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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.

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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.

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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.
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Understand your AI bill

Connect providers and follow spending into the work.

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Use the API, MCP and custom integrations.

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01 Get started 02 AI spending & adoption 03 People & planning 04 Delivery & effort 05 Finance 06 Integrations 07 Administration 08 Security 09 API & MCP 10 Release notes

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