machine0 Review (2026): Pricing, Features & Honest Verdict

TLDR

machine0 is a virtual machine platform built for AI agents and heavy compute. It gives you persistent VMs, up to 60 vCPUs and optional H100 or H200 GPUs, that you drive entirely from the CLI or MCP. Snapshot and suspend let you pause billing between runs. Best for: developers and agents that need a real, durable computer. Price: usage-based from $0.013/hr, no free plan. Rating: 7.1/10.

What is machine0?

machine0 is cloud compute designed around AI agents rather than around humans clicking a dashboard. You give an agent a machine, and that machine stays alive until it is told to stop. It is the opposite of the ephemeral, one-request-and-gone sandbox model that most agent platforms push. Under the hood you get standard NixOS or Ubuntu images, static IPs, and HTTPS endpoints across five regions, so the VM behaves like a normal server that happens to be controlled by code.

It is built by Fundamental Software, a Y Combinator company. The bet is that the next wave of agents needs somewhere durable to work: a place to clone a repo, install a toolchain, run a long job, and come back to it later without rebuilding from scratch every time. We tested it against that promise, because plenty of platforms claim to be agent-ready and then turn out to be a thin wrapper over stateless containers. machine0 is genuinely built for persistence, and that changes what an agent can do.

What Are machine0’s Key Features?

Persistent, agent-native VMs

The core product is a VM that stays running until you stop it. It can scale up to 60 vCPUs and 240 GB of RAM, which is a real workstation, not a toy container. Because the machine persists, an agent can install dependencies once, keep a database warm, and pick up a long-running task across multiple sessions. That state survival is the whole reason to choose machine0 over a stateless sandbox that wipes itself after each request.

Serious GPU muscle

machine0 offers a wide GPU lineup: H100, H200, L40S, MI300X, and RTX 4000 and 6000 Ada cards. You can go from a single RTX 4000 Ada up to an 8x H200 box. That range means the same platform handles light inference on a cheap card and full training runs on top-end hardware, without moving to a separate provider. For teams doing on-and-off model work, keeping compute in one place is worth a lot.

Snapshot, suspend, and resume

You can snapshot a VM, suspend it to freeze its exact state, and resume later. While a VM is suspended, you stop paying for compute and only pay storage at $0.078/GB per month. This is the feature that makes the persistent model affordable. An agent can do an hour of work, suspend the machine, and the meter stops until it wakes back up. It turns a persistent VM into something you can leave sitting around cheaply.

CLI and MCP built for agents

Everything is CLI-driven with clean JSON output, and there is a native MCP server so agents can manage machines directly. It integrates with Claude Code, Codex, GitHub, and custom MCP servers through profiles. In practice that means a coding agent can provision its own VM, run code, and tear it down without a human in the loop. The JSON output matters here, because it is what lets an agent parse results reliably instead of scraping human-readable text.

Multi-region reach and real networking

VMs run across five regions: US East, US West, UK, EU, and Asia. Each machine gets a static IP and an HTTPS endpoint, so a service running inside a VM is reachable like any normal server. That matters more than it sounds. An agent can stand up a web service, expose it, and hand you a working URL, all without you touching a load balancer or DNS. The NixOS and Ubuntu image choice also means the environment is reproducible, which is exactly what you want when an agent is rebuilding a machine from a snapshot.

How Much Does machine0 Cost?

machine0 starts at $0.013/hour for a small CPU VM, billed per minute with no setup fee. A maxed-out 60 vCPU machine runs up to $3.714/hour. GPU VMs start at $0.836/hour for an RTX 4000 Ada and climb to $39.336/hour for an 8x H200 system. The minimum top-up to get going is $5, so the barrier to trying it is low even though there is no standing free tier.

Against the competition, the pricing is fair but not the cheapest on paper. E2B charges roughly $0.0504 per vCPU-hour and adds a $150/month Pro plan for longer sessions. Modal only bills for active compute and includes $30/month in free credits, which undercuts machine0 for bursty, idle-heavy workloads. machine0’s advantage is not the raw rate. It is that suspend-to-pause lets a persistent machine sit at near-zero cost between runs, so you get durability without paying for idle time.

The honest catch is GPUs. Top-end configurations get expensive quickly, and a forgotten running H200 box is a painful bill. The suspend feature is your friend here, but you have to remember to use it. Set up automation to suspend idle machines, and machine0 is cost-effective. Leave big GPUs running by accident, and it is not.

Who is machine0 Best For?

Use machine0 if your agents need a real computer that persists. If you are building a coding agent that clones repos, installs toolchains, and runs multi-step jobs, the persistent VM model saves you from rebuilding the environment on every call. Use it if you want CPU and GPU compute from one provider, controlled entirely by code. And use it if suspend-to-pause fits your usage, meaning you work in bursts and want to stop the meter in between.

Skip machine0 if you only need stateless, per-request execution of untrusted code. For that, E2B’s microVM sandboxes or Modal’s serverless model are cheaper and more mature. Skip it if you want a fully managed platform-as-a-service with buildpacks and dashboards, because machine0 is deliberately low-level. And skip it if a free tier is a hard requirement, since Modal and E2B both hand out credits and machine0 does not.

Best machine0 Alternatives

Modal

Modal is serverless compute that drops charges to zero when idle and includes $30/month in free credits. It covers inference, training, and batch jobs inside one platform, using gVisor isolation. Modal is the better pick for bursty, event-driven workloads where you do not want a machine sitting around. machine0 wins when you specifically need a persistent, stateful VM that an agent keeps returning to rather than a function that spins up and vanishes.

E2B

E2B is purpose-built for running untrusted agent code in Firecracker microVMs, billed at about $0.0504 per vCPU-hour with a $100 starter credit and a $150/month Pro tier for 24-hour sessions. It is the safer choice when you are executing code you do not trust. machine0 is less about disposable isolation and more about giving an agent a durable home, so the two solve related but different problems.

Fly.io

Fly.io runs persistent machines with per-second billing across a global network, and it is battle-tested for hosting real apps. It does not, however, ship the agent-first CLI and MCP tooling that machine0 leads with, so wiring it up to a coding agent takes more glue. If you want a mature, general-purpose host, Fly.io is the safer bet. If you want compute that agents drive natively, machine0 is the tighter fit.

Final Verdict: Is machine0 Worth It?

machine0 is a focused answer to a specific 2026 question: where should an AI agent actually run? Its persistent VMs, broad GPU options, and suspend-to-pause billing add up to a genuinely useful platform for agents that need to keep state and come back to work. The CLI and MCP integration are clean, and the per-minute billing with a $5 entry point makes it easy to start without a sales call.

We rate it 7.1 because the execution is strong but the platform is young and the value depends on how you use it. There is no free tier to soften the first bill, GPU costs escalate fast, and the ecosystem is thinner than Modal’s or Fly.io’s. If you are building agents that need a durable computer and you will actually use suspend to control costs, machine0 is worth adopting. If your workloads are stateless and bursty, a serverless option will serve you better and cheaper. Recommended for the persistent-agent use case, with eyes open on the GPU meter.

One practical tip if you adopt it: wire the suspend command into your agent’s shutdown routine from day one. The economics of machine0 only work when idle machines actually go to sleep, and the platform does not force that on you. Treat suspend as a habit rather than an afterthought, and the persistent model pays for itself.