Best Code Execution Sandboxes for AI Agents (2026)

A code execution sandbox is an isolated, disposable machine where code your AI agent wrote runs without reaching your hosts, your data or other tenants. Your app creates one through an API, sends it code, reads back the output and files, and deletes it when the task ends. This guide compares the main options in 2026. One of them, PandaStack, is our product.

Key takeaways

• For code a model wrote, use a boundary with its own kernel: a microVM (Firecracker or Kata Containers) or, one step down, gVisor. A plain container shares the host kernel with every other tenant.
• Firecracker is the most common choice among the platforms here. E2B, Vercel Sandbox, Blaxel and PandaStack each say so in their own docs. Modal defaults to gVisor, Northflank uses Kata Containers or gVisor, and Daytona and Cloudflare's Sandbox SDK run containers by default.
• Persistence varies more than isolation. E2B, Blaxel, Daytona's VM classes and PandaStack can save a sandbox's memory as well as its disk and resume it later. Daytona's default container class keeps only the filesystem across a stop.
• Few of these are open source end to end. E2B's runtime and PandaStack are Apache-2.0 and document how to run them yourself, though E2B labels its single-machine package as an evaluation setup. Daytona's public repository has not been maintained since June 2026. Northflank and E2B's enterprise offering run inside your own cloud account instead.
• PandaStack's published numbers are 49ms p50 to create a sandbox and about 400ms to fork a running one. It fits teams that want an open-source Firecracker platform with forking and managed Postgres. It doesn't fit if you need GPUs or a SOC 2 report today.
• No roundup settles this for you. Shortlist two, then measure create time, forking and persistence on your own workload.

What is a code execution sandbox?

A code execution sandbox is an isolated runtime, in practice a short-lived virtual machine or a hardened container, for code you don't trust. For an AI agent that means every command and script the model writes. The agent loop calls it many times per task, so its start time shapes how your product feels and its billing model shapes your margin.

Five things separate a sandbox you can hand to an agent from 'somewhere to run code':

• An isolation boundary that assumes the code is hostile: a guest kernel behind hardware virtualization (a microVM), a user-space kernel (gVisor), or the shared host kernel (a container).
• A real Linux userland: a filesystem, a shell, package managers and long-running processes, so the agent can pip install, start a server or run a test suite.
• A network policy: egress to package registries and APIs, and no path to other tenants or to the cloud metadata endpoint.
• A lifecycle API: create, exec, read and write files, snapshot or pause, delete. It has to be fast enough to call on every task.
• Metering and limits: per-second billing, lifetimes and concurrency caps, so a runaway loop doesn't turn into a runaway bill.

'AI agent sandbox' and 'LLM code execution sandbox' describe the same thing from the agent's side. The longer explainer is What Is an AI Agent Sandbox?, and The Code Isolation Hierarchy ranks the isolation options in more depth.

Code execution sandboxes compared (2026)

The table sticks to three qualitative questions: what isolates each sandbox, whether you can run the platform yourself, and what state survives between sessions. Each competitor cell paraphrases that vendor's own docs or site, checked on 23 September 2026. Where a page didn't say, the cell says so. That means we don't know, not that the feature is missing.

Sources, checked 23 September 2026. E2B: github.com/e2b-dev/runtime and docs.e2b.dev/sandbox/persistence. Daytona: daytona.io/docs/en/sandboxes and github.com/daytonaio/daytona. Modal: modal.com/docs/guide/security and modal.com/docs/guide/vm-sandboxes. Vercel: vercel.com/docs/sandbox. Northflank: northflank.com/product/sandboxes. Blaxel: blaxel.ai/platform/sandboxes. Cloudflare: developers.cloudflare.com/sandbox. Fly.io: fly.io/sprites. PandaStack: docs.pandastack.ai/docs/security/isolation-model, docs.pandastack.ai/docs/self-host/overview and docs.pandastack.ai/docs/sandboxes/snapshots-and-forks.

The criteria that actually separate them

Almost every sandbox in this market will run a Python script and hand you back stdout. That baseline tells you nothing. The differences that decide whether a tool fits your agent live in six places: the isolation model, hosted vs self-host, cold-start latency, forking and persistence, platform breadth, and pricing posture. Work out which one is forcing your hand before you compare anything, because the right answer changes completely depending on which matters to you.

