Best AI Agent Sandbox in 2026: 27 Benchmarked, 3 Just Launched

The answer (which AI agent sandbox to pick)

Pick an AI agent sandbox by the shape of your workload, then check the measured data, because the data moves every week. As of 25 September 2026: for short stateless code bursts, E2B and Modal are proven choices. For long-lived sessions that pause, resume and fork, look at E2B Pro, Daytona, CreateOS, and the new DigitalOcean Managed Agents (public preview). For coding agents that start on a laptop and need to keep running in the cloud, Docker Cloud Sandboxes launched on 24 September. For reinforcement-learning runs at thousands of concurrent sandboxes, Prime Sandboxes went generally available on 23 September. When the sandbox has to become a deployed app, that is where CreateOS fits.

On the independent ComputeSDK sandbox benchmark, the 25 September 2026 burst run puts Isorun first at a 44 ms median and CreateOS second at 125 ms, both with 100% success. E2B measured 1.24 s and Modal 0.91 s. CreateOS has completed 100% of launches in all nine burst runs since 14 August. None of the three September launches is on that board yet, so their speed figures below are vendor-stated.

Disclosure, up front: CreateOS is our product, and CreateOS Sandbox is a young product. Our own GitHub Actions runner and our coding-agent plugin run on it. We are not a ComputeSDK sponsor, the methodology and raw results are public, and we tell you below where we lose.

How fast do AI agent sandboxes actually start?

Measured median startup on the latest run ranges from 44 ms to 15.8 s across the 27 providers ComputeSDK displays. ComputeSDK, operated by Snelling, LLC, measures time to interactive (TTI): the time from calling sandbox.create() to the first successful command inside the sandbox. The burst test launches 100 sandboxes at once, with a 120-second timeout per sandbox. The code and raw results are public at github.com/computesdk/benchmarks, so anyone can reproduce them.

Selected results from the burst run of 25 September 2026 (16:26 UTC), read from the raw results file:

The composite score weights median at 60%, P95 at 25% and P99 at 15%, then multiplies by the success rate. A provider at 50% success has its score halved. That multiplication matters, and the next section shows why.

Two limits on what this table tells you. It measures sandbox startup and nothing else: not isolation depth, not egress control, not price. And the sequential and staggered tests that ran until 7 August are now retired, so burst is the only startup test still running. ComputeSDK also runs a newer, single-iteration DAX test (clone, install, typecheck), where CreateOS finished fifth of the 20 providers shown on 25 September.

Why one benchmark run is the wrong thing to trust

The ranking changes from run to run. We pulled every burst result from 14 August to 25 September 2026, nine runs in total. Four providers show why a single table misleads.

• Daytona went from 37% success to 100%. On 14 August Daytona posted the fastest median of any provider (0.22 s) and completed only 37% of launches. It stayed at 37% on 21 August, reached 91% on 28 August, fell to 12% with a 44-second median on 4 September, and has held 100% in every run since 10 September. It placed fifth on 25 September. Daytona's own pricing page cites sub-90 ms sandbox creation (daytona.io, September 2026).
• Northflank went from second place to 0% success. Northflank placed second on 14 August at 0.38 s. In six of the eight runs since, it returned no working sandbox, so it is not on the current board. The benchmark does not say why. Treat that as "not measured" as much as "broken", and test it yourself before ruling it out.
• Vercel's 31-second tail is gone. On 14 August, Vercel had a 0.74 s median and a 31 s P99. In every run since, its P99 has stayed between 0.6 s and 1.8 s.
• Cloudflare's median has swung between 0.65 s and 5.7 s. Cloudflare Sandbox measured 4.84 s on 14 August, 0.92 s on 10 September, 5.7 s on 11 September and 0.65 s on 25 September.

Read P99 and success rate before the median, and read more than one run. Tail latency governs how an agent feels, because one agent run makes many sandbox calls in sequence. If one call in a hundred stalls for 31 seconds, a 50-step agent loop hits at least one stall about 40% of the time. Success rate governs whether the agent works at all.

