PandaStack vs Koyeb: an honest comparison

Koyeb and PandaStack both answer the sentence "I have code, please run it in the cloud." That's where the overlap ends and the interesting part starts. Koyeb is a serverless platform: you point it at a Git repo or a container image and it deploys, routes, and autoscales a long-lived service across global regions, including scaling to zero when nobody's calling it. PandaStack is a Firecracker microVM sandbox platform: the primary object is a disposable, hardware-isolated VM that your program creates through an API, execs into, snapshots, forks, and throws away — usually to run code somebody or something else wrote.

Both products can host an app from a Git push. Only one of them is designed around the assumption that the code inside the box is hostile and lives for eleven seconds. This post walks the axes that actually decide the call, and is explicit about where Koyeb is the better tool.

The core primitive: a deployed service vs an API-created sandbox

The cleanest way to tell these platforms apart is to ask two questions: what is the unit of work, and who creates it?

• Koyeb: the unit is a service you deploy. You (or your CI) connect a repo or push an image, Koyeb builds and runs it, gives it a URL, puts it in one or more regions, and autoscales the instance count against traffic — scaling down to zero when it's idle and waking on the next request. The service is a durable, named thing in your account that you operate over weeks and months.
• PandaStack: the unit is a sandbox your program creates at runtime. Your backend calls Sandbox.create(), gets an isolated Linux guest with its own kernel back in milliseconds, runs a command in it, reads a file out of it, maybe forks it three ways, then kills it. Nobody deploys a sandbox. There are potentially thousands of them alive right now and none of them existed when you shipped.

That difference in who creates the environment cascades into everything below. A deploy-oriented platform optimizes for stability, routing, and steady-state cost. A sandbox-oriented platform optimizes for create latency, per-task isolation, and disposal. Neither is a deficiency; they're different jobs that happen to share the phrase "run my code."

Head-to-head, dimension by dimension

Koyeb's side is described qualitatively on purpose — I'm not going to quote latency or pricing figures for someone else's platform. Confirm the specifics against Koyeb's docs.

• Core primitive — PandaStack: a disposable microVM created by an API call at runtime, with a lifecycle of create → exec → snapshot/fork → kill. Koyeb: a long-lived service deployed from a repo or container image, with a lifecycle of deploy → route → autoscale → redeploy.
• Isolation boundary — PandaStack: every sandbox is a Firecracker microVM with its own guest kernel behind a KVM hardware boundary, plus a dedicated Linux network namespace per sandbox. The design assumption is that the workload is hostile. Koyeb: a managed serverless runtime with per-service isolation appropriate for running your own trusted application code; verify the current isolation technology and its guarantees in their docs if you intend to run arbitrary third-party code on it.
• Cold start / scale-to-zero — PandaStack: no warm pool exists at all. Every create restores a baked Firecracker snapshot on demand — ~49ms for the restore step, ~179ms p50 and ~203ms p99 end-to-end — and the only slow path is the very first spawn of a brand-new template (~3s cold boot, which then bakes the snapshot). Koyeb: autoscaling with scale-to-zero for idle services and a wake on the next request; the relevant question for you is what that wake costs for your image and runtime, which is worth measuring rather than reading.
• Snapshot + fork — PandaStack: first-class. snapshot() captures the full VM (memory and disk); fork() branches a running sandbox copy-on-write — memory via MAP_PRIVATE, rootfs via reflink — in 400–750ms same-host, 1.2–3.5s cross-host. Koyeb: no real equivalent, because branching a running instance's live memory isn't a thing an app-deployment platform needs to do.
• Git-driven app hosting — PandaStack: yes, built on the same microVM substrate — connect a repo, framework auto-detection, blue-green deploys, push-to-deploy. Koyeb: yes, and this is its home turf — Git or container-image deploys with builds, health checks, rollbacks, and traffic management as the main event rather than a satellite feature.
• Managed databases — PandaStack: managed PostgreSQL 16 where each database is its own dedicated Firecracker microVM with a durable volume; create takes 30–90s. Koyeb: offers managed Postgres alongside its compute; check current tiers, limits, and regions in their docs.
• Global footprint — PandaStack: regional, oriented around wherever your agent backend runs; not a global edge network. Koyeb: a mature global region footprint with edge routing, which is a real and substantial advantage if user-facing latency across continents is your problem.
• Self-host and licensing — PandaStack: the control-plane API and per-host agent are Apache-2.0 and run on your own Linux KVM hosts; the hosted service and the self-hosted stack are the same binaries. Koyeb: a proprietary managed platform you run on their infrastructure.
• Pricing model shape (qualitative) — PandaStack: usage-based around ephemeral sandbox lifetime, where idle trends toward zero because there's no warm pool to keep alive. Koyeb: usage-based on running instance resources with scale-to-zero to cut idle cost. Confirm actual rates on each provider's pricing page; both models change.

