Separate AI requests from retries and evaluation work
Review AWS and Azure telemetry to separate application requests, model calls, retries, and evaluation activity before allocating AI spend.
Steps3 min read
Managing AI costs
Track what you're spending on models, tokens, and GPUs.
8 guides
Measure AI cost per successful business outcome
Compare scoped AI spending with accepted business outcomes using a read-only worksheet that keeps retries, failed attempts and attribution gaps visible.
Steps4 min read
Review quiet SageMaker capacity before cutting it
List SageMaker endpoints and notebook instances, compare endpoint activity, and ask owners which resources can become candidates for a cost reduction.
BASH3 min read
Choose models by cost per usable answer, not token price
Compare model candidates on shared tasks, quality limits and response times. Use a read-only worksheet to estimate cost per accepted result from evaluation records.
Steps3 min read
Budget AI search and tool usage separately
Separate model tokens, search grounding, and tool-resource measurements so an AI workflow budget does not hide charges behind prompt counts or monitoring data.
Steps3 min read
Check GPU commitment cost against useful work
Separate reservation use from GPU activity, then compare period cost with accepted workload output before deciding whether to renew capacity.
Steps4 min read
Compare AI prices without mixing token types or capacity
Use a read-only worksheet to compare Azure OpenAI, Amazon Bedrock, and Vertex AI costs, separating token categories, cache charges, and provisioned capacity.
Steps4 min read
Choose billed or effective cost for GPU reporting
Separate GPU invoice totals from recognized consumption costs. Review FOCUS measures, period boundaries, currencies, commitments and workload mapping before reporting trends.
Steps3 min read