Free whitepaper

FinOps for AI workloads

Token bills are only the visible tip. How to baseline, allocate, and govern the full cost of AI features on AWS — before finance asks.

  • See the full AI cost iceberg: models, vector stores, orchestration, observability
  • Layer Bedrock pricing modes deliberately instead of paying on-demand for everything
  • Report cost per business outcome, not cost per token
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WHITEPAPERFinOps for AI workloads7 pages · English
GDPR compliantEU hostingAWS-first expertise
29%
of cloud spend is wasted — AI complexity is a named driver
98%
of FinOps practitioners now manage AI spend
40–60%
of AI feature cost sits below the token line

Sources: Flexera, 2026 State of the Cloud; FinOps Foundation, State of FinOps 2026 & FinOps for AI working group

01

The cost iceberg

Why token dashboards miss half the bill, and which billing streams to pull into one baseline.

02

Pricing modes on AWS

On-demand, provisioned throughput, batch, prompt caching — when each mode pays off.

03

Unit economics

FOCUS-normalised billing and semantic metrics as the denominator for cost per outcome.

What you walk away with

  1. 1A baseline checklist for end-to-end AI spend
  2. 2A decision table for Bedrock pricing modes
  3. 3Two or three unit-economics metrics to start reporting
  4. 4A 0–90 day sequence you can run without new tooling
  5. 5The questions to ask before the next AI budget round

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