The dividing line: untrusted and agent-generated code

This is the axis where the two products genuinely diverge, and it's worth being precise instead of hand-wavy.

When you deploy a service to Koyeb, you wrote the code. You reviewed the dependencies. It's your artifact, running as a tenant on their platform, and the isolation the platform provides is between you and other customers. That's the right model for an application, and it's the model essentially every PaaS is built on.

Now change one variable: the code was written thirty milliseconds ago by a language model, or pasted in by an anonymous user, or is a package your agent decided to pip install without asking anyone. Now the threat isn't "another tenant" — it's your own service's runtime input. You need a boundary between one request and the next, between tenant A's job and tenant B's job, and you need to create and destroy that boundary constantly and cheaply. A long-lived deployed service, however well isolated from other customers, doesn't naturally give you a fresh kernel per request.

PandaStack is built entirely around that second case: a fresh Firecracker microVM per task, with its own guest kernel, its own network namespace (there are 16,384 pre-allocated /30 subnets per agent host so allocation isn't a bottleneck), egress controls, a TTL, and an idle reaper. If you wanted per-request microVM isolation on top of a deployment platform, you'd be building an orchestrator for disposable VMs yourself — which is a real project, not a weekend. For the longer argument on why the kernel boundary matters here, see /blog/why-docker-is-not-a-sandbox.

Cold starts and scale-to-zero, from two directions

Both platforms have a scale-to-zero story, but they mean different things by it, and conflating them leads to bad architecture decisions.

Koyeb's version answers: my service gets no traffic at 3am, so don't bill me for idle instances, and bring one back when a request arrives. The cost you're optimizing is the steady-state cost of a service that's mostly asleep, and the latency you care about is the first request after a quiet period. That's the classic serverless bargain and Koyeb handles the plumbing for you.

PandaStack's version is stranger: there is nothing to scale down because nothing was running. There's no warm pool of idle VMs by design — every create restores a baked snapshot on demand, which is why the numbers land at ~179ms p50 and ~203ms p99 with a ~49ms restore step rather than needing a pre-warmed fleet to hide a slow boot. Idle cost trends to zero not because a scheduler shut something down, but because the architecture never kept anything up. The trade-off is honest and worth stating: snapshot-restore fixes the guest's vCPU and RAM at bake time, so you can't resize a VM at restore — you bake a bigger template instead. That's a real constraint, and it's the kind of thing you want to know before you design around it.

PandaStack also has hibernate/wake for long-lived sandboxes and apps that should sleep between sessions, which is closer in spirit to the serverless version. /blog/always-on-vs-scale-to-zero-agent-infra digs into when each model actually saves money rather than just moving the cost around.

Snapshot and fork: the capability with no Koyeb equivalent

If there's one PandaStack feature that has no counterpart on a deployment platform, it's forking. Not because Koyeb neglected it — because it makes no sense for what Koyeb does. Nobody wants their production API instance to branch itself mid-request.

A PandaStack fork clones a running sandbox copy-on-write. Guest memory is shared through MAP_PRIVATE so the kernel only copies pages on write, and the rootfs is cloned with a reflink so disk data is shared until something touches it. Same-host forks land in 400–750ms; cross-host forks are 1.2–3.5s because the snapshot has to move. The practical shape: get one environment into an expensive known state — dependencies installed, a 4GB dataset loaded, a REPL warmed, a repo cloned and built — then fork it N times and let N agents explore N different branches from the identical starting memory, without re-running any of the setup.

