PandaStack vs Fly.io Machines: an honest comparison

PandaStack and Fly.io Machines both run on Firecracker, so it's natural to line them up. But they're aimed at different jobs. Fly Machines is a general-purpose app-hosting primitive: fast-starting Firecracker VMs you place across regions, attach volumes to, and run your own long-lived services on. PandaStack is a code-execution and sandbox platform: it spins up disposable, isolated environments on demand to run per-task, per-tenant, or untrusted code — the script an AI agent writes at runtime, the notebook cell, the CI job. Same isolation primitive, very different product around it.

I'm Ajay, founder of PandaStack. I'll be specific about how PandaStack works and deliberately general about Fly — I'm not going to quote Fly latency numbers I can't stand behind, and Fly's product surface moves fast. The goal is to help you pick the right tool, not to score points.

What are they actually for?

The cleanest test is to ask what the unit of work is and how long it lives.

• Fly.io Machines is an app-hosting platform. A Machine is a Firecracker VM you create, start, stop, and place in a region, typically to run a service, a web app, a worker, or a database you own and operate. It's built for deploying and running your software close to users, globally, with volumes for persistent state.
• PandaStack is a sandbox platform. You call an API to create an isolated Linux guest with its own kernel, exec commands, read and write files, snapshot it, fork it, or expose a port — then throw it away. The code inside is frequently code you did not write: an LLM-generated script, a user's submission, an agent's tool call.

That framing drives almost everything else. Fly optimizes for "run my app reliably in many places for a long time." PandaStack optimizes for "create a throwaway, hard-isolated box in milliseconds, run something risky in it, and dispose of it."

Head-to-head, dimension by dimension

Both are excellent at what they're designed for. Here's how they line up on the axes that usually decide the call. Fly's specifics are described qualitatively — verify current details against fly.io's docs.

• Isolation — PandaStack: every sandbox, database, and app is its own Firecracker microVM with a hardware (KVM) boundary and a separate guest kernel per task, plus a dedicated Linux network namespace per sandbox. Fly Machines: also Firecracker microVMs with per-VM kernel isolation — the same strong primitive, applied to your app instances rather than per-task disposable sandboxes.
• Create / boot model — PandaStack: snapshot-restore on every create, no warm pool of idle VMs; p50 ~179ms, p99 ~203ms (core path ~49ms), with a ~3s cold boot only on the first spawn of a template before its snapshot is baked. Fly Machines: Machines start fast from a stopped state and can auto-start on request, but the model is oriented around starting and stopping your own long-lived instances rather than restoring a per-task snapshot on every call — check their docs for current start latencies.
• Snapshot + fork — PandaStack: first-class snapshot() of the full VM (memory + disk) and copy-on-write fork() to branch a running sandbox; same-host fork 400–750ms, cross-host 1.2–3.5s. Fly Machines: centers on volume snapshots and image-based deploys; branching a running machine's live memory state by CoW fork is not the primitive it's built around.
• Networking — PandaStack: per-sandbox netns with 16,384 pre-allocated /30 subnets per agent, tokenless per-port preview URLs, and egress controls for untrusted code. Fly Machines: a full private WireGuard mesh (6PN), global Anycast, and load balancing — a richer production networking fabric for serving real user traffic.
• Primary use case — PandaStack: disposable, isolated execution environments on demand — AI agents, code interpreters, per-user playgrounds, ephemeral CI. Fly Machines: hosting long-lived apps and services globally, close to users.
• Pricing model (qualitative) — PandaStack: usage-based, priced around ephemeral per-task sandbox lifetime where idle cost trends toward zero because there's no warm pool. Fly Machines: usage-based on running Machines and attached resources, well suited to always-on or scale-to-a-floor services; a stopped Machine isn't billed for compute. Confirm exact rates on each provider's pricing page.

Running untrusted or AI-generated code

Here's the important nuance: because Fly Machines are also Firecracker VMs, they give you a genuine hardware-isolation boundary. This isn't a shared-kernel-container story where PandaStack has an isolation edge and Fly doesn't — both put a real guest kernel between the workload and the host. The difference is what's wrapped around that primitive.

PandaStack is built end-to-end for the assumption that the code inside is hostile and short-lived: snapshot-restore so a fresh, clean VM per task is cheap; a dedicated network namespace and egress controls per sandbox; a disposable lifecycle with TTLs and idle reaping; and an SDK whose verbs are exec, filesystem, snapshot, fork, and delete. On Fly, you'd be assembling that per-task-sandbox lifecycle yourself on top of a hosting primitive designed for long-lived instances. Both can isolate; PandaStack does the orchestration of disposability for you.

Snapshots and forking

This is where PandaStack's sandbox heritage shows most. A PandaStack sandbox is a live, stateful machine you can pause, snapshot, and branch.

