Vercel Sandbox vs E2B for AI-Generated Code

Two products keep landing on the same shortlist the moment a team admits their agent needs somewhere to run code that isn't the API server: Vercel Sandbox and E2B. On a feature grid they look like direct competitors — create a sandbox, write files, run a command, read stdout. In practice they're different shapes of product, and the shape matters more than the feature list. One is ephemeral compute inside a platform you're probably already deploying to. The other is a standalone execution API that assumes nothing about where the rest of your stack lives.

I'm Ajay, I built PandaStack — which is also in this category, so read this with the appropriate squint. My rule for these posts: specific and numeric about my own platform, qualitative about everybody else's, because I won't publish a benchmark of a product I don't operate. There's a section below that recommends both of them over me under conditions that are genuinely common.

What each one is actually optimized for

Vercel Sandbox is ephemeral compute that lives inside the Vercel platform and the Vercel workflow. That's the whole design center. It exists because Vercel's customers were already generating code inside their apps — app builders, agent features, user-supplied snippets — and had nowhere safe to run it that wasn't a second vendor, a second bill and a second auth story. You call it from your Vercel functions, it inherits the project's environment and deployment context, and the logs land in the observability surface you already have open. If your app deploys there, the integration cost is close to zero.

E2B is a standalone sandbox API and SDK aimed squarely at AI agents and code interpreters. It's the product for teams whose central problem is "the model wrote Python, now what." It gives you a template system for defining what's inside the box, a filesystem API, a process API, and SDKs that read like they were written by people who have actually built an agent loop. Critically, it assumes nothing else about your stack — your app can run on AWS, Fly, a Hetzner box, or your laptop, and E2B is just an HTTP dependency.

One is a feature of a platform. The other is a platform built around one feature. Neither of those is an insult — but they pull your architecture in opposite directions.

That difference drives almost every trade-off below. Platform-native compute is fast to adopt and slow to leave. Provider-neutral compute is one more vendor on the invoice and one fewer assumption baked into your code.

The axes that actually bite

Here are the dimensions engineers actually get burned on six months in, when the demo has become a product. PandaStack is in each line so you can see where it sits rather than infer it.

• Integration gravity — Vercel Sandbox: highest; it's a first-class primitive of a platform you deploy to, callable from your functions with the platform's auth and observability already wired. E2B: low; a provider-neutral API you call from anywhere, which is exactly the point. PandaStack: low; REST API plus Python/TypeScript SDKs, no assumption about where your app runs, and Apache-2.0 if you'd rather run the whole thing yourself.
• Session lifetime and max duration — Vercel Sandbox: bounded, with a documented ceiling that fits the platform's request-shaped mental model; if your job is a 40-minute build, check that ceiling before you design around it. E2B: designed for interactive agent sessions with an extendable lifetime; verify the current maximum and how extension works in their docs. PandaStack: a sandbox lives until its ttl_seconds expires, you kill it, or an idle reaper takes it, and persistent sandboxes are exempt from the reaper entirely.
• Filesystem persistence — Vercel Sandbox: ephemeral by design; the box is gone when the session ends and you should treat anything on disk as scratch. E2B: filesystem API with persistence semantics you should confirm per product tier — the honest answer is "read the docs" because this is the fastest-moving field in the whole comparison. PandaStack: rootfs is a copy-on-write clone that persists for the sandbox's life; snapshots capture disk and memory; durable volumes exist for state that must outlive the VM.
• Template and image customization — Vercel Sandbox: the platform's runtimes plus an install at session start, which is simple but puts your dependency install on the critical path of every run. E2B: an explicit template system, and a genuine strength — bake the environment once, start from it. PandaStack: templates are baked into Firecracker snapshots (base, code-interpreter, agent, browser, postgres-16) plus custom templates from a Dockerfile, so the environment is warm at restore rather than installed at boot.
• Network egress control — Vercel Sandbox: check what the platform exposes here; egress policy on managed compute is frequently coarser than security review expects. E2B: verify their current network controls against your threat model before you assume you can lock a sandbox down to one endpoint. PandaStack: each sandbox gets its own network namespace with its own veth pair and NAT rules, which is where allow-list egress policy is actually enforceable, and Stratum-style mining egress is blocked at that layer.
• Language and runtime surface — Vercel Sandbox: strong on the JS/TS end of the world, with Python available; if your agent writes Rust or needs a system package manager, test that first. E2B: broad, template-driven — whatever you can bake, you can run. PandaStack: a full Ubuntu 24.04 guest with mise-managed Node, Python, Go and Bun pre-warmed, so "install the thing" is a normal apt/pip/npm problem rather than a platform limitation.
• Observability and log access — Vercel Sandbox: best-in-class if you already live in Vercel's observability story, because it's the same pane of glass as your deployments. E2B: SDK-level stdout/stderr streaming that you wire into your own logging. PandaStack: streaming exec over SSE, a WebSocket PTY, per-sandbox lifecycle events, and metrics in ClickHouse — you own the pipe.
• Self-hosting — Vercel Sandbox: not applicable; it's a hosted platform primitive. E2B: has an open-source lineage, but confirm what a supported self-hosted deployment looks like today before promising it to a security committee. PandaStack: Apache-2.0, and the same agent binary runs on your own KVM hosts as on ours.
• Portability if you need to leave — Vercel Sandbox: the primitives port fine, but the surrounding assumption (your app deploys here) doesn't, so leaving is a platform decision rather than a library swap. E2B: high; it's already a standalone dependency. PandaStack: high, and self-hosting is the escape hatch of last resort.

