Best AI Agent Sandboxes in 2026

An AI agent is, mechanically, a loop that asks a model what to do next and then does it — and 'do it' increasingly means running model-generated code, shell commands, and tool calls. The sandbox is where that runs. It's the boundary between 'the model emitted rm -rf' and 'the model wiped a host,' and it's the thing your agent loop blocks on dozens of times per task, so its latency shapes how your product feels. Pick it wrong and you either ship a security hole or a sluggish agent. In 2026 there's a healthy field of credible options — which is great for buyers and confusing when every vendor's own blog ranks itself first. This is a roundup that tries to be an honest broker: real selection 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, broad platform), E2B, Modal, Daytona, Vercel Sandbox, Fly Machines, plus the build-it-yourself option (gVisor, Kata, or Firecracker direct) for teams who want to own the substrate. Rather than rank these into a leaderboard that ignores your workload, we'll set the selection criteria first, then walk each option with what it is, its isolation model, and an honest 'best fit.' The hosted-vs-self-host companion is /blog/best-code-execution-sandboxes; per-vendor deep dives are linked throughout.

The criteria that actually separate them

Every sandbox in this market will run a Python script and hand you back stdout — that baseline tells you nothing. For an AI agent specifically, the differences that decide fit live in five places. Work out which one is forcing your hand before you compare anything, because the right answer changes completely depending on which one matters to you.

• Isolation strength — an agent runs code it wrote itself, which is untrusted code by definition. This is the criterion that matters most: shared-kernel container, user-space kernel (gVisor), or hardware-virtualized microVM (Firecracker, Kata). More on the ladder in /blog/code-isolation-hierarchy.
• Cold-start and create latency — how long create() blocks inside the loop. It compounds across a long trajectory and is the difference between an agent that feels instant and one that feels broken.
• Statefulness and forking — can you snapshot a warm environment and fork it cheaply (copy-on-write), or persist state across sessions? This is where the microVM model pays off for tree-search and 'try N fixes' agent patterns.
• Self-host — can the agent's code run on your own hardware, under your own controls, or must it run on the vendor's infrastructure? 'Self-hosted' is badly overloaded, so be precise.
• Pricing at scale — the posture matters more than the per-unit rate, because agent loops spend most of their wall-clock time idle, waiting on model calls. How idle time is billed dominates the bill.

PandaStack

PandaStack (our project) is an open-source, Apache-2.0 platform where every sandbox 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). The bet is 'microVM isolation as a product you can own end-to-end' — you run the control-plane API and a per-host agent on any Linux box with /dev/kvm, and sandboxes execute entirely on your infrastructure. There's a hosted offering too, on the same binaries, so identical SDK code targets either. Where I'm allowed to be specific, because these are our own numbers: boot is snapshot-restore on every create — there's no warm pool of idle VMs — landing at 179ms p50, roughly 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. Forking is first-class via copy-on-write — same-host forks run 400–750ms, cross-host 1.2–3.5s — so you can warm an environment once and fork it N times for agent rollouts. Per-sandbox networking comes from 16,384 pre-allocated /30 subnets per agent, and a managed Postgres create runs 30–90s on the same substrate. Driving it from Python is a few lines:

from pandastack import Sandbox

# Boot a microVM from the agent template; auto-cleans after the TTL.
sbx = Sandbox.create(template="agent", ttl_seconds=900)

# Run a model-generated command inside the isolated guest.
result = sbx.exec("python -c 'print(2 + 2)'")
print(result.stdout)      # -> 4
print(result.exit_code)   # -> 0

# A Firecracker microVM booted (~179ms p50), ran untrusted code,
# and is yours for 900s — no VMM, jailer, or netns pool to operate.

• Isolation model: hardware-virtualized Firecracker microVM, own guest kernel per sandbox, KVM-isolated, minimal VMM surface under a jailer.
• Best fit: teams who want an open-source Firecracker platform they can self-host on their own KVM hosts, with first-class CoW forking for agent rollouts and a broader platform (Postgres, app hosting, functions) on one substrate. The wrong pick if you have no infra appetite and a hosted-only service would do.

E2B

E2B is a focused, mature sandbox built specifically for AI agents: a clean SDK, a long-standing code-interpreter heritage, and a hosted-first product with an Apache-2.0 core that's also self-hostable. Per E2B's own infra docs, sandboxes run as Firecracker microVMs — so it clears the isolation bar for arbitrary agent code, with its own guest kernel per sandbox. It's the canonical 'just give me a sandbox API' default, deliberately narrow in scope rather than a full platform, which is a legitimate strength if all you need is to run code. Verify current isolation backend, license, and persistence behavior against E2B's docs before you lean on them.

