Pay-per-token pricing broke the AI budget.

Copilot, Azure OpenAI, Anthropic and Gemini have shifted from flat fees to consumption pricing that moves under you mid-year. Spend is scattered across metered platforms, team subscriptions and self-hosted infrastructure — 91% of organisations plan to increase AI investment again this year, while 60% report minimal or no value from it. Budgets set annually against pricing that changes monthly are structurally broken before the fiscal year starts.

Three Ways AI Spend Escapes Control

  • Metered usage with no attribution — nobody can say which person, team or project drove the consumption
  • Overlapping subscriptions — multiple teams independently paying for the same AI capability
  • No early warning — the overage lands on the invoice, not before it
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The invoice is not a control.

Finance discovers the overrun weeks after it happened, in an expense category that maps to nothing. Licence counts are negotiated blind: over-licensed flat subscriptions quietly waste budget while under-licensed seats rack up overage penalties — and no one can connect a dollar of AI spend to the business outcome it produced.

What Finance Cannot Currently See

  • Who is driving consumption
  • Which tiers are wrongly sized
  • Where teams pay twice for the same capability

Why Veranthios Is Different

Can we see who is actually spending?

Yes — usage is attributed by individual, team, project and tool across every metered AI platform: GitHub Copilot, Azure OpenAI, Anthropic, Google Gemini and self-hosted models, in a single dashboard. Not a cloud bill breakdown — attribution at the level a budget owner actually manages.

How do we stop the overrun before it happens?

Weekly and monthly cost forecasts with threshold alerts — finance is warned before spend exceeds approved budgets, not weeks later on the invoice. Redundancy detection flags where multiple teams are independently paying for overlapping AI capability, a common and costly pattern in organically grown AI adoption.

Why does cost sit in a governance platform?

Because cost, risk and compliance describe the same AI assets. Every system in the AI Asset Registry carries its risk score, its compliance status and its cost in one record — so the board sees not just what AI costs, but whether the spend is governed and what value it returns. Subscription right-sizing compares actual usage against contracted tiers: over-licensed flat subscriptions are flagged to downgrade, under-licensed positions are flagged before overage penalties accrue.

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Every dollar of AI spend, attributed to its owner.

Usage attributed by individual, team, project and tool across all metered AI platforms — GitHub Copilot, Azure OpenAI, Anthropic, Google Gemini and self-hosted models — in a single dashboard. The spend conversation changes from “why is the invoice so big?” to “this team, this project, this tool.”

  • Per-Person, Per-Project Attribution

    Consumption mapped to the individual, team and project that generated it — the level a budget owner manages.

  • One Dashboard, Every Vendor

    Metered platforms and self-hosted models in a single view — no vendor-by-vendor reconciliation.

Book An AI Exposure Assessment

Every dollar of AI spend, attributed to its owner.

Weekly and monthly forecasts; threshold alerts before budgets breach. Cost Forecasting · Budget Threshold Alerts.

  • Per-Person, Per-Project Attribution

    Consumption mapped to the individual, team and project that generated it — the level a budget owner manages.

  • One Dashboard, Every Vendor

    Metered platforms and self-hosted models in a single view — no vendor-by-vendor reconciliation.

Book An AI Exposure Assessment

Every dollar of AI spend, attributed to its owner.

Right-size subscriptions against actual usage; detect redundant spend across teams. Subscription Right-Sizing · Redundancy Detection.

  • Per-Person, Per-Project Attribution

    Consumption mapped to the individual, team and project that generated it — the level a budget owner manages.

  • One Dashboard, Every Vendor

    Metered platforms and self-hosted models in a single view — no vendor-by-vendor reconciliation.

Book An AI Exposure Assessment