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The best Runloop alternatives in 2026

Ajay Kumar··9 min read

Runloop aimed squarely at coding agents: a Devbox is a persistent development environment an agent can work in over a long session, with a repo checked out, tooling installed, and a scenario harness for benchmarking agent performance. That's a more opinionated product than a generic sandbox API, and it's the reason people either love it or find it doesn't match their shape.

If you're comparing, here's what actually differs between providers in this category. I build PandaStack, one of the options, and I'll flag where it's the wrong pick.

The five things that actually differ

  1. Time to a usable sandbox. Everyone quotes a number; check whether it includes your dependencies being present. A 200ms VM that then spends 40 seconds on `npm install` is a 40-second sandbox.
  2. Session model. Ephemeral per-task, or a persistent environment that survives across an agent's whole session? Coding agents want the second; per-request code execution wants the first.
  3. Snapshot and fork semantics. Can you freeze a prepared state and branch from it? This is what makes best-of-N exploration and retry-from-checkpoint cheap, and the implementations differ enormously in what they actually capture.
  4. Idle economics. An agent thinking for two minutes is an idle VM. Whether you pay full rate for that, a reduced rate, or nothing at all changes your bill by an order of magnitude at scale.
  5. Isolation boundary. A container with hardened runtime, a gVisor-style syscall filter, or a real hypervisor. For running code an LLM just wrote on behalf of a customer, this is a security decision, not a performance one.

The alternatives

E2B

The most widely adopted sandbox API in the category, with the largest community and the most framework integrations already written. Firecracker underneath, a clean SDK, custom templates. If you want the option everyone has heard of and lots of examples to copy, start here. The usual reasons people look further are cost at sustained volume and wanting more control over the underlying environment.

Daytona

Development-environment lineage, which shows in the product: strong on reproducible workspaces and repo-centric workflows, with an open-source core and self-hosting available. Closest in spirit to Runloop's Devbox model. Good pick if the agent's job is 'work in this repository for a while' rather than 'run this snippet'.

Modal

Not really a sandbox product — a Python-first compute platform with excellent container start times and first-class GPU support that people also use as a sandbox. If your agent workload is compute-heavy, needs GPUs, or is fundamentally a Python data pipeline with an agent attached, Modal is a better fit than anything else on this list. If you need arbitrary languages and interactive sessions, less so.

Morph Cloud

Built around fast snapshot and branching, which is the interesting primitive for agent work — freeze a prepared state, fork it many times, throw away the branches that fail. Worth evaluating specifically if tree-of-thought or best-of-N execution is central to your design rather than an experiment.

Building it yourself on Firecracker

Entirely feasible and the reason several of these companies exist. What you're signing up for is not the VM — Firecracker is well documented — it's the networking pool, snapshot storage and replication, scheduling across hosts, teardown correctness, and the long tail of leaked resources. Budget quarters, not weeks, and only do it if sandbox behaviour is a competitive advantage for you rather than a dependency.

PandaStack (mine)

Firecracker microVMs where every create is a snapshot restore rather than a boot — p50 around 179ms in production, because the sandbox is rehydrated from a baked template snapshot instead of started from scratch. Sandboxes can be ephemeral or persistent, snapshot and fork are first-class, and billing is per-second on active CPU and resident memory rather than on committed capacity, which is the specific thing that makes an agent's thinking time cheap. Same platform also runs app hosting and managed Postgres, so an agent that needs a database to query or an app to deploy doesn't need a second vendor.

from pandastack import Sandbox

# Snapshot-restore create, not a cold boot
sbx = Sandbox.create(template="code-interpreter", ttl_seconds=900)
try:
    result = sbx.exec("python3 -c 'print(sum(range(100)))'")
    print(result.stdout)        # 4950
    print(result.exit_code)     # 0

    # Freeze prepared state, then branch from it
    snapshot_id = sbx.snapshot()
    branches = sbx.fork_tree(count=4)
finally:
    sbx.kill()

Where it's the wrong answer, plainly: there's no built-in scenario or evaluation harness of the kind Runloop ships, so if that benchmarking layer is why you're there, you'd be rebuilding it. There's no GPU offering — for model training or GPU inference, look at Modal or a GPU cloud. And fork currently captures disk state rather than live memory, so a fork resumes from the filesystem rather than from the exact instruction the parent was on; if you need memory-level branching, verify that behaviour against your use case before committing.

