Explore Flowstate

The Flowstate platform
Flowstate Helm

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

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

See AI costs by provider, person, team and project. Connect spending to business activity so you can understand what the money supports and where to invest next.

Talk through your AI spending

See costs by team.

Compare teams without collecting a spreadsheet from each one. Check the provider, person and billing period behind a cost.

See spending across your business ↗
AI spending · September
£8,400

Across connected providers

Engineering £4,800
Marketing £2,400
Operations £1,200
Updated today · View source records

Follow the spending into the work.

Connect provider usage to the projects, tasks and workflows it supports. See how people interact with AI and leave costs without enough evidence for review.

Connect AI use to work and projects ↗
Cost allocation
DestinationAmountStatus
Customer portal £3,200 Allocated
Internal reporting £1,600 Allocated
Missing project £240 Needs review
Review costs before you close the month

Explain a change in spending.

Compare periods and follow a change back to the work behind it. Use the forecast to decide whether to adjust a budget.

Explore shared AI budgets ↗
Engineering · Monthly forecast
£6,000

Forecast against a £6,500 budget

This monthAmount
Spent so far £4,800
Forecast remaining £1,200
Budget remaining at month end £500
Forecasts are estimates based on recorded usage
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 Microsoft Foundry / Azure OpenAI Discuss setup Amazon Bedrock Discuss setup Google Vertex AI Discuss setup

Measure what changes.

Track costs by business activity, budget variance and the return from defined workflows. Include review time, quality and running costs when you assess value.

More control over your AI costs.

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.

 Approvals & controls

Make AI spending someone’s responsibility.

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.

Why do AI invoices need more context?

An invoice shows what a provider charged. It may not explain which team, project or task benefited. Linking usage to work gives the cost a useful owner and purpose.

How do you handle costs that cannot be attributed?

Keep them visible as unallocated or estimated, with the source and basis recorded. Do not distribute them across teams in a way that makes an estimate look observed.