That's the primitive behind best-of-N code fixes, tree-of-thought agent rollouts, and "try five migrations and keep the one that doesn't corrupt anything." If your workload has that shape, evaluate forking hard — it's the thing that's genuinely difficult to reproduce elsewhere. /blog/snapshot-and-fork-explained covers the mechanics.

App hosting: the part where they really do overlap

Here's the honest overlap. PandaStack ships git-driven app hosting: connect a GitHub repo, it detects the framework (Next, Vite, CRA, plain Node, static, Python), installs the runtime version your repo declares, builds it, health-checks it, and does a blue-green flip to the new sandbox with the old one torn down after. Push to the tracked branch and it redeploys. That is, functionally, a thing Koyeb also does.

But I'd rather you calibrate correctly than be oversold. PandaStack's app hosting exists because agents need somewhere to deploy what they build, and because the microVM substrate was already there. Koyeb's app hosting is the entire product, with years of investment in build pipelines, global routing, autoscaling policies, health management, and the hundred unglamorous things a hosting platform needs. If "host my production web service, globally, reliably, for the next three years" is the sentence you're trying to satisfy, a dedicated serverless platform is the more mature answer and I'd say so to a customer's face. PandaStack's app hosting is strongest when the app is downstream of a sandbox workflow — the thing your agent just built, the preview environment for a generated project, the per-tenant service that needs a hard isolation boundary anyway.

Self-hosting and open source

This one is structural rather than a matter of degree. PandaStack's control-plane API and per-host agent are Apache-2.0 licensed and designed to run on your own Linux KVM hosts — anything with /dev/kvm. The hosted product and the self-hosted stack are the same binaries, so you can read the execution layer, audit what happens to code you're worried about, keep every byte inside your own VPC or rack, and leave without a migration project. For regulated environments, air-gapped deployments, or anyone whose security review includes the phrase "where exactly does the customer data go," that tends to matter more than any latency number.

Koyeb is a proprietary managed platform running on its own infrastructure, which is the normal and reasonable shape for a serverless product — the whole value proposition is that you don't operate it. If you actively don't want to run Linux hosts, that's a feature, not a gap. /blog/self-hosted-code-execution-sandbox goes deeper on when self-hosting is worth the operational cost and when it very much isn't.

Regions and global footprint

Being straight about this: a mature managed platform's global region footprint is a real advantage, and it's one I'm not going to argue around. If your users are spread across continents and your p99 is dominated by network round-trips rather than compute, deploying close to them is the single highest-leverage thing you can do, and Koyeb's regional coverage and edge routing are built for exactly that.

PandaStack's placement story is different in kind. Sandboxes are usually created by a backend, not by an end user's browser, so the latency that matters is between your agent orchestrator and the sandbox — a backend-to-backend hop you control by placing your orchestrator near the agent hosts. That works well for the sandbox use case and is not a substitute for a global edge network for user-facing traffic. If you need both, use both: serve the app from a global platform and call the sandbox API from inside it.

What PandaStack code actually looks like

The API surface is the clearest tell for what a platform is optimized for. PandaStack's verbs are create, exec, filesystem, snapshot, fork, kill — you'll notice "deploy" isn't among them. The Python SDK reads PANDASTACK_API_KEY from the environment:

from pandastack import Sandbox

# Create a disposable, hardware-isolated Firecracker microVM.
# Snapshot-restore on every create: ~179ms p50, ~203ms p99. No warm pool.
sbx = Sandbox.create(
    template="code-interpreter",
    ttl_seconds=600,
    metadata={"tenant": "acme", "task": "analyze-upload"},
)

# Drop the agent's generated script into the guest filesystem
sbx.filesystem.write("/work/analyze.py", generated_code)

# Run it. Whatever it does, it does inside its own guest kernel.
result = sbx.exec("python /work/analyze.py", timeout_seconds=120)
print(result.stdout, result.exit_code)