• snapshot() captures the full VM — memory and disk — so you can restore the exact running state later.
• fork() branches a running sandbox using copy-on-write: memory via MAP_PRIVATE and the rootfs via XFS reflink, so a fork shares pages with its parent until it writes. Same-host fork lands in 400–750ms; cross-host 1.2–3.5s.
• hibernate() / wake() lets a sandbox sleep when idle and resume on the next request, which is how scale-to-zero works without rebuilding state from scratch.

For agent loops this is structural: run several candidate branches from a common checkpoint, keep the best, discard the rest — forking a VM in under a second makes tree-of-thought exploration cheap. Fly's snapshot story is oriented around volume snapshots and reproducible image deploys, which is the right shape for hosting an app, not for branching a running machine's live memory.

Where Fly.io Machines is the better tool

Let me be direct, because these are real strengths and not in PandaStack's wheelhouse. If your unit of work is a long-lived app or service you own and want to serve to users worldwide, Fly is purpose-built for that: global regions and Anycast, a private WireGuard mesh, load balancing, `fly deploy` workflows, and a mature volumes-and-networking story for production traffic. PandaStack does ship git-driven app hosting for deploying what an agent builds, but PandaStack is not a global multi-region application platform, and it isn't trying to be. If you're picking where to run your production API in ten regions, that's Fly territory, not PandaStack's.

Self-hosting, databases, and the surrounding platform

PandaStack's core is Apache-2.0 licensed and designed to be self-hostable on your own KVM hosts, so you can run untrusted workloads inside your own VPC or hardware without vendor lock-in. Fly is a managed cloud you run on their infrastructure. Beyond ephemeral sandboxes, PandaStack also ships managed PostgreSQL 16 — each database is its own dedicated microVM with a durable volume, created in about 30–90s — plus durable volumes and git-driven app hosting for serving what an agent produces. Fly likewise has managed Postgres and volumes; the difference is orientation, not a missing checkbox: PandaStack packages these as building blocks for isolated per-task execution, Fly as building blocks for hosting your services.

What using PandaStack looks like

PandaStack ships a Python SDK (pandastack), a TypeScript SDK (@pandastack/sdk), and a CLI (pandastack). The client reads PANDASTACK_API_KEY from the environment and talks to https://api.pandastack.ai. Here's the canonical disposable-sandbox flow — create an isolated VM, exec a command, then delete it:

from pandastack import PandaStack

ps = PandaStack()  # reads PANDASTACK_API_KEY from the environment

# Spin up a disposable, hardware-isolated Firecracker microVM
# (p50 ~179ms via snapshot-restore; no warm pool to manage)
sb = ps.sandboxes.create(
    template="base",
    ttl_seconds=300,
    metadata={"task": "run-agent-snippet"},
)

# Execute code the agent generated — confined to this VM's own kernel
result = sb.exec("python -c 'import sys; print(sys.version)'")
print(result.stdout, result.exit_code)

# Throw the whole environment away when the task is done
sb.delete()

The shape to notice: you're not deploying and operating a long-lived instance, you're creating and disposing of an environment. create, exec, filesystem, snapshot, fork, delete. That API surface is the tell for what PandaStack is optimized for — and the tell for when Fly's deploy-and-run model is the better fit instead.

Pick Fly.io Machines when… / Pick PandaStack when…

Pick Fly.io Machines when

• Your unit of work is a long-lived app, service, or worker you own and operate.
• You need global placement — running close to users across many regions with Anycast and a private mesh network.
• You want a mature deploy-and-run workflow with load balancing, volumes, and production networking for real user traffic.
• You're hosting your own trusted software and want a fast, VM-isolated hosting primitive to run it on.

Pick PandaStack when

• You're running untrusted or AI-generated code and want disposable, hardware-isolated environments per task, created and destroyed on demand.
• You need fast per-task isolation: p50 ~179ms create via snapshot-restore, with no warm pool to keep running or pay for.
• Your agent benefits from stateful sandboxes — snapshot a setup, fork it in under a second to explore branches, hibernate and wake for scale-to-zero.
• You want per-sandbox network isolation and egress controls for code you don't trust, out of the box.
• You need to self-host on your own KVM hosts — the core is Apache-2.0.

And these aren't mutually exclusive: it's entirely reasonable to host your production agent product on Fly Machines globally, and call PandaStack from inside it for the per-session, per-tool-call isolated sandboxes where the agent runs code it generated. Match each workload to the tool whose lifecycle and isolation model fit it.

Weighing other options too? The full guide to E2B alternatives at /blog/e2b-alternatives compares the field across isolation model, hosted-vs-self-host, cold-start, forking, and platform breadth.