PandaStack vs Coder

I build PandaStack, so the bias is declared up front — and then immediately undercut, because the most useful thing I can tell you about this comparison is that it is mostly not a comparison. Coder and PandaStack get shopped against each other because both answer the sentence "give me a machine in the cloud to run code on," and search engines cannot tell those two sentences apart. But Coder provisions long-lived remote development workspaces for human engineers on infrastructure you already own, and PandaStack is a hosted API that creates short-lived Firecracker microVMs for programs. If you are genuinely torn between them, the likeliest explanation is not that one is better — it is that you haven't yet worked out which of the two categories you're shopping for. That is what this post is for.

Two shapes of "give me a machine"

Two different products hide under the same description, and most of the confusion here comes from not naming the split.

A workspace is long-lived and stateful. There is roughly one per developer, it holds uncommitted changes and shell history and a running dev server, it is optimised for a human's working day, and its defining feature is that an editor attaches to it — VS Code Remote, JetBrains Gateway, a web terminal, SSH. It sleeps at night and it is still there on Monday. Its success metric is "the engineer stopped thinking about their machine."

A sandbox is short-lived and disposable. There are as many as your code decided to create in the last minute, it holds nothing you care about afterwards, and no human ever attaches anything to it. Its defining features are boot latency, per-run isolation, and the ability to fork or snapshot state rather than rebuild it. Its success metric is "the machine was not the bottleneck."

The tell is who opens the environment. If it is a person with an editor, you want a workspace. If it is a `for` loop, you want a sandbox.

What Coder is

Coder is a self-hosted platform for provisioning remote development workspaces. You run the control plane yourself, define workspace types as Terraform templates, and those templates provision whatever compute you point them at — Kubernetes pods, EC2 or GCE instances, Docker containers on a build box, whatever your organisation already operates. Developers pick a template, get a workspace, and attach with VS Code Remote, JetBrains Gateway, the browser terminal, or plain SSH. The open-source project is genuinely well regarded, and the company sells an enterprise tier on top of it.

The design centre is clear and, I think, correct for its audience: the platform team owns the infrastructure, encodes it in Terraform, and hands engineers a self-service door to it. Coder is shaped around solving that, rather than around abstracting your infrastructure away from you.

What PandaStack is

PandaStack is a hosted sandbox API. You call create, and a Firecracker microVM with its own guest kernel comes back running in about 179ms at p50 (~203ms p99, of which the snapshot-restore step is roughly 49ms). You exec a command, read and write files, optionally snapshot or fork it, and destroy it. There is no editor and no workspace belonging to a named human. There is also no infrastructure to own: no control plane, no cluster to size, no Terraform state to reconcile. The primitives are the ones a program needs — exec, filesystem, snapshot, fork, TTL — optimised for the moment between "my code decided it needs a machine" and "my code has a machine."

Side by side

• Primary consumer — Coder: a human engineer with an editor attached, working a normal day. PandaStack: a program — an AI agent, a CI job, or a backend executing customer-submitted code.
• Unit of compute — Coder: a workspace, long-lived and stateful, roughly one per developer. PandaStack: a sandbox, ephemeral by default, with a TTL and an expectation of being destroyed.
• Provisioning model — Coder: Terraform templates you write, provisioning compute in your own accounts and networks. PandaStack: one API call that returns a running microVM; no infrastructure for you to model or operate.
• Where it runs — Coder: self-hosted on infrastructure you own, the point for teams with data-residency or air-gap needs. PandaStack: a hosted API; the per-host agent is open source, but self-running the platform is not the supported path.
• Isolation boundary — Coder: whatever your Terraform template provisioned — a Kubernetes pod, a container, a cloud VM. Isolation is a property of the template you wrote, not of Coder itself. PandaStack: always a Firecracker microVM with its own guest kernel, plus a dedicated network namespace and TAP device per sandbox.
• Start time — Coder: depends on the compute your template provisions, so measure your own; verify current start-time features against Coder's docs. PandaStack: ~179ms p50 create (~203ms p99) via snapshot-restore; ~3s for a brand-new template's first cold boot.
• Editor integration — Coder: the point of the product — VS Code Remote, JetBrains Gateway, web terminal, SSH. PandaStack: none, deliberately. The API is the only surface.
• Fork and snapshot — Coder: not the model; workspaces persist, they do not branch. PandaStack: first-class. Snapshot captures memory plus disk; a same-host fork clones a running VM copy-on-write in 400–750ms (1.2–3.5s cross-host).
• Fan-out shape — Coder: bounded by how many workspaces a team of humans plausibly opens. PandaStack: designed for it — each agent pre-allocates 16,384 /30 subnets, so the practical ceiling is host memory and CPU rather than networking.
• Cost shape — Coder: you pay your cloud for the compute the template provisioned, plus any Coder licensing; check their pricing. PandaStack: usage while a sandbox is alive, no per-seat notion — a thousand sandboxes belong to an API key, not a thousand people.
• Operational burden — Coder: you run the control plane, the templates, and the underlying compute; that is the cost of the control it gives you. PandaStack: you run nothing; that is the cost of not having the control.
• Best fit — Coder: standardising engineers' daily development environments on infrastructure you must own. PandaStack: executing untrusted or model-generated code, agent rollouts, CI fan-out, per-tenant execution inside a product you sell.