The same rule applies to us. CreateOS placed first on 14 August, second in seven of the eight runs since, and third on 10 September. Its median fell from 339 ms to 125 ms over that period. Isorun has led every burst run since 21 August. Treat every placing in this post as a measurement dated 25 September 2026.

Why "which AI agent sandbox" is the wrong first question

E2B and a self-hosted Firecracker fleet use the same isolation engine and solve different problems. Pick by what your agent does and for how long.

We build the compute layer agents run on, so we see the same mistake repeatedly: teams pick a sandbox tuned for 30-second code-interpreter calls, then try to run 4-hour browser-automation sessions on it and hit a wall. If you want the definition first, we covered what an AI agent sandbox is separately. This post is the buyer's guide.

Four workload shapes cover most agent use.

What launched in September 2026: Docker, DigitalOcean and Prime Intellect

Three vendors shipped AI agent sandboxes in the week of 22 September 2026, and all three chose microVM isolation over containers. Everything below comes from each vendor's own announcement, product page or docs. None of the three is on the ComputeSDK board yet.

Docker Cloud Sandboxes (available now, 24 September 2026)

Docker Cloud Sandboxes run the same sandbox as the local Docker Sandboxes sbx CLI on Docker-managed infrastructure, "with the same microVM isolation and policies as local sandboxes." Per the product page, each sandbox is a microVM with its own kernel and its own private Docker Engine. Network and filesystem policies are enforced at the microVM, credentials are proxied outside the VM, and sbx move carries a sandbox's filesystem between laptop and cloud in either direction.

• Billing: local use is free; cloud compute is metered by the second, from $0.07/hour for Micro (1 vCPU, 2 GB) to $1.12/hour for XL (16 vCPU, 32 GB). You bring your own model key.
• Limits: 1 to 16 vCPUs. Docker states boot times in the "low hundreds of milliseconds".
• Best fit: coding agents such as Claude Code and Codex that start on a developer laptop and need to keep working after the laptop closes.

Mark Cavage, Docker's President, put the isolation choice plainly: "While the containers Docker is known for still have a critical role to play, they weren't designed for the level of isolation AI agents demand."

DigitalOcean Managed Agents (public preview, 22 September 2026)

DigitalOcean Managed Agents is more than a sandbox. It bundles an execution runtime, tool access and inference. The Harness Runtime "combines the functionality of a lightweight microVM, built-in tools like chromium, and a coding sandbox", isolated at the hardware layer, with pause, resume and fork. The Action Gateway gives agents access to 16,000+ tools from 500+ providers through one MCP endpoint, and credentials "never reach the model or the sandbox".

• Billing: CPU at $0.044 per vCPU-hour, billed per second of actual use, with no CPU charge while the agent waits on a model or tool. Memory is $0.0095 per GB-hour and snapshots $0.005 per GiB-month. Auto-pause stops both CPU and memory charges. New users get a $5 credit.
• Harnesses: Claude Code, Codex CLI, OpenCode, Hermes and LangGraph agents run unmodified; custom agents ship as OCI images.
• Vendor-stated speed: resume from pause in 305 ms, in DigitalOcean's own testing.
• Status: public preview, so plan for changes before general availability.

Prime Intellect Prime Sandboxes (generally available, 23 September 2026)

Prime Sandboxes are built for agentic reinforcement learning. Per the docs, a VM sandbox boots an OCI or Docker image as the root filesystem of a hardware-isolated microVM with its own guest kernel, so Docker Compose and kernel-dependent workloads run inside it. Prime contrasts this directly with gVisor containers; we cover that trade-off in microVM, gVisor or container.

