Best Secure Code Execution APIs in 2026

A secure code execution API has a deceptively simple contract: you POST some code — usually written by an LLM, usually code you'd never run on your own machine — and you get back stdout, stderr, and an exit code. The whole value is in the word 'secure.' Anything can shell out to a subprocess; the hard part is doing it when the code is adversarial-by-default, at API latency, without the code reaching your host, your other tenants, or the open internet. In 2026 there are a lot of credible APIs that offer this drop-in shape, which is good for buyers and confusing when every vendor's own blog ranks itself first. This is a best-of that tries to be an honest broker: real decision criteria, a fair pass over the field, and specifics only where I can stand behind them.

The field covered here: PandaStack (our project — open-source Firecracker microVMs, self-hostable, exposed as a code-execution API), E2B, Modal, Daytona, Vercel Sandbox, and Fly.io, plus the open-source isolation building blocks — gVisor, Kata Containers, and WASM runtimes — that several of these APIs are built on and that you can also wire up yourself. Rather than rank these into a leaderboard that ignores your workload, we'll walk the six decisions that actually separate a secure code execution API from a subprocess with good marketing, then give a per-option 'pick this when…' summary. The broader sandbox roundup is /blog/best-code-execution-sandboxes; the isolation-first buyer's guide is /blog/best-microvm-platforms-2026.

What 'secure' has to mean for an execution API

Every API in this market will run a Python script and hand you back stdout. That baseline tells you nothing about security. When an LLM writes the code, you are running untrusted code by definition — the model is not an authority you can trust with your host, and 'the prompt asked it nicely to stay in bounds' is not a security boundary. So the question 'is this API secure?' decomposes into a handful of concrete sub-questions: what stops the code from escaping to the host, what stops it from reaching other tenants, what stops it from calling out to the open internet or exfiltrating data, and what happens to the environment after the run. Those decompose into six decision criteria. Work out which one is forcing your hand before you compare anything — the right answer changes completely depending on which matters most to you.

Criterion 1: the isolation model (the actual security boundary)

This is the criterion that makes an execution API 'secure' rather than merely 'remote,' and the one most often blurred by marketing. It answers: when the untrusted code runs, what is the wall between it and everything you care about? There are three broad models, in increasing order of strength, covered in depth in /blog/code-isolation-hierarchy.

• Containers (namespaces + cgroups + seccomp): fast and cheap, but every container shares the one host kernel, so a kernel-level escape is a host compromise. Many APIs marketed as 'sandboxes' are really hardened containers — fine for trusted code, riskier for arbitrary agent output (/blog/why-docker-is-not-a-sandbox).
• User-space kernel (gVisor): a second kernel in user space intercepts guest syscalls so they don't hit the host kernel directly, shrinking the attack surface without a full VM — a real step up from a plain container, with workload-dependent compatibility and performance trade-offs (/blog/gvisor-vs-firecracker).
• Hardware-virtualized microVMs (Firecracker, Kata): each execution gets its own guest kernel, isolated by KVM. Guest code never touches the host kernel; the exposed surface is the much smaller, better-audited VMM. The right default for arbitrary untrusted, LLM-generated code — see /blog/firecracker-vs-docker and /blog/what-is-a-microvm.
• WebAssembly (Wasmtime, WasmEdge, and friends): a deny-by-default capability model where the guest can only touch what you explicitly grant, with millisecond startup — excellent for pure, sandboxable compute, but with a narrower syscall/library surface, so full 'run any pip package' workloads often don't fit (/blog/wasm-vs-microvm).

Where the field lands, from each vendor's own primary sources: E2B and Vercel Sandbox both run executions as Firecracker microVMs (E2B in its infra docs; Vercel states it plainly and links the project), as does PandaStack. Fly.io's Machines are Firecracker-based, and its newer sandbox/agent offerings sit on that same substrate — consistent with Fly Machines, though confirm on Fly's docs. Modal uses gVisor — confirmed in its own security docs — a different bet from a hardware-virtualized VM. Daytona describes a dedicated kernel and complete isolation (VM-like) without naming a hypervisor, so I won't name one. The honest spine: for arbitrary LLM-generated code, microVM-class isolation (Firecracker or Kata) clears the bar, gVisor is a meaningful middle, WASM is a strong fit for a narrower slice of workloads, and a plain shared-kernel container generally does not clear the bar.