Criterion 1: the isolation model

When an agent runs code it wrote itself, you're running untrusted code by definition. The model is not an authority you can trust with your host. Isolation strength is therefore the criterion that matters most, and the one marketing blurs most often. There are three broad models, in increasing order of strength, covered in depth in The Code Isolation Hierarchy.

• Containers (namespaces, cgroups and seccomp): fast and cheap, but every container shares the one host kernel, so a kernel-level escape is a host compromise. Many products sold as 'sandboxes' are hardened containers. That's fine for code you've reviewed and riskier for arbitrary agent output (Why Docker Isn't a Sandbox).
• User-space kernel (gVisor): an application kernel written in Go runs in user space and handles the sandbox's system calls, so they don't hit the host kernel directly. It's a real step up from a plain container, with compatibility and performance trade-offs that depend on the workload.
• Hardware-virtualized microVMs (Firecracker, Kata Containers): each sandbox gets its own guest kernel, isolated by KVM. Guest code never runs against the host kernel, and the exposed surface is a much smaller virtual device model. This is the right default for arbitrary untrusted code; see Firecracker vs Docker: which one runs untrusted code? and What is a microVM? Firecracker, isolation, and why agents need it.

Isolation models side by side

Snapshot restore removes the old trade-off between VM isolation and start time. Instead of booting a kernel on every create, a platform restores a VM that has already booted. PandaStack does this on every create, and E2B's runtime README describes its creates, resumes and forks the same way. Don't read 'microVM' as 'unbreakable', though. KVM and VMMs have had bugs. The accurate claim is a much smaller, better-audited attack surface than the full system-call interface a container shares. gVisor vs Firecracker: Which Isolation for Agents? and Firecracker vs Kata vs gVisor: three isolation models go deeper.

Where the field lands, per each vendor's own docs. E2B runs one Firecracker microVM per sandbox. Vercel Sandbox runs each sandbox in a Firecracker microVM with its own filesystem and network. Blaxel runs every sandbox as an individual microVM built on Firecracker. PandaStack runs unmodified Firecracker with a Linux 5.10 guest kernel. Modal says its compute jobs are containerized and virtualized with gVisor, and its VM Sandboxes (beta) run on a full virtual machine instead. Northflank runs every workload in its own microVM with Kata Containers or gVisor. Daytona's sandboxes run as Linux containers by default, with Linux VM and Windows VM classes available. Cloudflare's Sandbox SDK gives each sandbox its own isolated container. The Fly.io Sprites page doesn't name its isolation technology, so neither do we. For arbitrary agent code, microVM-class isolation clears the bar, gVisor is a meaningful middle, and a plain shared-kernel container generally doesn't.

Criterion 2: hosted vs self-host

This is the cleanest fork in the road, and often the reason a team looks past the obvious hosted choice. The question is concrete: where does the agent's code physically run, and who operates the machines? 'Self-hosted' is badly overloaded here, so keep three things apart.

• Hosted service: you call an API, code runs on the vendor's machines, and you never touch a host. Modal, Vercel Sandbox, Blaxel, Fly.io Sprites and Cloudflare's Sandbox SDK work this way, and none of the pages we checked documents running the platform yourself. This is the least operational work.
• Bring-your-own-cloud (BYOC): the vendor runs the compute inside your cloud account. Northflank offers sandboxes inside your own VPC, and E2B runs dedicated deployments inside your account through its enterprise offering. You get data locality, but the vendor still operates the platform, so it isn't the same as self-hosting.
• Open source and self-hostable: code you deploy on machines you control. E2B's runtime is Apache-2.0 and ships a single-machine package, Terraform for GCP and a Kubernetes manifest, which E2B describes as an evaluation package rather than a production pattern. PandaStack is Apache-2.0 with self-host guides for GCP, AWS bare metal, on-prem hardware and a single Linux box. Daytona's public repository is still available but hasn't been maintained since June 2026, when development moved to a private codebase. The building blocks (Firecracker, Kata Containers, gVisor) are open source too. Licenses change, so check each repo.