Provisioning: a Terraform template vs. an API call

This is the deepest architectural difference, and it is not a case of one approach being lazier than the other. Coder's model is that a workspace is infrastructure, so you describe it the way you describe infrastructure. An illustrative pseudo-template — not a working Coder template, just the shape of the idea — looks roughly like this:

# ILLUSTRATIVE PSEUDO-TEMPLATE — shows the shape of the model, not real syntax.
# Check Coder's own template documentation for the current provider + schema.

resource "workspace_agent" "main" {
  os   = "linux"
  arch = "amd64"

  startup_script = <<-EOT
    set -euo pipefail
    git clone https://github.com/acme/webapp ~/webapp
    cd ~/webapp && npm ci
  EOT
}

# The compute is YOUR compute, in YOUR account, on YOUR network.
resource "kubernetes_pod" "workspace" {
  metadata {
    namespace = "dev-workspaces"
    name      = "ws-${local.username}"
  }
  spec {
    node_selector = { "workload" = "dev" }
    container {
      image = "registry.internal.acme.com/dev-base:2026.08"
      resources {
        requests = { cpu = "2", memory = "8Gi" }
        limits   = { cpu = "8", memory = "16Gi" }
      }
      env {
        name  = "ARTIFACTORY_URL"
        value = var.internal_artifactory
      }
    }
  }
}

Look at what that buys you. The workspace lands in your VPC, on your node pool, with your internal registry, your IAM roles, your compliance boundary. If a development environment genuinely has to reach an internal service that will never touch the public internet, Terraform-in-your-account is not overhead — it is the only model that works. Platform teams fluent in Terraform get to express org-specific infrastructure no hosted vendor would ever model for them, and to change it in a pull request. That is a real strength and I am not going to pretend otherwise.

The cost is that you own it. Somebody sizes the node pool, somebody handles the upgrade, somebody gets paged when the cluster runs out of capacity at 09:05 on Monday because everyone started their workspace at once. For a platform team, that is a Tuesday. For a three-person startup shipping an AI product, that is the quarter.

The API model makes the opposite trade. There is nothing to describe because there is nothing to own:

from pandastack import Sandbox

# No Terraform, no cluster, no control plane. ~179ms p50 to a running microVM.
sbx = Sandbox.create(template="base", ttl_seconds=900)
try:
    sbx.filesystem.write("/work/solution.py", model_written_code)
    result = sbx.exec("python /work/solution.py", timeout_seconds=60)
    print(result.exit_code, result.stdout[-500:])
finally:
    # Kernel, memory, and rootfs all cease to exist. Nothing to reconcile.
    sbx.destroy()

You cannot put that sandbox on your internal VPC by writing a Terraform resource, because there is no Terraform resource. That is the trade, stated honestly in both directions: control versus not having to have an opinion.

Isolation: a property of your template, not of the platform

This deserves saying plainly rather than becoming a scorecard row. Coder workspaces commonly run as containers or Kubernetes pods, and sometimes as full cloud VMs — because the template decides. So "how well isolated is a Coder workspace?" has no single answer; it is exactly as isolated as whatever you provisioned. A template that gives each workspace its own EC2 instance gives you hardware-virtualization isolation. One that packs workspaces as pods onto a shared node gives you namespace and cgroup isolation over a shared host kernel. Both are legitimate choices with different threat models, and the platform is not making that choice for you — you are, in Terraform, and you should know which one you picked.

PandaStack does not have that dial. Every sandbox is a Firecracker microVM with its own guest kernel behind a hardware-virtualization boundary — the same VMM class AWS Lambda uses to run untrusted code from many customers on shared fleets — with a dedicated network namespace, veth pair, and TAP device. That uniformity is boring in the best way, and it matters most in one situation: when the code you are about to run was not written by anyone you can hold responsible. Agent-generated code, customer-submitted scripts, an arbitrary repo someone pasted a URL for. There the boundary must not depend on the code inside behaving, and you do not want the answer to depend on which template someone picked six months ago.

None of that is a security criticism of Coder. A platform whose whole premise is "provision the infrastructure you already have" is correctly agnostic about what that infrastructure is. It just means the isolation question moves from the vendor's datasheet to your own template review, which is a fine place for it as long as somebody is actually reviewing.

Lifecycle and cost: a week vs. four seconds

A workspace lives all week. It idles overnight, sleeps through the weekend, and the value proposition explicitly includes that your half-finished refactor is still there on Monday. The cost curve you are managing is "how do I stop paying for machines that are idle most of the time," and the workspace-world answer is scheduled shutdown and idle policies.

A sandbox lives for one agent turn. It might exist for four seconds. The cost curve is completely different: you are not worried about idle machines, you are worried about whether three hundred machines an hour is affordable and whether a crashed orchestrator left a hundred running. That is why every sandbox carries a `ttl_seconds` and reaps itself, and why the billing unit is machine-seconds with no seat concept. "Who owns this machine?" has no answer when it existed for four seconds to run one test.

