — none of the three sells that.
• Rollback. Sandboxes have no notion of "the previous version is still live." A deploy needs versioned releases and one-command rollback when the new one fails health checks.
• Always-on and scale-to-zero. A sandbox bills while it exists and dies on a cap; a deployed service needs health-checked always-on (or genuine request-driven scale-to-zero like Knative), plus autoscaling the sandbox meter was never designed to express.

None of this is a criticism of sandboxes. Ephemeral, session-scoped, billed-by-the-second execution is exactly what agent tool calls need, and the three vendors above are genuinely good at it. The mistake is assuming the code-execution layer becomes the deployment layer if you just keep the session alive longer. It does not: stretching a 24-hour cap to a 30-day cap still leaves you without domains, TLS, releases, or rollback. The deploy surface has to be built — or bought — separately.

What sandbox-plus-PaaS would have to look like​

Close the gap and you get a concrete bridging list, which doubles as the spec for any PaaS (self-hosted or otherwise) adding E2B-style sandboxes to its roadmap:

1. Session state → persistent deploy. The artifact the agent built in the sandbox must promote to a first-class release — image, config, and environment — rather than living only inside a pausable VM.
2. Ephemeral URL → stable domain. Promotion must mint a stable HTTPS route, attach any custom domain, and handle TLS without the agent touching certificates.
3. No rollback → versioned releases. Every promotion is a numbered release with the previous one one command away.
4. Wall-clock meter → service meter. The sandbox's per-second execution billing must hand off to per-service hosting economics once the workload stops being a task and starts being an app.

That handoff — task economics becoming service economics — is the seam where most agent platforms today leave teams to wire two vendors together. The sandbox vendors know execution; the PaaS vendors know deployments; the agent in the middle just wants one API where "run this" and "ship this" are two calls on the same object.

Bex.co is the open-source, AI-native Render alternative — push a git repo, get a running HTTPS service on machines you own. Agent-operated deploys, with stable URLs, custom domains, and rollback, are the surface sandboxes stop short of. Star the repo on GitHub or deploy your first app today.