PandaStack's self-host path runs the same services as the hosted platform: a control plane (the API plus Postgres) and an agent on each KVM-capable Linux host, which runs the Firecracker microVMs. Both SDKs take their API URL from configuration, so the same code targets the hosted service or your own control plane. The counterweight applies to every self-hostable option here: you take on KVM hosts, an agent fleet, networking and snapshot storage. Without an infra team, a hosted service is less work, and that's a legitimate reason to stay hosted. Best Self-Hosted Code Execution Sandboxes in 2026 covers this side of the market.

Criterion 3: cold-start and create latency

Inside an agent loop, the time create() blocks decides whether a tool feels instant or broken, and it compounds over a long trajectory. PandaStack keeps no warm pool of idle VMs. Every create restores a baked Firecracker snapshot that already contains a booted kernel and a running guest agent, so starting a sandbox means restoring memory and resuming. On our published run against the live API (17 June 2026, 50 consecutive creates of the base template) that measured 49ms p50, covering the scheduler claim, snapshot restore, network assignment and the guest readiness probe. It's a server-side number, so your client adds its own network round trip. The slow path is the first boot of a brand-new template, a cold boot that bakes the snapshot. The method is on the benchmarks page.

Most vendors publish their own startup figures, and we don't reprint them here. They measure different things: create versus resume from pause, server-side versus client wall-clock, a warm pool versus a real restore, a trivial image versus your template. The number worth trusting is the one you measure yourself, on your own template, in your own region, from where your agent runs.

Criterion 4: forking, snapshots and persistence

Forking is where microVM snapshots pay off, and platforms differ here, so test it directly rather than trusting a feature matrix. PandaStack has three primitives. snapshot() captures a running sandbox's memory, device state and disk and returns an id you can pass to create(from_snapshot=...). fork_tree(count=N) snapshots a running sandbox once and restores N children from it in parallel, each with the parent's memory and disk and its own network identity; the published figure is about 400ms per fork on the same host. A plain fork() is different: it copies only the disk and cold-boots the child, so running processes and in-memory state don't carry over. Children share the snapshot's memory pages copy-on-write (MAP_PRIVATE) and each rootfs is an XFS reflink clone, so a child pays only for what it changes. The workload this fits is tree search, agent rollouts, or 'try five fixes and keep the one that passes': warm the environment once, fork it N ways, then promote the winner and delete the rest. The walkthrough is Snapshots and Forks: Copy-on-Write for Running Machines.

Persistence is the other half. PandaStack can hibernate a sandbox, which saves memory and disk, stops the VM and stops compute billing, and the next request wakes it. Elsewhere, per each vendor's docs: E2B's pause saves filesystem and memory and keeps paused sandboxes until you delete them. Blaxel drops idle sandboxes to standby with memory and filesystem snapshotted. Vercel Sandbox persists sandboxes by default, saving state when they stop. Daytona's VM classes support pause, fork and memory snapshots, while its container class keeps the filesystem across a stop and can archive it to object storage. Modal offers filesystem, directory and memory snapshots. Fly.io Sprites keep a disk that survives sleep and take whole-filesystem checkpoints. These are different bets: long-lived stateful machines versus cheap, identical, disposable creates. Weight them by the shape of your agent's work; PandaStack vs Fly.io Sprites: Firecracker Sandboxes is one head-to-head on exactly this.

Criterion 5: platform breadth

Sandboxes are short-lived by design, so the interesting question is what holds state and structure around them. Some tools are deliberately focused: a sandbox primitive and nothing more, which is a real strength if all you need is to run code, since it's simpler to reason about and easier to swap out. Others bundle a wider platform, consolidating onto one substrate and one bill but coupling you more tightly. Modal positions itself around serverless AI and ML compute, and Northflank is a full managed cloud platform where sandboxes are one feature among many. PandaStack runs everything on one microVM substrate:

• Managed PostgreSQL 16, where each database is its own Firecracker microVM with a durable volume, so a per-tenant database is a first-class object next to your sandboxes (managed Postgres).
• Git-driven app hosting: connect a repo, deploy blue-green, and scale to zero when idle (app hosting).
• Serverless functions with cron schedules, persistent volumes for state beyond the copy-on-write rootfs, and first-party templates (base, code-interpreter, agent, browser, postgres-16, claude-agent) so common agent shapes start ready instead of from a bare OS. The data-analysis path is on the code interpreter page.

