E2B Alternatives: A Guide to AI Code Execution Sandboxes

E2B is a good product. If you're searching for an E2B alternative, it's usually not because E2B is bad — it's because something about its shape doesn't fit: you need to self-host, you want more than raw code execution on one substrate, your forking pattern needs different semantics, or the hosted-first model conflicts with a data-residency rule. This guide is for that reader. It's an honest map of the landscape for running AI-generated or otherwise untrusted code, organized around the decisions that actually matter rather than a ranked leaderboard.

The field includes PandaStack (open-source Firecracker microVMs, self-hostable, broad platform — this is our product), Modal, Daytona, Northflank, Vercel Sandbox, and Fly.io Sprites, plus the underlying isolation-tech axis of containers vs gVisor vs Firecracker microVMs. We'll walk the decision criteria first, then give you the options at a glance with links to the per-competitor deep dives so you can go a level deeper on whichever one you're weighing.

Decision 1: the isolation model

This is the dimension people most often get wrong, and it's the one that matters most when the code is untrusted. There are three broad answers, in increasing order of isolation strength.

• Containers (namespaces + cgroups + seccomp): fast and cheap, but every container shares the host kernel. A kernel-level escape is a host compromise. Plenty of products marketed as 'sandboxes' are really hardened containers — fine for trusted code, riskier for arbitrary LLM output.
• gVisor: a user-space kernel that intercepts guest syscalls, shrinking the host-kernel attack surface without a full VM. A real step up from raw containers, with its own performance and compatibility trade-offs depending on the workload.
• Firecracker microVMs: each sandbox gets its own guest kernel and is isolated by hardware virtualization (KVM). The host kernel is never directly exposed to guest code; the attack surface is the much smaller, much better-audited VMM. This is the model E2B uses, and it's the right default for running code you didn't write.

Among the E2B alternatives here, the microVM camp is crowded: PandaStack runs every sandbox as a Firecracker microVM (kernel 5.10, Ubuntu 24.04 guest). Vercel Sandbox runs each sandbox in its own Firecracker microVM with a dedicated kernel — Vercel's docs state this plainly and link to the Firecracker project. Fly.io Sprites are Firecracker microVMs too. Northflank is the interesting outlier: its own marketing positions it on Kata Containers (microVM isolation via Cloud Hypervisor) plus gVisor, and explicitly distances itself from Firecracker — so don't lump it in. If your bar is 'safe to run arbitrary untrusted code,' microVM-class isolation (Firecracker or Kata) clears it; a plain container generally does not. See /blog/firecracker-vs-docker and /blog/what-is-a-microvm for the deeper isolation story.

Decision 2: hosted vs self-host

This is the cleanest structural fork in the road, and it's frequently the reason people look past E2B in the first place. The question is: where does untrusted code physically execute, and who operates the machines it runs on?

Most options here are hosted-first managed services — you call an API, code runs on the vendor's infrastructure, you don't touch a KVM host. That's true of E2B, Modal, Vercel Sandbox, and Fly.io Sprites. Be careful with the word 'self-hosted,' because it's overloaded: Northflank, for example, offers a Bring-Your-Own-Cloud model where its control plane manages compute that runs in your cloud account — but the Northflank platform itself is proprietary and not open-source software you run. That is a different thing from a project whose source you can deploy end-to-end.

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

Decision 3: cold-start and create latency

Inside an agent loop, how long create() blocks is often the difference between usable and not. An agent that spins up a fresh environment per task can't tolerate multi-second startup on every step.

PandaStack's design choice is specific: there is no warm pool of idle VMs. Every create restores a baked Firecracker snapshot on demand. The snapshot already contains a booted kernel, a running guest agent, and an open network stack, so 'starting' a sandbox is really 'restore memory pages and resume.' That lands at 179ms p50 (p99 ~203ms). The only slow path is the first-ever spawn of a brand-new template, which does a real cold boot (~3s) and bakes the snapshot; every create after that is on the fast restore path.

Most other providers also advertise fast startup — Vercel, for instance, markets 'millisecond' starts off Firecracker's fast boot. I deliberately won't quote competitor latency numbers, because cold-start is exactly the metric that's easy to mis-measure across vendors: warm pool vs true cold boot, snapshot resume vs full boot, your region vs theirs, your template size vs 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, fairly, how you read ours) as a starting hypothesis to benchmark — not a settled fact.