Criterion 2: hosted vs self-host (where the code physically runs)

For a security API this is not just an ops-convenience question — it's a data-residency and trust-boundary question. Where does the untrusted code physically execute, and who operates the machines it runs on? For a regulated workload, or one that touches customer data, 'runs on the vendor's infrastructure' can be a non-starter regardless of how good the isolation is. The word 'self-hosted' is badly overloaded here, so be precise about three distinct things.

• Hosted-only managed service: you call an API, code runs on the vendor's infrastructure, you never touch a host. Modal and Vercel Sandbox are hosted-only — neither documents a self-host path. The least operational work, full stop.
• Bring-your-own-cloud (BYOC): a proprietary control plane manages compute in your cloud account. Some vendors offer this — it gives data locality, but it isn't source-available software you run end-to-end, so don't conflate it with open-source.
• Genuinely open-source and self-hostable: source you deploy on your own machines. E2B (Apache-2.0), Daytona (AGPL-3.0; managed/self-host/hybrid), the OSS isolation runtimes (gVisor, Kata, WASM runtimes), and PandaStack all live here — with the usual caveat that licenses change, so confirm in each repo.

PandaStack's core is Apache-2.0 and designed to self-host on your own Linux KVM hosts — anything with /dev/kvm. You run the control-plane API and a per-host agent; executions run entirely on your infrastructure. There's a hosted offering too, but self-host is first-class: same binaries, same agent, a configurable SDK base URL so identical code points at either. The honest counterweight applies to every self-hostable option here: self-hosting is real operational weight — KVM hosts, an agent fleet, networking, snapshot storage. If you don't have an infra team or the appetite to grow one, a hosted-only API is genuinely less work, and that's a legitimate reason to stay hosted.

Criterion 3: cold-start and create latency

For an execution API, latency is the whole feel of the product: an agent loop blocks on create() dozens of times per task, and the cost compounds across a long trajectory. PandaStack's design choice is specific, because it's where our numbers come from: there is no warm pool of idle VMs. Every create restores a baked Firecracker snapshot on demand — a snapshot that already contains a booted kernel, a running guest agent, and an open network stack, so 'starting' an execution environment is really 'restore memory pages and resume.' That lands at ~179ms p50, ~203ms p99, with the restore step itself around 49ms. The only slow path is the first-ever spawn of a brand-new template, which cold-boots in about 3s and bakes the snapshot; every create after is on the fast restore path (mechanics in /docs/internals/snapshot-restore).

Most other APIs also advertise fast startup, several quoting sub-second or millisecond figures. I deliberately won't reprint competitor latency numbers, because cold-start is the metric easiest to mis-measure across vendors: warm pool versus true cold boot, snapshot resume versus full boot, your region versus theirs, your real template versus a trivial one. The only number you should trust is the one you measure yourself — on your template, in your region. Treat every vendor's headline figure (including how you read ours) as a hypothesis to benchmark, not a settled fact. Note too that a WASM runtime you wire up yourself will beat any microVM on raw cold-start, at the cost of the narrower workload surface from Criterion 1.

Criterion 4: forking and copy-on-write state

Forking is where the microVM model pays off in ways containers can't easily match, and a real point of divergence between execution APIs — so evaluate it directly, not from a feature matrix. PandaStack exposes full snapshots and forks as first-class primitives. A full snapshot captures the whole machine — guest memory plus rootfs. A fork clones a running execution environment via copy-on-write: guest memory shared through MAP_PRIVATE (the kernel copies a page only when it's written), the rootfs cloned with an XFS reflink — an O(metadata) operation where data stays shared until something writes (dm-snapshot is also supported). A same-host fork completes in 400–750ms; a cross-host fork (download plus restore) runs 1.2–3.5s. The workload this fits: tree-search, agent rollouts, 'run this LLM patch five ways and keep the one that passes tests' — warm the environment once (dependencies installed, dataset loaded, REPL hot), then fork it N times in parallel without re-running setup. The conceptual walkthrough is /blog/snapshot-and-fork-explained.

Persistence is the flip side of forking, and the field splits philosophically here. Some providers make persistence the default — the filesystem survives indefinitely between calls and the environment scales to zero when idle — which is a genuinely different bet on the same isolation primitive than PandaStack's snapshot-restore-on-every-create with no warm pool. Neither is wrong; they optimize for different shapes of work — long-lived stateful environments versus cheap, identical, disposable executions. For a security API there's a subtle point here: a disposable, restore-fresh-every-time model gives you a clean environment on every call with no state carried from the last (possibly hostile) run, which is often exactly what you want when the code is untrusted. Weight whichever fit matters — see /blog/pandastack-vs-fly-sprites.