The loop both products exist to serve

Strip the branding off and every one of these products is serving the same twelve lines: ask the model for code, put it somewhere that isn't your API process, run it, feed the failure back, repeat until it works or you give up. Here it is against the PandaStack TypeScript SDK — the shape is close enough that you could retarget it at any of the three in an afternoon, which is itself a useful fact about this category.

import { PandaStack } from "@pandastack/sdk";

const client = new PandaStack({ apiKey: process.env.PANDASTACK_API_KEY });

// The loop every code-interpreter product exists to serve: ask the model for
// code, run it somewhere that is not your API process, feed the errors back.
async function runAgentTurn(question: string) {
  const sandbox = await client.sandboxes.create({
    template: "code-interpreter",
    ttlSeconds: 900,
    metadata: { purpose: "agent-turn" },
  });

  const failures: string[] = [];

  try {
    for (let attempt = 0; attempt < 5; attempt++) {
      // Whatever your model of choice just produced. It has never seen
      // your data, has strong opinions about pandas, and is very confident.
      const source = await askModelForPython(question, failures);

      await sandbox.filesystem.write("/work/step.py", source);

      const run = await sandbox.exec("python /work/step.py", {
        timeoutSeconds: 120,
      });

      if (run.exitCode === 0) return run.stdout;
      failures.push(run.stderr.slice(0, 4000));
    }

    throw new Error("model failed to produce working code in 5 attempts");
  } finally {
    // The unglamorous line that keeps the invoice unglamorous.
    await sandbox.kill();
  }
}

Note what this loop is actually doing: taking a string that a statistical model produced from a prompt a stranger typed, writing it to disk, and asking a computer to execute it. Five times. With a retry. We have collectively decided this is a normal thing to build, and it is — but only because the execution happens somewhere disposable. Run that same loop in your API process and you've built a remote code execution vulnerability with a product roadmap and a pricing page.

The isolation question, honestly

Here's where I have to be careful, because isolation is the dimension where vendors are most tempted to be vague and readers are most tempted to accept a blog post as evidence. Both Vercel Sandbox and E2B describe VM-level, microVM-based isolation in their public documentation. That's a good sign, and it's also exactly where you should stop reading me and start reading them. Open their current security or architecture pages and answer three questions: what is the boundary, who else is inside it, and what does the vendor commit to in writing rather than in a launch blog?

The reason this deserves ten minutes of your time rather than a checkbox is straightforward: model-generated code is untrusted code. Not "probably fine" code — untrusted in the formal sense, because the input that produced it came from outside your trust boundary and the thing that wrote it cannot be reasoned about. If the boundary between that code and everyone else's is a shared kernel, then a single kernel CVE is a shared-fate event across every tenant on the host. Container escapes are not theoretical, they are a recurring annual genre. A container is a polite suggestion to the kernel; a hypervisor boundary is an argument the kernel doesn't get to have.

A hardware-virtualized boundary changes the shape of that risk. The guest gets its own kernel, so a guest kernel bug is contained to one tenant's VM. The attack surface exposed to the guest shrinks from the whole Linux syscall interface to a small virtio device model. It doesn't make you invulnerable — a VMM has its own CVE history — but it means the thing an attacker has to break is orders of magnitude smaller than the thing they'd have to break in a shared-kernel setup.

This is the part PandaStack was built around, so here are the specifics rather than adjectives. Every sandbox is its own Firecracker microVM with its own guest kernel and its own network namespace — 16,384 pre-allocated /30 subnets per agent host, so the namespace, veth pair and NAT rules are already standing before your create request arrives. The usual objection is that VMs are slow to start; that's a boot-path problem, not a virtualization problem. Every create restores a baked snapshot instead of cold-booting, which is a ~49ms restore step inside a p50 of 179ms end-to-end (p99 around 203ms). The only ~3s cold boot is the very first spawn of a template, before its snapshot has been baked. Forking a running sandbox — copy-on-write memory, reflinked rootfs — is 400-750ms on the same host and 1.2-3.5s cross-host.