Decision 4: forking and copy-on-write state

Forking is where the microVM model pays off in ways containers can't easily match, and it's a real point of difference between providers — so evaluate it directly rather than from a feature matrix.

PandaStack exposes full snapshots and forks as first-class primitives. A snapshot captures the full machine state — memory plus rootfs. A fork clones a running sandbox via copy-on-write: guest memory is shared through MAP_PRIVATE (the kernel only copies pages on write), and the rootfs is cloned with an XFS reflink so data is shared until something writes. A same-host fork completes in about 400ms; a cross-host fork (GCS download plus restore) runs 1.2–3.5s. The pattern this unlocks: warm one environment to a known state — dependencies installed, dataset loaded, REPL hot — then fork it N times to explore branches in parallel, each starting from the exact same memory without re-running setup. If your workload is tree-search, agent rollouts, or 'try five fixes and keep the one that passes,' fork semantics are the feature to test hardest.

Persistence is the flip side of forking, and the field splits philosophically here. Fly.io Sprites, for example, make persistence the default — the filesystem survives indefinitely between sessions and the VM scales to zero when idle. That's a genuinely different bet on the same Firecracker primitive than PandaStack's snapshot-restore-on-every-create with no warm pool. Neither is wrong; they optimize for different workloads (persistent agent environments vs. cheap, identical, disposable creates). The honest framing is 'two designs on one isolation tech' — not 'one is faster.' See /blog/snapshot-and-fork-explained for how CoW forking works under the hood.

Decision 5: platform breadth

Sandboxes are ephemeral by design, so the interesting question is what holds state and structure around them. Some tools are deliberately focused point solutions — a sandbox and nothing else — and that focus is a legitimate strength if all you need is to run code. Others bundle a wider platform. The trade-off is the familiar one: a focused tool is simpler to reason about and easier to swap out; a broad platform consolidates onto one substrate and one bill but couples you more tightly.

On the broad end, Modal positions around serverless compute for AI/ML workloads, and Northflank is a full managed cloud platform (app hosting, managed databases, jobs, GPU workloads, CI/CD) where sandboxes are one feature among many. PandaStack runs everything on one microVM substrate:

• Managed PostgreSQL 16 — each database is its own dedicated Firecracker microVM with a durable volume, pgvector and other extensions, PgBouncer pooling, and connectivity over native postgres:// (via SNI routing) or an HTTP query broker.
• Git-driven app hosting — connect a repo and PandaStack auto-detects the framework (next/vite/cra/node/static/python), does blue-green deploys, scales to zero via auto-hibernate, and supports GitHub push-to-deploy.
• Serverless functions with cron schedules — code bundles invoked directly or over HTTP, on scheduled triggers.
• Durable volumes — persistent disk for sandboxes that need state beyond the ephemeral copy-on-write rootfs.

The point isn't 'more features win.' 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, having it on one isolation substrate and one bill is the argument for breadth. If all you need is to run code, that breadth is irrelevant to you and a focused tool like E2B may be cleaner. Decide which side of that line you're on before you weigh anything else.

Decision 6: pricing posture

I won't quote dollar figures for anyone but ourselves, because pricing in this space changes often enough that any number I print will be stale by the time you read it — go to each vendor's live pricing page. But the posture is worth understanding at a structural level, because it shapes cost more than the per-unit rate does.

• Metered usage is the norm: most options bill on some mix of CPU time, memory, creations, storage, and egress. Watch specifically for how idle time is treated — a few designs only bill active CPU (so model-call and network wait is cheap), and scale-to-zero models stop compute billing when a sandbox sleeps. For bursty agent workloads, that idle treatment can dominate your bill.
• Ecosystem coupling matters: a sandbox that's a feature of a larger platform (and authenticates through that platform's account/tokens) is convenient if you already live there, and lock-in if you don't.
• Self-host changes the equation entirely: with an open-source, self-hostable option you're trading 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.

The options at a glance

Here's the short version of each alternative, with a link to the head-to-head where we go deeper. These deep dives follow the same discipline as this guide: specific numbers only for PandaStack, the competitor described in general terms with a 'verify against their docs' caveat, and an honest 'pick the competitor when…' section.