More features don't win by themselves; it's a fit question. If you're building an AI product that also needs a database per tenant and a place to host the app, one substrate and one bill is the argument for breadth. If all you need is to run code, that breadth is irrelevant and a focused tool is cleaner. Decide which side of the line you're on first, because breadth you don't use is just surface area you have to learn.

Criterion 6: pricing posture

Competitor prices are left out here because they change often. Read each vendor's pricing page and date what you find. PandaStack's rate card, from the pricing page: $0.054 per active vCPU-hour and $0.0162 per working-set GiB-hour, metered per second, with network egress not billed and no compute billed while a sandbox is hibernated. The Free plan includes $5.40 of usage credit a month. The posture shapes your real bill more than the per-unit rate does:

• Metered usage is the norm: most hosted options bill some mix of CPU, memory, storage and egress, usually per second, usually with a free tier or credit. The headline rate matters less than what each meter actually counts.
• Idle treatment can dominate: agent workloads spend much of their wall-clock time waiting on model calls. Billing only CPU actually burned, or scaling a sandbox to zero while it sleeps, costs far less for bursty loops than billing provisioned cores for every second an idle VM exists. This is where bills diverge most.
• Self-hosting changes the equation: with an open-source option you trade a per-second hosted bill for your own hardware plus operational cost. At low volume the hosted bill almost always wins; at scale, or under a data-residency constraint, owning the substrate can flip it. Run the numbers at your projected volume.

A worked example on PandaStack's rates: an agent session holds a sandbox for an hour and keeps 2 GiB resident, and its code burns 6 CPU-minutes because the rest of the hour is spent waiting on the model. That's 0.1 active vCPU-hours × $0.054 = $0.0054, plus 2 GiB × 1 hour × $0.0162 = $0.0324, or about $0.038 for the session. A meter that counted one provisioned vCPU for the whole hour would charge ten times that CPU line for the same work, which is why the billing basis matters more than the headline rate. Run the same arithmetic against each vendor's current pricing page.

Run LLM-generated code in a sandbox (Python and TypeScript)

This is the whole integration for the common case: create a sandbox, write the code your model returned into it, run it under a time limit, read the result, and delete the sandbox. Both SDKs read PANDASTACK_API_KEY from the environment. Set PANDASTACK_API and the same code talks to a self-hosted control plane instead of the hosted API.

# pip install pandastack      (reads PANDASTACK_API_KEY from the environment)
from pandastack import Sandbox

llm_code = """
import platform
print("guest kernel:", platform.release())
print(sum(range(10_000)))
"""  # whatever your model returned

# One Firecracker microVM. Leaving the block deletes it.
with Sandbox.create(template="code-interpreter") as sb:
    sb.filesystem.write("/tmp/main.py", llm_code)
    # Enforce the wall-clock limit inside the guest with coreutils `timeout`.
    result = sb.exec("timeout 20 python3 /tmp/main.py")
    print(result.exit_code)             # 124 means the timeout fired
    print(result.stdout or result.stderr)

// npm install @pandastack/sdk   (reads PANDASTACK_API_KEY; ESM, Node 18+)
import { Sandbox } from "@pandastack/sdk";

const llmCode = [
  "import platform",
  'print("guest kernel:", platform.release())',
  "print(sum(range(10_000)))",
].join("\n"); // whatever your model returned

const sb = await Sandbox.create({ template: "code-interpreter" });
try {
  await sb.filesystem.write("/tmp/main.py", llmCode);
  // Enforce the wall-clock limit inside the guest with coreutils `timeout`.
  const result = await sb.exec("timeout 20 python3 /tmp/main.py");
  console.log(result.exitCode); // 124 means the timeout fired
  console.log(result.stdout || result.stderr);
} finally {
  await sb.kill(); // deletes the microVM
}

The time limit runs inside the guest: coreutils timeout kills the process and exits with 124 when it fires. The printed kernel release is the sandbox's own guest kernel, not your host's. For a one-line snippet there's a shortcut, run_code(code) in Python or runCode(code, "python") in TypeScript. For a notebook-style loop, where variables survive between calls and charts come back as images, use create_code_context() in Python or createCodeContext() in TypeScript on the code-interpreter template. To try several fixes in parallel from one warmed sandbox, call sb.fork_tree(count=5) in Python or sb.forkTree({ count: 5 }) in TypeScript.

