AI and GenAI cloud costs.
AI spend combines the worst properties of every other cloud cost: GPU capacity is scarce and often requires commitments, inference scales with success like BigQuery, and the people spending it are the least patient with governance. Meanwhile the board wants a cost per AI outcome nobody has defined. These articles bring the FinOps discipline — unit economics, reservations, caching, chargeback — to the AI estate.
27 articles
Every article in this hub.
BlogAI capacity reservations and commitmentsAI spend governance for the CFOAI Unit Economics the Board UnderstandsBatch inference economicsCaching strategies that cut inference spendChargeback for AI Platform TeamsFine tuning versus prompting on costForecasting AI Spend Under GrowthGPU and TPU cost control on GCPGPU pricing across the four cloudsMeasuring cost per AI outcomeModel choice as a cost decisionNegotiating AI capacity with cloud providersObservability for AI CostOpen Weight Models on Your Own GPUsPrompt and context cost disciplineProvisioned throughput versus on demand inferenceRAG Architecture Cost BreakdownShadow AI spend and how to find itThe AI cloud cost guideThe AI Cost Review CadenceToken Economics for Enterprise Buyers of GenAITraining Versus Inference Cost ProfilesVector database costs comparedWhen AI Workloads Should Leave the CloudWhy GenAI broke the cloud budget
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