Criterion 5: network egress control

This is the criterion buyers most often forget until an incident, and it's specific to running untrusted code: even inside a perfect isolation boundary, code that can reach the open internet can exfiltrate data, phone home, join a botnet, or mine crypto. A secure execution API needs a story for what the code can reach — not just what can reach it. The relevant questions are concrete: can you deny egress by default and allowlist specific hosts, block or rate-limit outbound connections, and observe what the code tried to reach? APIs vary widely here, and it's frequently underspecified in docs, so ask directly and test it. This is deep enough that we wrote it up on its own in /blog/controlling-network-egress-untrusted-code.

Where I'm allowed to be specific: on PandaStack every execution gets its own Linux network namespace, veth pair, and tap device — its own /30 subnet from a pool of 16,384 pre-allocated subnets per agent — so network policy is enforced per-execution at the netns/iptables layer rather than shared across tenants. Because you can self-host the agent, egress policy is yours to define at the host and namespace level rather than being whatever the vendor's default happens to be. For every other API, treat egress as a first-class question in your evaluation: read the docs, then actually try to curl an arbitrary host from inside a sandbox and see what happens. Do not assume a strong isolation boundary implies a strong egress boundary — they're independent controls.

Criterion 6: pricing posture

I won't quote dollar figures for anyone — ourselves included — because pricing here changes often enough that any number I print will be stale. Go to each vendor's live pricing page and date what you find. The posture, though, shapes your real bill more than the per-unit rate does:

• Metered usage is the norm: most hosted APIs bill on a mix of CPU time, memory, executions, storage, and egress, usually per-second, usually with a free or credit tier. The headline rate matters less than how idle time is treated.
• Idle treatment can dominate: agent workloads spend a lot of wall-clock time waiting on model calls. A design that bills only active CPU, or scales an environment to zero when it sleeps, can cost dramatically less for bursty loops than one billing wall-clock for an idle VM — this is where bills diverge most.
• Self-host 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 math at your projected volume.

The field, at a glance

The short version of each option, by isolation model and hosting, with a link to the head-to-head where one exists. The deep dives follow the same discipline: specific numbers only for PandaStack, the competitor in general terms with a 'verify against their docs' caveat, and an honest 'pick the other one when…' section.

• PandaStack — open-source (Apache-2.0) Firecracker microVMs, self-hostable, exposed as a code-execution API; snapshot-restore on every create (~179ms p50), first-class CoW forking (400–750ms same-host), per-execution networking with self-hostable egress policy, plus managed Postgres, app hosting, and functions on one substrate.
• E2B — focused, mature, hosted-first Firecracker execution API with an Apache-2.0 core that's also self-hostable. See /blog/pandastack-vs-e2b.
• Modal — hosted serverless AI/ML compute with a Sandbox primitive on gVisor. See /blog/pandastack-vs-modal.
• Daytona — a dev-environment-and-sandbox angle with a dedicated-kernel isolation story (AGPL-3.0; managed, self-host, or hybrid). See /blog/pandastack-vs-daytona.
• Vercel Sandbox — hosted, Firecracker-backed, tightly integrated with the Vercel AI SDK; client SDK open source, runtime not. See /blog/pandastack-vs-vercel-sandbox.
• Fly.io — hosted app platform whose Machines are Firecracker-based, persistent-by-default and scale-to-zero, with newer sandbox/agent offerings on the same substrate. See /blog/pandastack-vs-flyio-machines.
• OSS isolation building blocks — gVisor (user-space kernel), Kata Containers (VM isolation with a container UX), and WASM runtimes (Wasmtime/WasmEdge, capability-based). Pick when you want to build the execution API yourself and own the isolation layer directly.

Where PandaStack fits (and where I'm allowed to be specific)

To pull the threads together: every PandaStack execution is a Firecracker microVM with its own guest kernel (5.10, Ubuntu 24.04 guest), isolated by KVM, running under a jailer that drops privileges and exposes only a minimal virtio device model (net, block, vsock). Networking is per-execution — its own Linux netns, veth pair, and tap from 16,384 pre-allocated /30 subnets per agent — so egress policy is enforced per-execution rather than shared. Boot is snapshot-restore on every create (~179ms p50, ~203ms p99, no warm pool), with an optional UFFD streaming mode that pages guest memory on demand from object storage — HTTP Range GET in 4 MiB chunks, zero-page elision, a prefetch trace, a shared per-host chunk cache, optional 2 MiB hugepages — so it begins restoring before the whole memory image is local. Forking is copy-on-write and first-class (400–750ms same-host, 1.2–3.5s cross-host), and a managed database is its own microVM that comes ready in 30–90s. You drive it from the pandastack Python package, the @pandastack/sdk TypeScript SDK, or the pandastack CLI — each reads PANDASTACK_API_KEY (keys prefixed pds_) with a configurable base URL, so the same code targets the hosted offering or your self-hosted control plane unchanged.