Same loop as above, in Python, with the two lines that separate a demo from a system: an explicit TTL and a kill in a finally block.

from pandastack import Sandbox

# ttl_seconds is the backstop for the day your process dies mid-loop and
# never reaches the finally block. Always set it. Ask me how I know.
sbx = Sandbox.create(template="code-interpreter", ttl_seconds=900)

try:
    source = ask_model_for_python(question)  # freshly hallucinated, unreviewed
    sbx.filesystem.write("/work/analysis.py", source)

    run = sbx.exec("python /work/analysis.py", timeout_seconds=600)
    if run.exit_code != 0:
        raise RuntimeError(run.stderr)

    print(sbx.filesystem.read("/work/report.csv"))

    # Freeze this exact machine state so the next turn starts warm
    # instead of re-installing the same three gigabytes of wheels.
    snapshot_id = sbx.snapshot()

    # Branch it: copy-on-write memory + reflinked disk, 400-750ms same-host.
    branch = sbx.fork()
    print(branch.exec("python -c 'print(1)'", timeout_seconds=30).stdout)
    branch.kill()
finally:
    sbx.kill()

Which should you actually pick

In rough order of how often I see each case, and yes, the first two are recommendations for the other guys.

1. You're already all-in on Vercel and the workload is short — pick Vercel Sandbox. If your app deploys there, your team lives in that dashboard, and the job is "run this generated snippet for a few seconds and show the user the output," adding a second vendor buys you nothing but a second on-call rotation. Integration gravity is a real engineering asset when it's pulling in the direction you were already going.
2. You want the most mature agent-focused SDK ecosystem available today — pick E2B. They've been building specifically for the code-interpreter and agent use case longer than most of the field, the templates and process/filesystem APIs reflect that, and there's a body of community examples that will save you a week. If your team's constraint is developer-time rather than isolation posture or unit economics, that maturity is worth paying for.
3. You need long-running or stateful sessions — look past both. If your agent's job is a 30-minute build, a multi-hour training run, or a session that must survive across user turns with real state, check the duration ceilings first; this is where hosted, request-shaped compute tends to run out of road. PandaStack sandboxes live until their TTL, can be marked persistent, and can be snapshotted and forked to resume warm.
4. The isolation boundary is a compliance artifact, not a preference — get it in writing. If you run strangers' code for regulated customers you need a documented hardware-virtualization boundary, per-tenant network isolation and controllable egress, from a vendor willing to write it down. Ask all three of us what happens when a guest kernel CVE drops on a Friday.
5. You need to self-host or run in your own cloud account — that narrows the field fast. Data residency, air-gapped environments and "our security team will not approve outbound sandbox traffic" are all real constraints. PandaStack is Apache-2.0 and the same agent runs on your KVM hosts; verify E2B's current self-hosting story with them; Vercel Sandbox is hosted by design.
6. You need a database or a deployed app next to the sandbox — count the vendors. If your agent must write to a real Postgres it can also branch, or serve the app it just built at a stable URL, that's one bill or three. PandaStack runs managed Postgres 16 (30-90s create) and git-driven app hosting on the same microVM primitive.

Migration: what actually changes when you swap providers

Good news first: the core primitives across every serious sandbox API are close to isomorphic. Create a sandbox from a template, write a file, run a command with a timeout, read stdout/stderr/exit code, destroy it. If you wrapped that in a thin interface — and you should, on day one, it's forty lines — swapping providers is mostly an afternoon of renaming camelCase to snake_case and arguing about whether the result field is exit_code or exitCode. The bad news is that none of the pain is in the primitives. It's in the things nobody puts in the migration guide.

• Templates and environment definition — this is the real lock-in surface. Every provider has a different answer for how an environment gets defined, versioned and warmed: a Dockerfile, a proprietary template format, or an install step you run at session start. Rebuilding that is where your migration week goes, and it's worth designing your environment as a Dockerfile from the beginning purely so it's portable.
• Timeout and lifetime semantics — if you built against a platform with a short hard ceiling, your code is full of chunking, checkpointing and resume logic that exists only to route around that limit. Moving to a provider with longer sessions is easy; moving the other direction quietly rewrites your control flow. Read the ceiling before you write the loop.
• Egress policy — the thing that silently breaks in staging. Sandboxes that reached your package mirror, your model endpoint or a customer's API on one provider may be blocked, NAT'd differently or on a new IP range on another. Inventory every outbound dependency before you migrate, including the ones that only appear under load.
• Identity and credentials — how the sandbox proves who it is when it calls back into your systems. Platform-native compute often gets this free from the surrounding platform, which is lovely right up until you leave and discover you now mint, scope and rotate short-lived tokens yourself.

The cheap insurance is a provider interface with four methods — create, exec, writeFile, destroy — and a rule that nothing else in your codebase imports a vendor SDK directly. It costs an hour and it converts a migration from a project into a pull request.

How to decide in a day

Don't build a matrix. Build the smallest thing that would embarrass you in production and run it on each candidate: create a sandbox, install your two ugliest dependencies, run your longest realistic job, try to reach an endpoint you're supposed to be blocked from, and kill everything. Time each step from the region you'll deploy to. Then read the two pages nobody reads — limits and security — and ask each vendor the one question your architecture depends on. An afternoon of that beats any comparison post, including this one.

If you want the deeper single-vendor comparisons, /blog/pandastack-vs-vercel-sandbox goes into the platform-native trade-off in detail, /blog/pandastack-vs-e2b covers the isolation and fork story head-to-head, and /blog/best-vercel-sandbox-alternatives-2026 surveys the wider field if neither of these two ends up fitting.