Micro-VMs for AI workloads

Zero-downtime snapshots. Native branching. 50-90% compute savings. Built for coding agents, browser automation, and RL training.

~70ms

Boot time

0ms

Snapshot pause

50-90%

Cost savings

Launch a VM Read Docs

Developer Experience

A few lines of Python

import asyncio
from fastvm import FastVM

async def main():
    async with FastVM() as client:
        # Launch a micro-VM (~70ms boot)
        vm = await client.launch(machine="c1m2")

        result = await client.run(vm, "python3 --version")
        print(result.stdout)  # => Python 3.13.5

asyncio.run(main())

PythonReady

Zero-downtime snapshots. Native branching. 50-90% compute savings. Built for coding agents, browser automation, and RL training.

~70ms

Boot time

0ms

Snapshot pause

50-90%

Cost savings

Launch a VM Read Docs

import asyncio
from fastvm import FastVM

async def main():
    async with FastVM() as client:
        # Launch a micro-VM (~70ms boot)
        vm = await client.launch(machine="c1m2")

        result = await client.run(vm, "python3 --version")
        print(result.stdout)  # => Python 3.13.5

asyncio.run(main())

PythonReady

Performance

How we compare

Purpose-built infrastructure for AI workloads. Not retrofitted containers or legacy VM architectures.

Metric	Fast VM	E2B	Modal	Daytona
Boot time	~70ms	300ms	1s	~300ms
Memory snapshots	Zero downtime	Interrupts existing processes	VM shutdown requiredFeature in Alpha	Not possibleFilesystem only
Storage cost scaling	Sub-linear	Linear	Linear	Linear
Compute cost	50-90% savings	Standard	Standard	Standard
Isolation	StrongDedicated kernel per VM	StrongDedicated kernel per VM	ModerateShared kernel (gVisor)	WeakDocker (Kata optional)

Fast VM

Others

Boot time

~70ms

300ms

Memory snapshots

Zero downtime

Interrupts existing processes

Storage cost scaling

Sub-linear

Linear

Compute cost

50-90% savings

Standard

Isolation

StrongDedicated kernel per VM

Use Cases

Built for AI workloads. Ready for anything.

Checkpoint before every decision. Fork into 100 VMs. Generate millions of diverse trajectories faster and cheaper.

Frontier RL training needs to fork environments at decision points while keeping TCP, streaming, and database connections alive across branches. FastVM does this natively.

Post-training

Checkpoint before every decision. Fork into 100 VMs. Generate millions of diverse trajectories faster and cheaper.

Frontier RL training needs to fork environments at decision points while keeping TCP, streaming, and database connections alive across branches. FastVM does this natively.

Ready to build?

Launch a VM