Whichever you pick, run one benchmark before you commit: create a sandbox, install your real dependency set, run your actual task, and destroy it — a hundred times, concurrently. The number that matters is p99 of that whole loop, not the p50 of a create call on an empty template.

How to choose

  • Agent works in a repo for a long session, and you want a benchmarking harness with it: Runloop is already the specialist. Daytona is the closest alternative.
  • You want the ecosystem and the most examples: E2B.
  • GPUs or heavy Python compute: Modal.
  • Branching and snapshot exploration is central: Morph or PandaStack.
  • Cost at sustained volume, with a lot of idle thinking time: compare per-second active-usage billing against per-hour committed capacity — this is where the numbers diverge most.
  • You also need a database or app hosting for the same agents: fewer vendors is a real advantage, and worth weighing against best-of-breed.

Short version

There's no single best provider here, and anyone claiming otherwise is selling. Match the session model to your agent's shape first — ephemeral execution versus long-lived workspace — then compare on idle economics and snapshot semantics, because those two decide your bill and your architecture respectively. Everything else is close enough that the SDK you find pleasant to use is a legitimate tiebreaker.

Frequently asked questions

What should I compare when choosing an AI agent sandbox provider?

Five things, in roughly this order. Time to a genuinely usable sandbox — including your dependencies, not just the VM. The session model: ephemeral per-task versus a persistent environment that survives an agent's whole session. Snapshot and fork semantics, since that is what makes retry-from-checkpoint and best-of-N exploration affordable, and implementations differ in what state they actually capture. Idle economics, because an agent spends much of its wall-clock time thinking rather than executing, and whether you pay full rate for that dominates the bill at scale. And the isolation boundary — container, syscall filter, or hypervisor — which is a security decision when the code was written by a model on a customer's behalf.

How much does agent idle time cost in a sandbox?

Frequently more than the execution itself. A typical agent loop runs a command for a second or two, then waits several seconds or minutes for a model response, then runs the next command. If your provider bills for committed capacity by the hour, you pay full price for all of that waiting, and the sandbox is doing nothing for the large majority of its lifetime. Providers that bill per second on active CPU and resident memory charge close to nothing during the wait. Before committing, model your own agent's real duty cycle — measure the ratio of execution seconds to wall-clock seconds in a real session — and price both models against it.

Is Runloop or E2B better for a coding agent?

It depends on how long the agent works in one place. Runloop's Devbox model is built for long-lived development environments where an agent checks out a repository and works in it across many steps, and its scenario harness is aimed at benchmarking agent performance on tasks — genuinely useful if you are evaluating agents rather than just running them. E2B is a more general sandbox API with a larger community and more existing framework integrations, which makes it faster to adopt and easier to find examples for. For short bursts of code execution, E2B's shape fits better; for an agent that lives in a repository for an hour, the workspace model does.

Should I build my own agent sandbox on Firecracker?

Only if sandbox behaviour is a competitive advantage for your product rather than a dependency. Firecracker itself is well documented and getting a VM to boot is not the hard part. The hard parts are everything around it: a pre-allocated network pool so creates are not dominated by namespace setup, snapshot storage and replication across hosts, a scheduler that places work sensibly, teardown that is correct under crashes so you do not leak namespaces and disk, and the operational work of keeping guest kernels and templates current. That is quarters of engineering, and it never finishes. Buy it unless the specific way you run sandboxes is the thing customers pay you for.

What does snapshot and fork actually give an AI agent?

Cheap exploration and cheap recovery. With a snapshot you can prepare an environment once — repository cloned, dependencies installed, database seeded — and start every subsequent task from that state instead of rebuilding it, which usually saves far more time than any difference in raw boot speed. With fork you can branch from a running state, try several approaches in parallel, keep whichever succeeds, and discard the rest, which is the mechanism behind best-of-N code fixing. The important detail when comparing providers is what the fork actually captures: filesystem state alone, or filesystem plus live memory. The difference determines whether a branch resumes mid-execution or restarts a process from disk.

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Written by Ajay Kumar, Founder, PandaStack.