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Know what AI costs. See what you get back.

Understand spending, share budgets and connect AI use to the work it supports.

Recent insights All insights

Buy tokens, rent GPUs, or own the rack?

Open-weight models are now good enough to make self-hosting a financial question rather than a capability one. I modelled four options, an office rack, colocation, rented GPUs and per-token APIs, to find where each one actually wins. The answer turns on utilisation, and it isn't the one the internet keeps giving.

Turn a $3m AI bill into $1.9m

Most teams overpay for AI twice without noticing: they run everything through the default flagship model, and they buy the tokens on a metered plan they can't see into. An intelligent proxy fixes both. Here is the maths, a calculator you can drag, and where Flowstate fits.

Start with the work All use cases

Understand AI spending and value

See where the budget goes and what the business gets back.

Share one AI budget across your tools

Fund people, teams and projects across connected services.

Give your team more capacity

Reduce the preparation, checking and follow-up around recurring work.

Connect AI use to delivered work

Follow AI activity into projects, code and business results.
Quick links
Flowstate / People + AI Talk through your priorities
Use cases

Make AI spending someone’s responsibility.

Set budgets, assign owners and give requests a clear approval process. Keep the controls that fit your tools and the way your business works.

Talk through your AI spending

Give every request an owner.

Send requests to the person who can approve the cost. Keep the reason and decision together so finance can follow up without searching through messages.

Explore access and spending controls ↗
Access request
RequestDetail
Tool Claude
Team Engineering
Reason Review a service migration
Approver Engineering budget owner
Awaiting budget approval

Match controls to the connection.

Choose how alerts, approvals and limits should work for each supported service. Agree what happens when a team reaches its allowance.

Explore shared AI budgets ↗
Team spending policy
TriggerAction
80% of allowance Notify budget owner
More budget needed Request approval
Limit reached Apply configured control
Engineering · Monthly allowance £2,000

Keep a record of decisions.

See what changed, why it changed and which role approved it. Use the record when you review budgets or investigate a charge.

Explore access and spending controls ↗
Budget history
ChangeReason
Allowance increased by £200 Approved research project
Temporary access ended Project completed
Unused seat suspended Inactivity policy
Decisions linked to their requests
Your systems, connected

Use the tools behind this work.

We agree which records, access and controls your workflow needs, then connect the relevant systems.

Explore all integrations ↗
Claude Platform Available OpenAI Platform Available Okta Discuss setup

Measure what changes.

Track budget exceptions, approval turnaround and the time it takes to reconcile spending decisions.

More control over your AI costs.

AI spending & value

Know where your AI budget goes. And what it’s doing.

 One budget across your AI tools

Give your people a budget. Let them choose the tools.

 AI adoption

See who uses AI. Find the tools earning their place.

Start with the costs you need to control.

Show us your tools and how you manage spending today.

Talk to Flowstate
Practical questions

What to know before you start.

Who should approve AI access and budget requests?

Give each request a clear owner. A team manager may approve ordinary requests while an administrator handles sensitive access, service accounts or exceptions, according to the rules you agree.

Can the same controls apply to every tool?

Not every provider exposes the same APIs or enforcement options. Define the available controls per connection and keep any gaps visible.