In practice the secure-exec path is about as small as the container it replaces — create an isolated microVM, run the untrusted code, read stdout, and let the environment be disposed:

from pandastack import Sandbox

# Boot a Firecracker microVM from a baked snapshot (~179ms p50, no warm pool).
# ttl_seconds bounds the blast radius: the microVM is reaped automatically.
sbx = Sandbox.create(template="code-interpreter", ttl_seconds=120)

# Run untrusted / LLM-generated code inside the microVM's own guest kernel.
# It never touches the host kernel; egress is governed per-execution.
untrusted = "import platform; print(platform.uname().system, 6 * 7)"
result = sbx.exec(f"python3 -c {untrusted!r}")

print(result.stdout)     # -> "Linux 42"
print(result.exit_code)  # -> 0

# Nothing to clean up by hand: the sandbox self-disposes at TTL, and each
# create() restores a fresh snapshot — no state carried from a prior run.
sbx.kill()

It's the right pick when you want an open-source Firecracker execution API you can own end-to-end, with egress policy and data residency under your control — and the wrong pick if you have no infra appetite and a hosted-only service would do, or if your entire workload is pure sandboxable compute that a WASM runtime would run faster and lighter.

When something other than PandaStack is the right call

Being an honest broker means saying plainly when another API fits better. Map your situation to the option, not the reverse:

• Pick E2B (or another focused hosted execution API) when you want zero infrastructure to operate and would rather adopt a mature default than own a substrate.
• Pick Vercel Sandbox when you're already building on the Vercel AI SDK and want the tightest path from 'the LLM writes code' to 'it runs safely' inside that stack.
• Pick Modal when your real workload is scale-out AI/ML compute (GPU jobs, batch inference) and the code-execution API is incidental, and you want it fully managed.
• Pick Daytona when its dedicated-kernel, development-environment model maps to how your team works better than a raw execution primitive.
• Pick Fly.io when durable per-environment state is your core requirement: long-lived environments that keep state across calls, not identical disposable executions.
• Pick a WASM runtime (Wasmtime, WasmEdge) when your untrusted code is pure, sandboxable compute — expression evaluation, transforms, plugin functions — and you value millisecond starts and a deny-by-default capability model over 'run any Linux binary or pip package' (/blog/wasm-vs-microvm).
• Pick a raw OSS isolation runtime (gVisor, Kata) when you want maximum control over the security boundary and have the appetite to build the API layer — networking, egress, snapshots, an SDK — around it yourself.

One honest note on the locked-in baseline: many teams start from OpenAI's hosted Code Interpreter (Python-only, hosted-only, no self-host, no egress control you own) and outgrow it the moment they need a second language, a data-residency guarantee, or a network policy. OpenAI's own agent stack now treats the sandbox as a pluggable, swappable backend — the clearest signal that the execution environment is a layer you get to choose, not a default you're stuck with. Where these APIs diverge is everything above the isolation boundary — boot path, fork semantics, persistence, egress control, and whether you can own the substrate. Concentrate your evaluation there, because isolation strength is roughly the thing the good ones already agree on.

The bottom line

There is no single best secure code execution API — there's a best one for your six constraints. The serious options share the thing that matters most: hardware-virtualized microVM (or, for Modal, gVisor) isolation is the correct foundation for running code an LLM wrote, and that's not where they differ. They differ on whether you can self-host, how fast create() returns, whether forking is first-class, how egress is controlled, and how the bill is shaped. Start from the criterion forcing your hand — usually isolation strength, self-host/residency, or egress control — shortlist the two that fit, and prototype against both, including a genuine attempt to break out, before you commit. PandaStack's bet, for the record, is an Apache-2.0 Firecracker core exposed as a code-execution API — snapshot-restore on every create (~179ms p50), CoW forking (400–750ms same-host), per-execution networking and egress policy — that you run end-to-end on your own hardware. If that matches your constraints, benchmark it against the field and keep us honest.