Comparison

AI Commander vs renting cloud compute

This one is not a product comparison — it is a hardware question. On one side, machines you already own: the GPU rig under the desk, the workstation in the office, the server in the colo. On the other, compute you rent by the second from providers like E2B, Modal and RunPod, who will give you a card in minutes and take it back when you stop paying. AI Commander is only useful on the first side. If you own no hardware, this page ends with a list of places to go instead, and that is the honest answer.

Verified 2026-08-26 Category claims below are drawn from three representative providers read directly — every figure is theirs, listed under Sources.

The short answer

Rent when you do not own the hardware, or when you need more of it than you own. No GPU at home, a card you cannot buy, fifty machines for one afternoon, or isolated throwaway sandboxes for code you do not trust — all of that is what E2B, Modal and RunPod exist for, and AI Commander cannot substitute for any of it. Use AI Commander when the card is already bought. Then the marginal cost of an hour of training is electricity rather than a per-second meter, the dataset never has to move because the job runs where the data already lives, and what you were missing was simply a way for an AI client to reach the machine — which is free here for up to 10 machines.

Neither side supplies the intelligence. Claude, Codex or ChatGPT does the thinking; rented compute sells you the hardware to run it on, and AI Commander connects you to the hardware you have.

What "renting compute" looks like

These are unrelated companies with different products, so the only fair way to describe the category is through what specific providers publish. We read three on 2026-08-26:

E2B — "sandboxes for AI agents": isolated cloud environments where an AI system runs code. Hobby is free with "One-time $100 of usage in credits", up to 1-hour sandbox sessions and a maximum of 20 concurrent sandboxes; Pro is "$150/mo" plus usage, with up to 24-hour sessions and up to 100 concurrent sandboxes, expandable to 1,100. Compute is metered per second — 2 vCPU at "$0.000028/s" is the default, memory at "$0.0000045/GiB/s".
Modal — serverless compute billed per second, with "scale to zero" so you "never pay for idle resources — just actual compute time". Published GPU rates include an Nvidia H100 SXM5 at "$0.001097/sec", an A100 80 GB at "$0.000694/sec" and an L4 at "$0.000222/sec". The Starter plan carries "$30 / month free credits".
RunPod — rented GPU instances by the hour, split between Community Cloud and Secure Cloud. Listed rates include an H100 PCIe at $1.99/hr (Community) and $2.89/hr (Secure), an A100 PCIe at $1.19 and $1.39, and an RTX 4090 at $0.34 and $0.74. Persistent storage is billed separately: volume disk at $0.10/GB/mo while running and $0.20/GB/mo idle, network storage from $0.05–$0.14/GB/mo.

The shape of the category is consistent even where the products differ: someone else owns the hardware, you are billed by time, capacity appears on demand and disappears when you stop, and your data has to get to their machine before the work can start. We publish no count of how many such providers exist, because none of the sources we checked states one.

Side by side

	AI Commander on your own hardware	Rented cloud compute
Who owns the machine	You do, already	The provider; you rent it by the second or hour
Cost of an hour of GPU time	Electricity. The software layer is free up to 10 saved machines, $49/month on Pro	Metered, e.g. RunPod H100 PCIe $1.99/hr Community or $2.89/hr Secure; Modal H100 SXM5 $0.001097/sec
If you own no GPU	✗ Nothing to connect to; this is not an option at all	✓ A card in minutes, no capital outlay
Burst and parallelism	✗ You have exactly the machines you have	✓ E2B: up to 20 concurrent sandboxes free, up to 100 on Pro, expandable to 1,100. Modal scales to zero and back
Where the data sits	Stays on the machine it is already on; only the command and its output cross the relay, and neither is logged or stored	Has to reach the provider's machine — uploaded or held in their storage, billed while it sits (RunPod network storage from $0.05/GB/mo)
Session lifetime	A detached job has no time cap and outlives the call, the client and the conversation	E2B publishes up to 1-hour sessions on Hobby and up to 24-hour on Pro; Modal and RunPod bill for as long as the workload runs
Idle cost	None beyond the plan; the machine is yours whether it is busy or not	Compute stops billing when it stops, but stored data does not (RunPod volume disk $0.20/GB/mo idle)
Isolation for untrusted code	✗ Commands run on the real machine as the agent's user — there is no sandbox	✓ E2B's product is precisely isolated sandboxes for AI agents
Choice of hardware	Whatever you bought	✓ A catalogue — H100, A100, L4, RTX 4090 and others
GPU reservation between your own jobs	✓ `gpu_index` holds one card exclusively; a second job is refused with `gpu_busy`	Not applicable — each rented instance is separate hardware
Desktop and non-GPU machines in the same fleet	✓ Desktop macOS and Windows, headless Linux, a NAS, a Pi — all addressed by name	✗ Rented instances only
Free path	Free plan, up to 10 saved machines	E2B Hobby: "One-time $100 of usage in credits". Modal Starter: "$30 / month free credits"
What you have to build	Install an agent per machine; the machine keeps its own OS, drivers and data	Package the work — image, SDK, entrypoint — and get the data there

Every figure in the right-hand column is published by the provider named beside it and was read on 2026-08-26; prices change, so check them before deciding. Treat the column as the shape of the category rather than a claim about any provider we did not read, and make no inference about a provider from a row sourced to a different one. We make no claims at all about any of these providers' security, reliability or roadmap.