• Isolation model: Firecracker microVMs (per E2B's infra docs) — hardware-virtualized, own guest kernel per sandbox.
• Best fit: teams who want a proven, focused, agent-oriented sandbox with little to operate and would rather adopt a mature default than own a substrate. See /blog/pandastack-vs-e2b and /blog/e2b-alternatives.

Modal

Modal is a hosted serverless platform built around scale-out AI/ML compute — GPU jobs, batch inference, fan-out workloads — with a Sandbox primitive layered on top for running arbitrary agent code. Per Modal's own security docs, its sandboxing uses gVisor, a user-space kernel that intercepts guest syscalls so they mostly don't hit the host kernel directly. That's a meaningful step up from a plain container and a different bet from a full hardware-virtualized VM — a deliberate trade many teams are comfortable with, with workload-dependent syscall compatibility. Modal is hosted-only; there's no documented self-host path. Confirm the isolation backend and current behavior against Modal's docs.

• Isolation model: gVisor user-space kernel (per Modal's security docs) — syscall interception, not a hardware-virtualized VM.
• Best fit: teams whose real workload is scale-out AI/ML compute (GPU, batch inference) where the sandbox is incidental, and who want it fully managed. See /blog/pandastack-vs-modal.

Daytona

Daytona comes at agents from a development-environment-and-sandbox angle — it's AGPL-3.0 and offers managed, self-host, or hybrid deployment. Its docs describe a dedicated-kernel, VM-like model with complete isolation, without (in what I've read) naming a specific hypervisor — so I won't name one for it. If your agent work is shaped like 'spin up a dev-environment-flavored workspace and let the agent operate in it,' Daytona's model may map to how your team already works better than a raw, ephemeral sandbox primitive. Verify the isolation details, license, and deployment options against Daytona's current docs.

• Isolation model: a dedicated-kernel, VM-like model per its docs (hypervisor not named here); AGPL-3.0, self-hostable.
• Best fit: teams whose agent work maps to a development-environment shape, and who want managed, self-host, or hybrid options. See /blog/pandastack-vs-daytona.

Vercel Sandbox

Vercel Sandbox is a hosted sandbox tightly integrated with the Vercel AI SDK and the broader Vercel platform — the shortest path from 'the LLM in my Vercel app wrote code' to 'it runs safely' inside that ecosystem. Vercel states plainly that it runs on Firecracker microVMs and links the project, so it clears the isolation bar with hardware-virtualized VMs and a guest kernel per sandbox. The client SDK is open source; the runtime is not, and it's hosted-only — no self-host path. If you're already on Vercel, the integration tax is near zero, which is the whole point. Verify current limits, isolation, and pricing against Vercel's docs.

• Isolation model: Firecracker microVMs (Vercel states this plainly) — hardware-virtualized, guest kernel per sandbox; hosted-only.
• Best fit: teams already building on the Vercel AI SDK who want the tightest path from agent-generated code to safe execution inside that stack. See /blog/pandastack-vs-vercel-sandbox.

Fly Machines

Fly Machines are Fly.io's fast-booting, API-driven VMs — Firecracker-based per Fly's own platform docs — that you can start, stop, and scale to zero, with persistent volumes for durable state. They aren't an agent-specific 'sandbox' product so much as a general microVM primitive you can shape into one, and the persistence story is the key differentiator: a Machine can keep its filesystem across sessions and idle down to zero, which is a genuinely different bet than snapshot-restore-on-every-create. If your agents need long-lived, stateful environments rather than identical disposable creates, that fit matters a lot. (Fly's higher-level 'Sprites' agent-runner builds on this Machines substrate; confirm which product you're actually buying.) Verify isolation, persistence, and scale-to-zero behavior against Fly's docs.

• Isolation model: Firecracker-based microVMs (per Fly's platform docs) — hardware-virtualized; persistent volumes and scale-to-zero.
• Best fit: teams whose core requirement is durable, long-lived per-agent state across sessions, not cheap identical disposable creates. See /blog/pandastack-vs-fly-sprites.