# Done. The entire environment goes away.
sbx.kill()

And the fork path, which is the part with no deployment-platform analogue — pay the expensive setup once, then branch it:

from pandastack import Sandbox

# Pay for the expensive setup exactly once
base = Sandbox.create(template="code-interpreter", ttl_seconds=1800)
base.exec("pip install -q pandas scikit-learn")
base.filesystem.write("/work/dataset.csv", dataset_bytes)
base.exec("python /work/warm_cache.py")

# Freeze that exact state (memory + disk), then branch it N ways.
# Same-host fork: 400-750ms. Each child starts from identical memory.
checkpoint = base.snapshot()

branches = []
for strategy in ("ridge", "gbdt", "random-forest"):
    child = base.fork()  # copy-on-write: MAP_PRIVATE memory + reflinked rootfs
    child.exec(f"python /work/train.py --model {strategy}")
    branches.append(child)

# Keep the winner, kill the rest
for child in branches:
    child.kill()
base.kill()

The TypeScript SDK mirrors this shape if your orchestrator lives in Node:

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

const sbx = await Sandbox.create({
  template: "code-interpreter",
  ttlSeconds: 600,
});

await sbx.filesystem.write("/work/task.py", generatedCode);
const result = await sbx.exec("python /work/task.py");
console.log(result.stdout, result.exitCode);

await sbx.kill();

Choose Koyeb if / Choose PandaStack if

Choose Koyeb if

• Your unit of work is a long-lived service — a web app, an API, a worker, a queue consumer — that you wrote and want deployed, routed, and autoscaled without operating infrastructure.
• Global reach is a first-order requirement: users on several continents, and you want instances close to them with edge routing rather than a single region.
• You want a managed developer experience for the whole deploy lifecycle — Git or image builds, health checks, rollbacks, autoscaling policies, traffic management — as the main product rather than a feature attached to something else.
• Scale-to-zero on an idle service is the cost problem you're solving, and you'd rather the platform handle the wake path than build anything yourself.
• You have no appetite for running Linux KVM hosts and consider that a permanent, deliberate choice — a completely legitimate position.
• You need GPU-backed inference deployed and served as a managed endpoint alongside your other services.

Choose PandaStack if

• You're executing code you don't trust — LLM-generated scripts, user submissions, agent tool calls — and want a fresh guest kernel per task rather than per deployment.
• You need per-request or per-tenant isolation created programmatically at runtime, thousands of times a day, where create latency is a product metric (~179ms p50, ~203ms p99 via snapshot-restore).
• Forking is central to your workload: snapshot an expensive warmed state once, then branch it in 400–750ms same-host for parallel agent rollouts or best-of-N exploration.
• You need to self-host on your own KVM hosts under Apache-2.0 for compliance, data residency, auditability, or to avoid a control plane you can't inspect.
• You want per-sandbox network namespaces and egress controls for untrusted code without assembling them yourself.
• Your app hosting, databases, and functions should sit on the same isolation substrate as your sandboxes, on one bill.

And as with most of these comparisons, "both" is frequently the correct answer rather than a diplomatic dodge. Deploy your product's front end and API on a global serverless platform; call a sandbox API from inside it for the per-session, per-tool-call boxes where generated code actually runs. The failure mode isn't picking the wrong vendor — it's stretching one primitive across two jobs it was never shaped for.

The bottom line

Koyeb and PandaStack overlap on one sentence — "deploy my app from a repo" — and diverge completely on the sentence underneath it. Koyeb is a serverless platform for services you own: deployed from Git or an image, routed globally, autoscaled with scale-to-zero, managed end to end so you never touch a host. PandaStack is a microVM sandbox platform for code you don't own: a disposable Firecracker VM created by an API call in ~179ms p50, isolated by a hardware boundary with its own kernel, forkable copy-on-write in 400–750ms, and Apache-2.0 licensed so you can run the whole thing on your own KVM hosts. If your problem is hosting a global production service, Koyeb is the more direct and more mature answer. If your problem is running arbitrary code safely, thousands of times an hour, with branching state, that's what PandaStack was built to do. If you're comparing more broadly, /blog/pandastack-vs-northflank and /blog/pandastack-vs-flyio-machines cover neighboring platforms with similar honesty.