Where AI Commander is different, concretely

The hardware is already yours. AI Commander adds a way to reach it, not a bill per second. A machine that sits idle costs nothing extra, and a job that runs for eleven hours costs the same as one that runs for one.
The data does not move. The job runs on the machine the dataset is already on. Only command text and stdout/stderr cross the relay, and those pass through memory and are discarded — never written to logs or a database. See the privacy policy.
Nothing inbound. The agent opens one outbound WebSocket to the relay, so a GPU box behind NAT in a cupboard or an office is reachable with no port forwarded, no exposed SSH and no VPN.
Jobs have a lifecycle, and it outlives everything. remote_job_start returns a job id immediately; the job survives the call returning, the client disconnecting and the conversation ending, and you read its logs or cancel it by id tomorrow. It also survives the agent itself restarting: on Linux each job is launched into its own transient systemd scope, outside the agent service's control group, so restarting or upgrading the agent leaves it running — that needs a systemd host with the agent running as root, and without both a restart still ends running jobs.
Short work and long work are different tools. A command is capped at 1 hour of wall clock, a hard kill, and 1 MiB of output, which truncates the reply. A detached job has no wall-clock limit and writes its output to a log file on the machine; that log records up to 256 MiB, after which the agent appends a truncation notice and stops recording — the job itself keeps running.
GPUs are described and reserved. Each machine reports its NVIDIA cards with model, VRAM and utilisation, so an agent can choose a box; gpu_index then holds one card exclusively and a second job wanting it is refused with gpu_busy rather than quietly sharing.
Machine notes, per account. A notes file kept on the machine — what CUDA version is installed, where the datasets live, which quirk bit you last time — so the second session starts informed.
Artifacts come back. On Pro, file transfer moves one file up to 100 MiB each way; the stored copy is unreadable 24 hours after creation and the download link expires in an hour. For multi-gigabyte checkpoints, have the job push to your own object storage instead.
Agent to agent. A job can be a coding agent running on the GPU box itself — see remote coding agents — started from your chat and collected later by job id.

Price

AI Commander's Free plan is AI access to your computers, up to 10 saved machines. Pro is $49 per month and covers every saved machine up to a technical 100-machine ceiling*, plus file transfer with remote_pull and remote_push. It is a flat software fee on top of hardware you already paid for — there is no compute meter, because we do not supply the compute. Full detail on our pricing page.

The rented side is metered, and comparing the two only makes sense once you own a card. Published figures read on 2026-08-26: RunPod listed an H100 PCIe at $1.99/hr on Community Cloud and $2.89/hr on Secure Cloud, an A100 PCIe at $1.19 and $1.39, and an RTX 4090 at $0.34 and $0.74, with persistent volume disk at $0.10/GB/mo running and $0.20/GB/mo idle. Modal listed an H100 SXM5 at $0.001097/sec, an A100 80 GB at $0.000694/sec and an L4 at $0.000222/sec, with "$30 / month free credits" on Starter. E2B listed Hobby free with "One-time $100 of usage in credits" and Pro at "$150/mo" plus per-second CPU and memory usage. If you have no hardware, those numbers are your real alternative, and for occasional work they are very hard to beat.

* 100 machines is a technical ceiling, not a policy limit. Need more? Get in touch — we'll sort it out.

When NOT to use AI Commander

Rent compute instead if:

You own no suitable hardware. AI Commander is structurally incapable of helping here — it connects to machines that exist; it does not provide any. Renting is the answer, not a compromise.
You need burst scale: fifty GPUs for an afternoon and none tomorrow. Your own rig is exactly one rig, however good it is.
You need a specific card you do not have — an H100 for a model that will not fit anywhere else, or simply newer silicon than you bought.
You are running code you do not trust and want it isolated. AI Commander runs commands on the real machine as the agent's user; there is no sandbox. E2B's entire product is sandboxes for AI agents.
You would rather someone else owned the hardware problem — capacity, drivers, a dead fan at 3 a.m., the machine being on at all.
The workload is spiky and rare. Per-second billing with scale to zero beats owning a card you use twice a month.