Build it yourself (gVisor / Kata / Firecracker direct)

The honest baseline that reframes the whole comparison: for some teams the right 'sandbox for AI agents' is one you build on open-source primitives. gVisor is a user-space kernel that drops in as an OCI runtime (runsc) under Docker/Kubernetes — a step up from a plain container with little new operational surface. Kata Containers gives VM-grade isolation with a container/Kubernetes UX, and can run on top of Firecracker, Cloud Hypervisor, or QEMU. Firecracker direct gives you the smallest, best-audited VMM and total control. The catch is uniform: the VMM is the easy 10%, and the platform around it — networking, snapshot/restore orchestration, a template pipeline, cross-host scheduling, image storage, and an API — is the other 90% you'll build and operate yourself (the building blocks are surveyed in /blog/best-open-source-code-sandboxes).

• Isolation model: your choice — gVisor (user-space kernel), Kata (VM-grade, container UX), or Firecracker (minimal hardware-virtualized VMM).
• Best fit: teams with real systems/infra muscle who want maximum control and minimal trust surface, and are happy to build the orchestration layer — or cost-sensitive scale where owning the substrate beats a hosted bill. See /blog/gvisor-vs-firecracker and /blog/kata-vs-firecracker.

The field, option by option

The short version of each, by isolation model and hosting posture, so you can scan and shortlist. The discipline holds throughout: specific numbers only for PandaStack, every competitor in general terms with a 'verify against their docs' caveat, and no invented competitor figures.

• PandaStack — open-source (Apache-2.0) Firecracker microVMs, self-hostable on any /dev/kvm host or hosted on the same binaries; snapshot-restore on every create (179ms p50, ~203ms p99, no warm pool), first-class CoW forking (400–750ms same-host, 1.2–3.5s cross-host), plus managed Postgres, app hosting, and functions on one substrate.
• E2B — focused, mature, agent-oriented Firecracker sandbox (per its infra docs); Apache-2.0 core, hosted-first but self-hostable. Verify license and isolation in its docs.
• Modal — hosted serverless AI/ML compute with a Sandbox primitive on gVisor (per its security docs); hosted-only. Best when GPU/batch compute is the real workload.
• Daytona — dev-environment-and-sandbox angle, AGPL-3.0, managed/self-host/hybrid; dedicated-kernel VM-like model per its docs (hypervisor not named here).
• Vercel Sandbox — hosted, Firecracker-backed (Vercel states this plainly), tight Vercel AI SDK integration; client SDK open source, runtime not, hosted-only.
• Fly Machines — Firecracker-based microVMs (per Fly's docs) with persistent volumes and scale-to-zero; best when durable per-agent state is the requirement.
• Build-it-yourself — gVisor, Kata, or Firecracker direct: maximum control, minimal trust surface, but you build and operate the platform layer. See /blog/best-open-source-code-sandboxes.

When something other than PandaStack is the right call

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

• Pick E2B when you want a mature, agent-focused sandbox with zero infrastructure to operate and would rather adopt a proven default than own a substrate.
• Pick Modal when your real workload is scale-out AI/ML compute (GPU, batch inference) and the sandbox 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 ephemeral sandbox primitive.
• Pick Vercel Sandbox when you're already on the Vercel AI SDK and want the tightest in-stack path from agent-generated code to safe execution.
• Pick Fly Machines when persistence is your core requirement: long-lived stateful agent environments that scale to zero, not identical disposable creates.
• Build it yourself (gVisor / Kata / Firecracker) when you want maximum control and minimal trust surface and have the infra muscle to build the platform layer.

One honest note on the locked-in baseline: many teams start from a hosted, Python-only code interpreter and outgrow it when they need self-host, a different runtime, or cheaper idle behavior. The modern agent stacks increasingly treat the sandbox as a pluggable, swappable backend — the clearest signal that it's a layer you get to choose, not a default you're stuck with. Where these options diverge is everything above the isolation boundary — boot path, fork semantics, persistence, breadth, and whether you can own the substrate. Concentrate your evaluation there, because isolation strength is roughly the thing the serious options already agree on.

The bottom line

There is no single best AI agent sandbox — there's a best one for your five 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 your agent wrote, and that's not where they differ. They differ on whether you can self-host, how fast create() returns, whether forking and persistence are first-class, and how the bill is shaped for an idle-heavy agent loop. Start from the criterion forcing your hand — usually self-host, statefulness, or cold-start — shortlist the two that fit, and prototype against both before you commit. PandaStack's bet, for the record, is an Apache-2.0 Firecracker core wrapped in a full platform — snapshot-restore on every create (179ms p50), CoW forking (400–750ms same-host), per-sandbox networking, managed services — that you run end-to-end on your own hardware. If that matches your constraints, benchmark it against the field and keep us honest.