Measure AI cost per successful business outcome
Connect model, retry, evaluation, and supporting-service costs to a quality-qualified business result instead of reporting cost per token alone.
Steps7 min read
Managing AI costs
Track what you're spending on models, tokens, and GPUs.
8 guides
Find idle Azure Machine Learning inference capacity
Review low-demand managed online deployments and compute-cluster floors without treating low utilization as permission to remove capacity.
Steps7 min read
Compare Azure OpenAI provisioned and standard deployment costs
Compare standard token billing with provisioned throughput using workload shape, cache rate, PTU sizing, deployed hours, and current Azure rates.
Steps7 min read
Compare model quality and cost with a repeatable evaluation
Choose a model with the same task set, quality gates, latency measures, usage records, retry policy, and applicable provider pricing.
Steps7 min read
Budget for AI search and tool-call costs
Separate model, grounding, search, session, and provisioned-capacity assumptions so AI workflow budgets remain traceable to provider billing.
Steps6 min read
Compare GPU reservation cost with actual useful work
Compare commitment cost, provider-reported utilization, GPU activity, and locally defined output before changing reserved GPU capacity.
Steps6 min read
Compare AI token prices without mixing token types
Compare Azure OpenAI, Amazon Bedrock, and Vertex AI pricing without blending input, output, cached, batch, and provisioned capacity.
Steps7 min read
Read FOCUS billed cost versus effective cost for GPU spend
Compare FOCUS BilledCost and EffectiveCost for GPU-oriented analysis, while checking invoice timing, charge timing, currency, service identity, and commitment treatment.
Steps6 min read