◈ AI agents

Agents that work all day.
A bill that does not
work against you.

An agent is a loop: it calls a model, calls a tool, reads the result and goes round again. Every turn is a charge somewhere. On Compute the model, the machines and the state an agent keeps sit on one bill, priced below what the leading cloud charges for the same thing, and you pay for the turns that happened rather than the capacity you guessed at.

Used by startups shipping their first agent and by enterprises running fleets of them.

30%
Under the leading cloud’s model service on tokens
Per-token
Inference billing
Per-second
Machine billing
One
Bill for model, tools and state

What this looks like in practice

◆Customer support and service agents◆Research and browsing agents◆Coding and review agents working against CodeGraph◆Back-office agents for documents, invoices and forms◆Tool servers and function endpoints◆Agent memory, state and conversation history◆Evaluation runs and batch replays◆Customer support and service agents◆Research and browsing agents◆Coding and review agents working against CodeGraph◆Back-office agents for documents, invoices and forms◆Tool servers and function endpoints◆Agent memory, state and conversation history◆Evaluation runs and batch replays
What holds true here

Four things you stop arguing about

◆

Tokens priced against the leading cloud

Input and output tokens on the general model cost 30% under the leading cloud’s model service for the same model. The comparison is published, so nobody has to take it on trust.

◇

Machines that stop when the agent does

Tool servers and GPU jobs are billed by the second. An agent that is idle overnight costs nothing overnight.

◈

State beside the work

Memory and history live in CosmoBase, artefacts in Cloud Services, code in CodeGraph. One account, one set of permissions, one invoice.

○

No commitment to unwind

No reserved throughput and no minimum term. Agent workloads change shape every month, and the bill is allowed to change with them.

The shift

From a pilot you cannot afford to scale to a fleet you can

Most agent projects stall at the same point: the demo works, and then someone multiplies the token bill by the number of customers. The answer is rarely a cleverer prompt. It is paying less for the same model and only for what actually ran.

The usual arrangement
  • ✕Token spend that grows faster than usage does
  • ✕Reserved capacity bought for a peak that rarely arrives
  • ✕Model, compute and storage on three separate bills
  • ✕Prices discovered on the invoice, not before it
  • ✕A pilot that never earns the right to scale
What replaces it
  • ✓The same model for 30% under the leading cloud’s price
  • ✓Machines billed by the second, released when idle
  • ✓Everything an agent touches on one bill
  • ✓Prices published beside the leading cloud’s own
  • ✓A unit cost that makes the business case
What you can count on

Stated plainly, and qualified

30%
Under the leading cloud’s model service
General-model input and output tokens
Per-second
Machine billing
Tool servers and GPU work
Zero
Commitments
No reservation, no minimum term
One
Bill
Model, machines, databases and code
Closer in

AI Agents, specifically

The unit cost that decides everything

Whether an agent becomes a product depends on one figure: what a completed task costs. On Compute that figure is lower because the tokens cost less than on the leading cloud and the machines only bill while they run. It is also easier to know, because every part of the task lands on the same bill with a published price beside the leading cloud’s.

Tools that scale with the loop

Function endpoints and tool servers on Compute Instances, sized for the work and billed by the second.

Memory that persists

Conversation history and agent state in CosmoBase, with two databases included before you pay anything.

Agents that write code

Coding agents open changes in CodeGraph, run CI and leave a reviewable record of what they did.

Evaluation without a standing cluster

Replay a large set of conversations on GPUs for an hour, then stop paying for them.

Against the leading cloud

AI Agents on Compute, against the leading cloud

General & code model — input
$0.105 / million tokens
Leading cloud $0.15 / million tokens · The same open-weight model, input tokens
30% less
General & code model — output
$0.455 / million tokens
Leading cloud $0.65 / million tokens · The same open-weight model, output tokens
30% less
H100 GPU hour
$4.82/hr
Leading cloud $6.88/hr · Same GPU (H100 80 GB)
30% less
4 vCPU / 8 GB server
$0.0941/hr
Leading cloud $0.1344/hr · Its cheapest machine with 4 vCPU and 16 GiB
30% less

Compared with the leading cloud's own published on-demand prices, US East. Every comparison.

Questions we are actually asked

Answers without the hedging

Which models can our agents call?+

Open-weight models through one API, billed per input and output token. The current list and every price are on the pricing page, with the leading cloud’s price for the same model beside it where one exists.

Do we need to commit to a volume to get the price?+

No. The price on the page is the price at any volume, with no reservation, no minimum and no term. A startup running its first agent and an enterprise running hundreds pay the same rate.

Can we move an agent off the leading cloud without rewriting it?+

Usually the change is an endpoint and a key. Move the model calls first, compare a week of cost and behaviour against what you had, and move the tools and state when you are satisfied.

What do we do about data our agents handle?+

Where data lives and who may reach it are configured properties of your account. If your sector requires a specific attestation, ask us directly and we will tell you plainly what we hold today.

Other industries

Where else this goes

Customer Acquisition

Host the landing pages, attribution pipelines and onboarding flows that bring customers in, and spend less on the infrastructure than you would on the leading cloud.

Marketing

Run campaign sites, personalisation, content pipelines and marketing data on one platform that costs less than the leading cloud for the same machines and models.

Lead Generation

Run the forms, enrichment jobs, scoring models and routing that turn interest into leads, for less than the same infrastructure costs on the leading cloud.

Sales

Run CRMs, sales data, call analysis and AI assistants on one platform, priced under the leading cloud for the same machines, databases and models.

Creator Economy

Host creator platforms, media processing, storefronts and AI tools on infrastructure that bills by the second and costs less than the leading cloud for the same machines.

SaaS

Run your product, its databases, CI and AI features on one platform that costs less than the leading cloud for the same machines, databases and models.

Start where it matters most

Begin with one workload. Keep what you have running. Decide whether to continue once you have seen it carry something real.

No migration required to begin. Nothing to tear down if you stop.