The best CodeSandbox alternatives in 2026

CodeSandbox has quietly become two products that share a name. One is the thing most people remember: an in-browser editor where you open a link and a React app is running in three seconds, perfect for bug reproductions, documentation examples, and interview exercises. The other is an SDK — a programmatic API for creating VMs, forking their memory, and running code inside them, aimed squarely at AI agents.

Those two jobs have almost nothing in common, and the reason most "CodeSandbox alternatives" lists are useless is that they mix them. So this one splits the question first. I build PandaStack, which is only a candidate for the second job, and I have tried to be clear about where it is not the answer.

First, decide which job you are hiring for

Answer one question: is a human going to look at this, or is a program going to drive it?

• A human looks at it — you need an editor. File tree, syntax highlighting, hot reload, a preview pane, a shareable URL, and an embed that does not wreck your documentation site's performance. The runtime matters far less than the editing experience.
• A program drives it — you need an API. Create an environment in a known amount of time, put files in, run a command, read the output, tear it down. There is no UI requirement at all, and the properties that matter are creation latency, isolation strength, filesystem and network control, and what happens to the environment when your process crashes halfway through.

A few products try to do both. They are generally better at one of them, and knowing which one you actually need is the whole decision.

If you need an in-browser editor

• StackBlitz — The closest thing to a direct swap, and architecturally the most interesting: WebContainers run Node entirely inside the browser tab via WebAssembly, so there is no server round trip and no per-user compute for you to pay for. Startup is close to instant and it works offline. The limits are the limits of the browser — no native binaries, no arbitrary system packages, and anything expecting a real Linux will notice.
• Replit — A full cloud IDE with real Linux underneath, a package manager, ports, databases, and deployment. Heavier than an embed, but the right answer when the example needs to actually be a working application rather than a snippet.
• GitHub Codespaces — VS Code against a real container, defined by a devcontainer file that your repository already benefits from having. Best when the audience is contributors to your project rather than readers of your docs.
• Gitpod — Similar premise with a strong focus on reproducible, prebuilt workspaces. Worth evaluating on prebuild behaviour, which is where the perceived speed comes from.
• Sandpack — CodeSandbox's own open-source bundler-and-editor component. If you liked the embed and want to stop depending on the hosted product, this is the honest answer: you keep the component and self-host it.
• Monaco or CodeMirror plus your own runner — The build-it-yourself option, and more reasonable than it sounds when your examples are narrow. The editor is a solved problem; you supply execution, which for many docs sites means a single API call to a sandbox service.

If you need a programmable VM API

This is the category that exploded once agents started writing code, and the products in it are more similar than their marketing suggests. Nearly all of them will create an isolated Linux environment in under a second, let you write files, run commands, and stream output. The differences that actually decide the purchase are narrower and less glamorous.

• Creation latency, and its tail. A p50 of 200 milliseconds with a p99 of eight seconds is a much worse product than a p50 of 400 milliseconds with a p99 of 600, because your agent's user experience lives in the tail. Ask for both numbers.
• Isolation model. A container with a hardened seccomp profile is not the same boundary as a hypervisor. If you are running code a language model wrote, or code a user uploaded, the difference matters and you should know which one you are buying.
• Fork semantics. Cloning a filesystem copy-on-write is common and is what removes repeated dependency installs. Forking a running process — memory, open descriptors, a warm interpreter with your data already loaded — is much rarer, usually limited to a single child, and worth asking about specifically rather than assuming, because "fork" is used for both.
• Lifetime and cleanup. What happens when your orchestrator dies mid-task? A platform with no TTL leaves you paying for orphans; a platform with an aggressive one kills long-running work. You want both a default TTL and an explicit override.
• Egress control. Whether you can restrict what the sandbox reaches. For untrusted code this is not a nice-to-have — unrestricted egress turns a sandbox into a proxy for whatever the code wants to do.
• Persistence. Whether state survives between sessions, and at what granularity: a volume, a snapshot, or nothing.