• PandaStack — open-source (Apache-2.0) Firecracker microVMs you can self-host; snapshot-restore on every create (179ms p50, no warm pool); first-class CoW forking (~400ms same-host); plus managed Postgres, app hosting, and functions on one substrate. See /blog/pandastack-vs-e2b.
• Modal — hosted serverless compute oriented toward AI/ML workloads. If you want a fully managed, scale-out compute platform rather than a self-hostable sandbox, compare at /blog/pandastack-vs-modal.
• Daytona — a development-environment and sandbox angle on the problem. See /blog/pandastack-vs-daytona.
• Northflank — a managed full-stack cloud platform (apps, databases, GPU, CI/CD) with a sandbox feature; offers BYOC, runs on Kata + gVisor per its own positioning (not Firecracker). See /blog/pandastack-vs-northflank.
• Vercel Sandbox — a hosted, Firecracker-backed ephemeral compute primitive, tightly integrated with the Vercel AI SDK agent stack; its SDK/CLI is open source, the runtime is not. See /blog/pandastack-vs-vercel-sandbox.
• Fly.io Sprites — persistent-by-default Firecracker microVMs that scale to zero, aimed at long-lived agent environments. A different bet on the same isolation tech. See /blog/pandastack-vs-fly-sprites.

When an E2B alternative is the wrong move (pick the others when…)

Being an honest broker means saying when something other than PandaStack — including E2B itself — is the right call. Real reasons, not strawmen:

• Pick E2B or another hosted-only sandbox when you want zero infrastructure to operate, your need is narrowly code execution, or you're already deep in one ecosystem and the switching cost outweighs a marginal feature difference.
• Pick Vercel Sandbox when you're already building on the Vercel AI SDK and want the tightest path from 'the LLM writes code' to 'the code runs safely' inside that stack — the integration is the point.
• Pick Modal when your real workload is scale-out AI/ML compute (GPU jobs, batch inference) and the sandbox is incidental to that, and you want it fully managed.
• Pick Northflank when you want a unified managed platform — sandboxes alongside full app hosting, databases, and GPU — possibly running in your own cloud via BYOC, and you don't need the software itself to be open-source.
• Pick Fly.io Sprites when persistence is your core requirement: long-lived agent environments that keep their state across sessions and scale to zero between bursts, rather than identical disposable creates.
• Pick Daytona when its development-environment model maps more directly to how your team works than a raw sandbox primitive does.

The open-source / self-host lens

Because 'open-source E2B alternative' and 'self-host an E2B alternative' are two of the most common reasons people land on this page, it's worth being precise, since the word 'self-hosted' gets stretched across very different things.

• Genuinely open-source and self-hostable software: PandaStack's core is Apache-2.0 and runs end-to-end on your own KVM hosts — control plane plus per-host agent, sandboxes on your infra.
• Open-source client, proprietary runtime: Vercel Sandbox publishes its SDK/CLI under an open license, but the Firecracker host runtime is a closed hosted service. The open artifact is the client library, not the platform — so 'open source' applies only with that qualifier.
• BYOC, not OSS: Northflank's 'self-hosted' means a proprietary control plane managing compute in your cloud account. That's data-locality, not source-available software you operate.
• Stated intent, not shipped: Fly has publicly said it intends to offer an open-source local version of Sprites, but as of this writing that's a forward-looking statement, not a released artifact — don't plan around it as if it exists today.

The bottom line

E2B is a focused, mature, hosted Firecracker sandbox, and for a lot of teams it's exactly right. You look for an alternative when one of the six decisions above pulls you a different way: you need to self-host (PandaStack is the open-source, Apache-2.0, run-it-on-your-own-KVM answer); you want platform breadth on one substrate (PandaStack or Northflank); you live in the Vercel AI stack (Vercel Sandbox); you need persistent agent environments (Fly Sprites); or your real job is scale-out ML compute (Modal). Most of the serious alternatives share E2B's isolation model — Firecracker microVMs are the correct foundation for running untrusted AI-generated code, and that's not where they differ. They differ on boot path, persistence philosophy, breadth, and whether you can own the substrate. Start from the decision that's forcing your hand, read the head-to-head for the one or two that fit, then prototype against both before you commit.