• Billing: billed while running, at $0.02 per vCPU-hour, $0.0125 per GiB-hour of memory and $0.0002 per GiB-hour of disk. These are launch rates, valid through 22 December 2026. Prime's docs price 1 vCPU, 2 GiB RAM and 5 GiB disk at $0.046/hour.
• Limits: 1 to 16 vCPUs, up to 64 GiB memory and 128 GiB disk per sandbox; a 60-minute default timeout that can be set to unlimited. Every account starts at 1,024 active sandboxes and 102,400 creations per hour.
• Watch out for: the first launch of a new image converts it to a VM image, which "can take a few minutes". Snapshots, forking and GPU sandboxes are listed as coming soon.

What one vCPU and 2 GiB cost for an hour

List prices normalised to one sandbox with 1 vCPU and 2 GiB of memory running for one hour, as published on 25 September 2026. Free credits, disk and network are excluded.

E2B's figure uses $0.000014 per vCPU-second and $0.0000045 per GiB-second (e2b.dev/pricing). Daytona's uses $0.0504 per vCPU-hour and $0.0162 per GiB-hour (daytona.io/pricing). Modal's uses $0.00003942 per core-second for half a physical core and $0.00000667 per GiB-second (modal.com/pricing). CreateOS is not in this table because CreateOS does not publish a per-hour Sandbox rate; see CreateOS pricing for plans. Price per hour also ignores startup speed and idle billing, which change the real bill for bursty agents. We broke down what sandbox compute actually costs separately.

Which AI agent sandbox is best for short code-interpreter bursts?

The workload: an agent generates a snippet, runs it, reads the result, throws the environment away. Sub-minute, stateless, high volume.

What wins: E2B and Modal are purpose-built here, and both held 100% success on the 25 September run at 1.24 s and 0.91 s median. E2B runs each sandbox on a Firecracker microVM and bills per second: $0.000014/s per vCPU plus $0.0000045/GiB/s of memory (e2b.dev/pricing, September 2026). Its Hobby tier includes a one-time $100 of credits, sessions up to 1 hour and 20 concurrent sandboxes, with no card. Modal's Sandbox product bills $0.00003942/core/sec and $0.00000667/GiB/sec, with $30/month of free compute on the Starter plan (modal.com/pricing, September 2026).

When Cloudflare Sandbox wins instead: if your agent already runs inside Cloudflare Workers, Cloudflare Sandbox runs Python and JavaScript with rich outputs and integrates natively with Workers AI (developers.cloudflare.com/sandbox, July 2026). It runs each sandbox in its own isolated container with a full Linux environment: a container, not a microVM. Its measured median has swung between 0.65 s and 5.7 s across recent runs.

When Prime Sandboxes win instead: if the bursts are RL rollouts or evaluations at thousands of concurrent sandboxes, Prime's 1,024-sandbox default account limit and $0.02 per vCPU-hour launch rate are built for that volume.

Don't overthink this shape. For pure bursts at low volume, an off-the-shelf sandbox API is the right answer, and a few hundred milliseconds of startup will not change your product. It starts to matter when the burst count per agent run climbs into the dozens.

Which AI agent sandbox handles long-lived, stateful sessions?

The workload: an agent works for minutes to hours, holds state, pauses to wait on a model or a human, then resumes warm. Browser automation, multi-step research, coding agents that install packages and iterate.

What breaks: serverless functions. AWS Lambda caps a single invocation at 900 seconds, which is 15 minutes (docs.aws.amazon.com), so it cannot hold a durable session.

What wins: session length and pause/resume decide this one. E2B's Pro tier ($150/month plus usage) extends sessions to up to 24 hours with 100 concurrent sandboxes (e2b.dev/pricing, September 2026). Daytona positions squarely at AI-generated code execution, billed per second after $200 in free compute (daytona.io/pricing, September 2026). DigitalOcean Managed Agents auto-pauses sessions and stops CPU and memory charges while paused. Docker Cloud Sandboxes keep a coding agent running after you detach from your laptop. CreateOS Sandbox pauses, resumes and forks with a memory plus disk snapshot: pause takes about 1.4 s, same-host resume under 0.7 s, and cross-host cold resume 11 to 13 s. Auto-resume triggers on inbound HTTP only.