• E2B — The most established sandbox API for agent workloads, with mature Python and TypeScript SDKs, a code-interpreter-shaped abstraction, and a large body of published examples. The default starting point for most teams, and the one to benchmark others against.
• Modal — A serverless compute platform rather than a sandbox product, but its sandbox primitive is capable and it is exceptional at the adjacent problem of GPU work and heavy Python dependency trees. Choose it when execution is part of a larger compute pipeline.
• Daytona — Development-environment lineage pointed at agent workloads, with a focus on fast creation and declarative environment definitions.
• Runloop — Purpose-built for coding agents, with devbox-shaped environments and snapshotting aimed at long-running agent sessions.
• Vercel Sandbox — Ephemeral compute inside the Vercel platform, natural if your agent already lives in a Vercel-hosted app and you want one vendor.
• Cloudflare's sandbox and container products — Very low latency and a global footprint, with the runtime constraints that come from that architecture. Excellent for short, self-contained execution; check compatibility for anything needing a full Linux userland.
• Fly Machines — Not a sandbox product, a VM API. More assembly required — you build the SDK ergonomics yourself — and in exchange you get complete control over the image and the network.
• Self-hosted Firecracker, gVisor, or Kata — The build option. Firecracker is a public, well-documented VMM and this is a legitimate path if isolation is a compliance requirement or your volume makes per-sandbox pricing painful. Budget for the parts that are not the VMM: networking at scale, snapshot storage, cleanup of leaked resources, and the on-call that comes with all of it.
• PandaStack — Firecracker microVMs behind a sandbox API. Every create restores a memory snapshot rather than cold-booting, which is where the sub-second numbers come from; each sandbox gets its own kernel and network namespace; and fork clones the parent's disk copy-on-write, so N children start with dependencies already installed instead of each repeating setup (a warm memory-fork mode exists for a single child, and pauses the parent to do it). Managed Postgres and git-driven app hosting sit on the same substrate, which matters if your agent needs a database or has to deploy what it built. It is not an editor, it has no embeddable UI, and if what you wanted was a bug-reproduction link for your docs it is the wrong tool entirely.

# The shape of every sandbox API in this category is similar enough that
# a migration is mostly renaming. What differs is what you can do AFTER
# the environment exists -- fork, snapshot, restrict egress.
from pandastack import Sandbox

sbx = Sandbox.create(
    template="code-interpreter",
    ttl_seconds=900,          # ALWAYS set a TTL. If your orchestrator
)                             # crashes, this is what stops the bill.

try:
    sbx.filesystem.write("/work/analyse.py", SCRIPT)
    r = sbx.commands.run("python /work/analyse.py", timeout=120)
    print(r.stdout, r.exit_code)

    # Snapshot once the expensive setup is done, so the next run starts
    # from a warm machine instead of repeating pip install. Returns the
    # snapshot id; pass it to Sandbox.create(from_snapshot=...) later.
    snapshot_id = sbx.snapshot()
finally:
    sbx.kill()

# fork() is the other half: children inherit the parent's DISK
# copy-on-write -- installed packages, written files -- and boot fresh,
# so they do not inherit the parent's running processes. That is still
# the expensive part of agent setup, and it is what makes "try five
# approaches in parallel" cost one dependency install instead of five.
parent = Sandbox.create(template="code-interpreter")
parent.commands.run("pip install -q pandas pyarrow", timeout=300)
children = parent.fork_tree(5)
for child in children:
    child.commands.run("python -c 'import pandas; print(pandas.__version__)'")

What a migration actually costs

For the API case, less than you think. Create, write file, run command, read output, destroy — every product in the category exposes those five operations, so the mechanical port is a day. The parts that take longer are the ones nobody plans for.

• The environment image. Your code assumes a set of installed packages, and the new platform's base image differs. Pin your dependencies explicitly and build your own template rather than relying on what happens to be there — you needed to do this anyway.
• Timeout and retry behaviour. Every platform fails differently under load, and your error handling is currently tuned to one specific failure vocabulary. Expect to rewrite the retry logic.
• Streaming. Blocking exec is easy to port. Streaming stdout is where the protocols diverge — SSE, WebSocket, long-poll — and if your UI shows live output, this is the real work.
• Cost shape. Per-second billing, per-invocation billing, and reserved-capacity billing produce wildly different bills for the same workload. Model your actual usage pattern rather than comparing headline rates.

Pick by situation

• Embeddable examples in documentation, and you care about page weight → StackBlitz WebContainers, or Sandpack self-hosted. No server-side compute to pay for either way.
• A full cloud IDE for contributors → GitHub Codespaces or Gitpod, driven by a devcontainer file that helps your repo regardless.
• An AI agent that executes code it wrote → a sandbox API with a hypervisor boundary. E2B as the baseline, PandaStack if fork-from-warm-memory or an attached database matters, Modal if the work is GPU-shaped.
• You run untrusted user-submitted code → the isolation model is the whole decision. Insist on a VM boundary and egress control, and treat container-only isolation as unsuitable regardless of the seccomp profile.
• Interview exercises or an autograder → an API plus your own thin UI. The editor is CodeMirror; the hard part is a clean environment per candidate, which is the API's job.
• Very high volume and pricing has become the problem → self-hosted Firecracker, with clear eyes about the operational surface you are adopting.
• You liked CodeSandbox and just want to stop depending on the hosted service → Sandpack for the editor, any sandbox API for execution. That is the decomposition.

The short version

Split the question before you shop. If a human is going to type into it, you are buying an editor, and StackBlitz or Sandpack will get you closest to what you liked about CodeSandbox. If a program is going to drive it, you are buying compute with an SDK, and the editor half is irrelevant.

For the API case, benchmark on the tail rather than the median, insist on knowing the isolation boundary, and set a TTL on everything you create. Those three habits matter more than which vendor you pick — and they make the next migration, whenever it comes, a day of work instead of a quarter.