The field, vendor by vendor

A short profile of each option, with a link to the head-to-head where one exists. The same rule applies as in the table: numbers only for PandaStack, and everything else from the vendor's own pages.

PandaStack

The open-source (Apache-2.0) Firecracker platform. Sandboxes, managed Postgres 16 (each database in its own microVM), git-driven app hosting and serverless functions run on one substrate, hosted or on your own KVM hosts. Published numbers: 49ms p50 create and about 400ms per fork. Best for: agents that branch, teams that want to own the substrate, and products that need a database per tenant. Watch out for: no GPUs on the hosted service; no audited SOC 2, ISO 27001 or HIPAA attestation yet (see the security page); open egress by default with no outbound allowlist, though VM-to-VM traffic, the cloud metadata range and mining-pool ports are blocked; and Free sandboxes are deleted an hour after creation.

E2B

A focused sandbox API built on Firecracker. Its runtime (API, orchestrator, in-VM agent and template builder) is Apache-2.0 on GitHub. Pause saves filesystem and memory, paused sandboxes stay until you delete them, and a fork starts new sandboxes from a checkpoint of a live one while the original keeps running. The single-machine self-host package is labelled for evaluation; production in your own cloud is a dedicated deployment through E2B's enterprise offering. Best for: a mature default with nothing to operate. Compare: PandaStack vs E2B: Choosing an AI Sandbox Provider and E2B Alternatives: A Guide to AI Code Execution Sandboxes.

Daytona

Sandboxes with container, Linux VM, Windows, GPU and macOS options. The default class is a Linux container; the VM classes add pause and resume, fork and memory snapshots. Daytona's public GitHub repository stopped being maintained in June 2026, when core development moved to a private codebase, so treat it as a hosted service. Best for: Windows or GPU sandboxes next to Linux ones. Pick a VM class if you need hardware isolation. Compare: PandaStack vs Daytona for AI Sandboxes.

Modal

A serverless compute platform for AI and ML with a Sandbox primitive. Sandboxes run on gVisor by default; VM Sandboxes, in beta, run on a full virtual machine with a real Linux kernel and are CPU-only for now. Modal offers filesystem, directory and memory snapshots. It's hosted only, and its Python client and CLI are open source. Best for: sandboxes that sit next to GPU jobs on one platform. Compare: PandaStack vs Modal: which for AI code execution?.

Vercel Sandbox

Firecracker microVMs built into the Vercel platform, with JavaScript and Python SDKs and a CLI. Sandboxes are persistent by default: state is saved when a sandbox stops and it resumes where it left off, and snapshots let later runs skip dependency installs. It's hosted only; the SDK and CLI source is on GitHub. Best for: apps already on Vercel. Compare: PandaStack vs Vercel Sandbox: MicroVM Code Execution.

Northflank

A managed cloud platform where sandboxes sit next to services, databases and GPU workloads. Each workload runs in its own microVM with Kata Containers or gVisor, on Northflank's infrastructure or inside your own VPC. Sandboxes are ephemeral by default, can attach persistent volumes, and have no forced time limit. Best for: one managed platform, possibly in your own cloud, where a vendor-operated control plane is fine. Compare: PandaStack vs Northflank: Sandboxes for AI Agents.

Blaxel

Firecracker microVMs built around standby. Idle sandboxes are suspended with memory and filesystem snapshotted and resume on the next call, and Blaxel says its sandbox API is open source. Best for: long-lived agent environments that sit idle most of the time. Compare: PandaStack vs Blaxel: agent sandbox platforms compared.