Both worlds say "scale to zero," and they mean different things by it. In the workspace world it means a machine you still own stops burning money while its human sleeps, and wakes back to the same state. In the sandbox world it means the machine ceases to exist, and zero is the resting state rather than the achievement. PandaStack has a hibernate-and-wake path for the cases that genuinely need to hold state — managed Postgres databases, hosted apps — but that is the exception the product supports, not the shape it is built around.

Self-hosted vs. hosted, without the marketing

Coder's self-hosting is not a limitation dressed up as a feature; for a large slice of its audience it is the requirement that eliminated every hosted alternative. If your source cannot leave your network, if you have data-residency obligations, if you operate air-gapped, or if the environment must sit inside a VPC to reach internal services, then a hosted API is not a cheaper option — it is not an option. Coder is built for that constraint, and that is why it wins those deals.

I should be careful about the mirror-image claim, because I have seen competitors overclaim it and it is embarrassing. PandaStack's per-host agent is open source, and you can run it on your own KVM hosts. That is real, and people do it. But I will not tell you it is a like-for-like self-hosted alternative to a platform whose self-hosted deployment is the primary supported product with the documentation, upgrade path, and enterprise support to match. Ours is an open-source component of a hosted service; theirs is a self-hosted platform. If self-hosting is your hard requirement rather than a nice-to-have, weigh that difference honestly, and weigh it against Coder's current documentation rather than my summary of it.

The flip side is the operational burden, and it is symmetric. Hosted means you cannot fix it at 3am; self-hosted means you must. Pick the failure mode you would rather have.

What an AI-agent workload actually needs

Worth spelling out, because this is a genuinely different product requirement and not just "a dev environment but faster." An agent workload wants four things, none of which a workspace product has reason to prioritise.

1. Create latency measured in milliseconds, because the machine sits in the middle of a loop and the loop runs hundreds of times an hour. A human tolerates a twenty-second workspace start once a morning; a program pays that tax on every iteration.
2. Per-run disposal, because state that survives between runs is contamination. When you are comparing fifty candidate patches, run 12 must not be able to see anything run 11 left behind.
3. Fork and snapshot, because the expensive part is setup, not execution. Warm one machine to a known-good post-install state, snapshot it, then branch it fifty ways — memory shared copy-on-write, rootfs cloned by reflink — instead of running `npm ci` fifty times.
4. No human attachment surface at all. No IDE plugin, no editor session, no per-user identity, no seat. The consumer is an API key and the interface is a function call.

The third one is the interesting one, because it is the capability that has no workspace equivalent. Nobody needs to open fifty identical editors, so no workspace product has a reason to build branch-my-running-machine. An agent doing best-of-N wants exactly that:

from pandastack import Sandbox

# Warm ONE machine to a known-good state.
golden = Sandbox.create(template="base", ttl_seconds=1800)
golden.exec("git clone --depth 1 https://github.com/acme/webapp /work/app",
            timeout_seconds=120)
golden.exec("cd /work/app && npm ci && npm run build", timeout_seconds=900)
golden.snapshot()  # memory + disk, frozen post-install

# ...then branch it N ways. 400-750ms per same-host fork, copy-on-write.
passed = 0
for patch in candidate_patches:
    branch = golden.fork()
    try:
        branch.filesystem.write("/work/app/candidate.diff", patch)
        run = branch.exec(
            "cd /work/app && git apply candidate.diff && npm test",
            timeout_seconds=600,
        )
        passed += 1 if run.exit_code == 0 else 0
    finally:
        branch.destroy()

print(f"{passed}/{len(candidate_patches)} candidates pass")
golden.destroy()

When to pick which — and when to run both

Pick Coder when the consumer is a person and the infrastructure has to be yours: standardising a team's daily development environments, giving engineers self-service access to compute in your own accounts, meeting data-residency or air-gap requirements, or modelling org-specific infrastructure no hosted vendor will anticipate. If your platform team already speaks Terraform and already operates the compute, Coder meets you where you are rather than asking you to move.

Pick a sandbox API like PandaStack when the consumer is a program: an agent executing code it just wrote, a CI system fanning out across a matrix, a SaaS backend running customer-supplied scripts, or any workload that needs hundreds of short-lived hard-isolated environments per hour, created in code, with no seats and no cluster to babysit. If branch-and-explore is central to what you are building, fork is the tiebreaker.

And the answer a surprising number of teams land on is both, without it feeling like a compromise. Coder for the engineers' daily environments, inside your network, with the editor integration and Terraform templates your platform team already maintains. A sandbox API for the agents and the CI fan-out, where the consumer is code and nobody wants a per-seat license for a process. Different slots in the pipeline: humans author, machines execute. You only have to choose if you have mistaken one for the other — easy to do, since both are accurately described as "a machine in the cloud to run code on."

As always, distrust the table and build the one-hour spike. Measure a workspace start against your own template and cluster; measure sandbox create latency in your own region with your own workload. And check Coder's current documentation before taking my word for anything on their side — they ship fast, and a competitor's summary ages badly, whether on purpose or by accident.