The state question: if your agent explores branches, you want to fork a running session rather than restart it. That is the difference between paying for one run and paying for ten. CreateOS Sandbox and DigitalOcean Managed Agents both list fork today; Prime lists it as coming soon. We cover forking a running agent's state in detail.

Does an AI agent sandbox need microVM isolation for untrusted code?

The workload: you run code you did not write and cannot trust: user submissions, marketplace plugins, or fully autonomous agents whose output no human reviews. Here isolation is what you are buying.

What wins: microVM-grade isolation. A container shares one host kernel across every tenant, so a kernel bug is a shared fate: one escape reaches the host. A microVM gives each sandbox its own guest kernel behind a hardware virtualization boundary, which is why microVM isolation matters for agents. Firecracker, the open-source engine AWS built for Lambda and Fargate, boots a microVM in about 125 ms and is free under Apache 2.0 (firecracker-microvm.github.io).

Docker, DigitalOcean and Prime all shipped microVMs in September, so the isolation boundary is now common. Compare on what sits around it: egress control, where the sandbox runs, and who holds the credentials.

• E2B runs one Firecracker microVM per sandbox with a per-sandbox egress firewall and domain allow and deny lists. Its runtime is open source under Apache 2.0; E2B Embed runs the full stack on one Linux host as an evaluation package, and production self-hosting is a dedicated deployment in your own account (github.com/e2b-dev/infra).
• Docker Cloud Sandboxes enforce network and filesystem policies at the microVM and keep credentials outside the VM.
• A self-hosted Firecracker fleet is the cheapest per unit, but you own orchestration and patching.
• CreateOS Sandbox runs each sandbox as a Firecracker microVM with its own guest kernel, not a container, with a host-enforced per-sandbox egress allowlist that survives a full guest compromise. That is running untrusted code as systems, not snippets.

Cloudflare Sandbox's container model is a weaker fit for genuinely hostile code.

Which platform fits full app deployment?

The workload: the sandbox is not the end state. The agent builds something that has to run as a real service, with a database, networking between components, environment variables, human approval before irreversible actions, and separate staging and production environments.

What wins: an execution layer. CreateOS is built for agent workloads that graduate from "run this snippet" to "deploy and operate this app". That means managed PostgreSQL and MySQL, GPU compute, 14 framework runtimes, 150+ templates, human approval gates where you set them, and multi-environment deploys, with a $0 free tier and no card required.

DigitalOcean Managed Agents sits beside its Managed Databases and storage, with tools and inference from the same vendor.

For teams that need sessions to stay on their own infrastructure, CreateOS Sandbox supports bring your own bucket (your S3, R2 or MinIO) and bring your own compute (the same API on your machines).

When is CreateOS the wrong AI agent sandbox?

Placing near the front on startup time does not make CreateOS right for every workload, and five cases stand out.

• You need a bare snippet runner. If all you do is execute a snippet and read stdout, CreateOS is overkill and E2B or Modal is the better call.
• You are already inside another platform's ecosystem. If your agent runs in Cloudflare Workers, Cloudflare Sandbox's native Workers AI integration is worth more to you than startup time.
• Your coding agents live on developer laptops. If the goal is to sandbox Claude Code or Codex locally and move the same sandbox to the cloud, Docker's sbx move is built for exactly that.
• You are training models with RL. For tens of thousands of rollouts across diverse images, Prime Sandboxes ship with RL integrations and a registry of more than 365,000 prebuilt environments.
• Your procurement requires certifications. SOC 2 and ISO are on the CreateOS Sandbox roadmap, not held today. Bring your own compute keeps sessions on your machines in the meantime.

One further caution about the benchmark, including our position in it: CreateOS has placed first, second and third on burst runs in the last six weeks, and the suite re-runs automatically. Check the live board on the day you decide.

The AI agent sandbox decision framework, in one table

E2B appears in three rows because it is a strong general-purpose sandbox. The September entrants each claim one row sharply: Docker the laptop-to-cloud path, Prime RL scale, DigitalOcean the bundled runtime, tools and inference. CreateOS appears where the sandbox has to become infrastructure. Pick on the row that matches your workload.