Good places to start: E2B · Modal · RunPod

When AI Commander fits

The card is already bought and mostly idle, and you want an AI client to be able to use it.
The dataset is already on the machine and you would rather not move it anywhere.
The box is behind NAT — a home lab, an office workstation, a colo server — with nothing exposed.
The work is long and must survive the chat, the network and your laptop sleeping.
Two jobs want the same card and you need one of them to wait, not share.
The fleet is mixed: a GPU rig, a NAS, a Pi and a desktop Mac, all addressed by name in one conversation.

FAQ

Can Claude use my own GPU instead of renting a cloud one?

Yes, that is what AI Commander is for. Install the agent on the machine with the card in it and your AI client can run commands and long jobs there. The machine reports its NVIDIA cards with model, VRAM and utilisation, so the agent can pick a box, and passing gpu_index reserves one card exclusively for a job — a second job asking for the same card is refused with gpu_busy rather than quietly sharing it. The data stays on the machine it is already on; only the command and its output cross the network, and neither is logged or stored.

Is there a cheaper alternative to renting a cloud GPU?

Only if you already own the card. If you do, the marginal cost of an hour of training is electricity, and AI Commander's job is to make that card reachable from an AI client — free for up to 10 saved machines. If you do not own one, nothing about AI Commander helps: it has no hardware to sell you, and renting from Modal, RunPod or E2B is genuinely the cheaper and faster answer. For reference, on 2026-08-26 RunPod listed an H100 PCIe at $1.99/hr on Community Cloud and $2.89/hr on Secure Cloud, and Modal listed an H100 SXM5 at $0.001097 per second.

Do I still need Modal, RunPod or E2B?

Yes, in several common cases. You need rented compute when you own no suitable hardware, when you need burst scale — fifty GPUs for an afternoon and none tomorrow — when you need a specific card you do not have, or when you want throwaway isolated sandboxes for code you do not trust. E2B publishes up to 20 concurrent sandboxes on its free Hobby tier and up to 100 on Pro, expandable to 1,100; Modal bills per second and scales to zero so idle costs nothing. AI Commander cannot do any of that: it runs commands on machines that already exist and belong to you.

Will a training run on my own machine survive the conversation ending?

Yes, if it is started as a detached job. The call returns a job id immediately and the job keeps running after the client disconnects and after the conversation ends; you come back in a new chat and read its logs, check its status or cancel it by id. A plain command is capped at 1 hour of wall clock, a hard kill, and 1 MiB of returned output, which truncates the reply, so long work belongs in a job. It also survives the agent itself restarting: on Linux each job is launched into its own transient systemd scope, outside the agent service's control group, so restarting or upgrading the agent leaves it running — that needs a systemd host with the agent running as root, and without both a restart still ends running jobs.

What happens to my data with each approach?

With rented compute the data has to get to the rented machine: you upload the dataset, or mount storage the provider hosts, and you pay for that storage while it sits there — RunPod, for example, published network storage at $0.07/GB/mo under 1 TB on 2026-08-26. With AI Commander the dataset never moves, because the job runs on the machine the data is already on; only the command text and its output cross the relay, and those pass through memory and are discarded rather than logged or stored. If you want an artifact back afterwards, Pro's file transfer moves one file up to 100 MiB, with the stored copy unreadable 24 hours after creation and the download link dead after an hour.

Sources & verification

Provider figures on this page were read from each provider's own pricing page on 2026-08-26:

"sandboxes for AI agents"; Hobby free with "One-time $100 of usage in credits", up to 1-hour sessions, up to 20 concurrent sandboxes; Pro "$150/mo" plus usage, up to 24-hour sessions, up to 100 concurrent sandboxes expandable to 1,100; per-second CPU ("$0.000028/s" at 2 vCPU) and memory ("$0.0000045/GiB/s")e2b.dev/pricing
Per-second GPU rates — H100 SXM5 "$0.001097/sec", A100 80 GB "$0.000694/sec", A100 40 GB "$0.000583/sec", L4 "$0.000222/sec"; "scale to zero"; Starter "$30 / month free credits"modal.com/pricing
Hourly GPU rates — H100 PCIe $1.99 Community / $2.89 Secure, A100 PCIe $1.19 / $1.39, RTX 4090 $0.34 / $0.74; volume disk $0.10/GB/mo running and $0.20/GB/mo idle; network storage $0.07/GB/mo under 1 TB, $0.05 over, $0.14 high-performancerunpod.io/pricing

We deliberately attribute every figure to the provider that published it, state no count of how many rented-compute providers exist because no source we checked publishes one, and make no claim about providers we did not read. Prices are the ones shown on 2026-08-26 and will change.

AI Commander facts trace to llms.txt, the docs, file transfer, the privacy policy and the plan limits in the relay's own entitlement code.

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