Why Klique
You enabled AI. Now measure it, and scale with confidence.
Leadership said yes, budget opened up, and adoption took off — that was the right call. The next step isn't pulling back, it's gaining a clear, real-time view of spend by project, group, and use case, so you can put more behind what's working and scale AI on purpose instead of on faith.
The companies pulling ahead saw this coming.
Usage-based, per-token pricing means cost scales with exactly the behavior you wanted: more usage.
Without a meter, that's invisible until the invoice lands. Industry research puts it plainly: roughly half of enterprises running AI agents have already pulled back deployments because operating costs outran the benefit — and roughly a quarter can even see what they're spending in real time. This isn't an argument against AI spend. It's the argument for knowing what you're spending before finance finds out from the invoice.
Own your AI. Not just the bill.
Visibility is step one. The second step is running models on your terms — your policies, your infrastructure, your cost curve.
Once you can see your spend, the next question is who actually owns your AI. Today most enterprises are tenants: the models, the routing, and the pricing all live on someone else’s side of the API. The second step is ownership — not training frontier models, but controlling and deploying them under your own roof: your policies decide which model serves each request, your infrastructure runs the workloads that matter, and your production costs drop as usage scales instead of climbing with it.
Phase 00 · ENABLE
Enable
Opening access so builders, business teams, and agents can move fast was the right call — the problem was never whether to enable AI, it's that most platforms give you no way to see what it actually costs once a thousand people take you up on it.
Phase 01 · MEASURE
Measure
Token consumption, GPU hours, storage, and gateway spend are tracked at every level: by user, group, project, model, and agent. Cost attribution stops being a spreadsheet exercise and becomes a live report finance can pull anytime, broken down the way your business is already organized.
Phase 02 · CONTROL
Control
Visibility is step one. Allocation is step two. Set a budget per business unit and let the platform enforce it: quotas that hold before they're exceeded, and routing policies that keep spend inside the lines you set, automatically, instead of chasing teams down after the fact.
Phase 03 · OWN
Own
Once you can see your spend, the next question is how you can optimize your AI usage. Today, most enterprises are tenants: the models, the routing, and the pricing all live on someone else’s side of the API. The second step is ownership, not training frontier models, but controlling which model is used for what purpose, while no manual decisions need to be made: your policies decide which model serves each request, your infrastructure runs the agentic workloads that matter (and, in some cases, the models themselves) to reduce cost and retain sovereignty, and your production costs drop as usage scales instead of climbing with it.
The only thing certain about AI is that it will keep changing. This is the layer that absorbs the change so your stack does not have to.
See it running against your own spend.
A working environment, your models, your budgets — in about 30 minutes.
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