Common questions

What is the best AI agent sandbox in 2026?

There is no single best one; the right pick depends on workload shape. On ComputeSDK's 25 September 2026 burst run, Isorun led at 44 ms median and CreateOS placed second at 125 ms, both at 100% success. E2B and Modal remain strong for stateless bursts. September added Docker Cloud Sandboxes for laptop-to-cloud coding agents, DigitalOcean Managed Agents (public preview) and Prime Sandboxes for RL scale.

Which AI agent sandbox has the fastest startup time?

Isorun, on ComputeSDK's burst test: it has led every burst run since 21 August 2026 and measured a 44 ms median on 25 September. CreateOS placed second that day at 125 ms with 100% success. Placings move between runs, so check the live board. Docker, DigitalOcean and Prime Intellect are not benchmarked there yet; their speed figures are vendor-stated.

Is the ComputeSDK sandbox benchmark independent?

It is operated by Snelling, LLC, and publishes its code and raw results on GitHub so anyone can reproduce them. Its README lists sponsors including Latitude, Google Cloud Run, Browserbase, Tigris, Neon, GitBook and Namespace, and says sponsors cannot influence results; several sponsors are themselves benchmarked. CreateOS is not a sponsor. We place near the front, so read our summary with that in mind.

What is a good E2B alternative?

It depends on why you are leaving E2B. For faster measured startup, Isorun and CreateOS both measured under 130 ms on the 25 September burst run against E2B's 1.24 s. For the lowest listed hourly price, Prime Sandboxes. For coding agents that move between laptop and cloud, Docker Cloud Sandboxes. For sandboxes that also deploy databases and full apps, CreateOS. E2B's runtime is also open source.

Do AI agent sandboxes need microVM isolation or are containers enough?

Containers are enough for your own trusted agent code. For untrusted third-party or fully autonomous code, use microVM isolation. A container shares the host kernel across tenants, so one kernel-level escape compromises the host. A microVM gives each sandbox its own guest kernel behind a hardware boundary. Docker, DigitalOcean and Prime Intellect all launched microVM sandboxes in September 2026.

How much does an AI agent sandbox cost?

For 1 vCPU and 2 GiB for one hour, list prices on 25 September 2026 were about $0.045 on Prime Sandboxes (launch rate), $0.063 on DigitalOcean Managed Agents, $0.07 on Docker Cloud Sandboxes, $0.083 on E2B and Daytona, and $0.119 on Modal. Free credits differ: E2B $100 one-time, Daytona $200, Modal $30/month, DigitalOcean $5. CreateOS has a $0 free tier with no card.

Why does P99 latency matter more than median for AI agents?

Because a single agent run makes many sandbox calls in sequence, so rare stalls compound. On 14 August 2026 Vercel measured a 0.74 s median alongside a 31 s P99. At that tail, a 50-step agent loop hits at least one 31-second stall about 40% of the time. Vercel's P99 has since stayed between 0.6 s and 1.8 s, which is why one run is not enough.

When is a bare sandbox API not enough?

A bare sandbox API is not enough when the agent's output has to become a running service. If you need a managed database, networking between components, environment variables, approval gates before irreversible actions, and separate staging and production environments, you are assembling infrastructure around the sandbox. An execution layer like CreateOS, or DigitalOcean's bundled Managed Agents, provides that stack.

About the author

Naman Kabra is the founder of CreateOS, the execution layer for AI apps and agents built on the NodeOps network. He works on the compute, isolation, and deployment layer that agent workloads run on, and writes about the real economics of running sandboxed code at scale. He has also written on what a sandbox escape actually looks like for an AI agent. Connect on LinkedIn.

Next step

Sorting your agent into a workload shape and it lands on "full app deployment"? Start free on the CreateOS pricing tier and run your own startup test against your workload, or see how the platform is built for agent workloads.