Cloudflare Sandbox SDK

Built on Cloudflare Containers: each sandbox is its own isolated container with a full Linux environment, driven from a Worker. It requires the Workers Paid plan, and data that must outlive a sandbox goes in a mounted S3-compatible bucket. Best for: apps already on Workers and Durable Objects, where container isolation fits the threat model. Compare: Cloudflare Sandbox SDK vs E2B for Agent Code and Vercel Sandbox vs Cloudflare's Sandbox SDK.

Fly.io Sprites

Persistent Linux computers for agents: a disk that survives sleep, whole-filesystem checkpoints you can restore, and an HTTPS URL per Sprite that wakes it on request. The Sprites page doesn't name its isolation technology. Compare: PandaStack vs Fly.io Sprites: Firecracker Sandboxes.

Open-source building blocks

If you'd rather build the platform yourself, start from Firecracker, Kata Containers or gVisor and add the scheduler, networking, snapshot storage and API on top. The roundup is Best Open-Source Sandboxes for Running Untrusted Code.

Where PandaStack fits

To pull the threads together: every PandaStack sandbox is a Firecracker microVM with its own Linux 5.10 guest kernel under KVM and a minimal virtio device model (block, net, vsock). Each VM gets its own network namespace and TAP device on a unique /30, and top-of-chain firewall rules drop VM-to-VM traffic and the link-local range that holds the cloud metadata endpoint. Every create is a snapshot restore (49ms p50 on the published run, no warm pool), and an optional streaming mode pages guest memory on demand from object storage, so a host can start a VM before the whole memory image is local. You drive it from the pandastack Python package, the @pandastack/sdk TypeScript SDK or the pandastack CLI; all three read PANDASTACK_API_KEY and a configurable API URL. It's the right pick when you want an open-source Firecracker platform you can own end to end. It's the wrong pick if you need GPUs, need a SOC 2 report today, or have no appetite for infrastructure and a hosted-only service would do.

Which code execution sandbox should you pick?

Map your situation to the option, not the reverse:

• Pick E2B when you want a mature, focused sandbox API with nothing to operate, and an Apache-2.0 runtime you can read.
• Pick Vercel Sandbox when your app already runs on Vercel and you want sandboxes that persist by default.
• Pick Modal when your real workload is AI and ML compute, such as GPU jobs or batch inference, and the sandbox is incidental.
• Pick Daytona when you need Windows or GPU sandboxes next to Linux ones. Choose a VM class if you need hardware isolation, since the default class is a container.
• Pick Northflank when you want sandboxes, apps, databases and GPUs on one managed platform, possibly in your own VPC, and a vendor-operated control plane is fine.
• Pick Blaxel when your agents sit idle most of the time and must come back with memory intact.
• Pick Cloudflare's Sandbox SDK when your app lives on Workers and Durable Objects and container isolation fits your threat model.
• Pick Fly.io Sprites when persistence is the core requirement: long-lived environments that keep their disk across sessions.
• Pick an open-source building block (Firecracker, Kata Containers, gVisor) when you want maximum control and will build the platform layer yourself; see Best Open-Source Sandboxes for Running Untrusted Code.
• Pick PandaStack when you want an open-source Firecracker platform you can self-host, with snapshot restore on every create, fork_tree() for branching agents, and managed Postgres on the same substrate.

Where the strong options differ is mostly above the isolation boundary: boot path, fork semantics, persistence, platform breadth, and whether you can own the substrate. Concentrate your evaluation there, once you've confirmed which isolation class a vendor gives you by default.

The bottom line

There is no single best code execution sandbox for AI agents; there's a best one for your constraints. Start with isolation: for code a model wrote, use a microVM or at least gVisor, and check which class a vendor gives you by default. Past that, the options differ on whether you can self-host, how fast create() returns, whether forking is first-class, what state survives between sessions, and how the bill is shaped. Start from the constraint forcing your hand, shortlist two, and prototype against both before you commit. PandaStack's bet, for the record, is an Apache-2.0 Firecracker platform with snapshot restore on every create (49ms p50), fork_tree() for branching (about 400ms per fork), per-sandbox networking and managed Postgres, which you can run on your own hardware. If that matches your constraints, benchmark it against the field and keep us honest.