GenAI cost management
GenAI cost management is different from traditional SaaS cost control because the main cost object is not a server; it is a model call. The cost of a single user action can vary by model, context length, output length, cache state, retry behavior, and provider route.
The first step is to classify workloads. User-facing chat, support summarization, document extraction, evals, enrichment jobs, and agent workflows should not share one budget model. Each has a different latency requirement and quality bar.
High-leverage controls
- Model routing for easy versus hard requests.
- Prompt and semantic caching for repeated work.
- Context pruning for RAG and agent memory.
- Batch APIs for evals, enrichment, and offline processing.
- Provider benchmarking for equivalent open-weight models.
What to avoid
Do not optimize only for cheapest model. A cheaper model that retries, escalates, or produces lower-quality output can raise total cost. The metric should be cost per successful task under agreed quality and latency constraints.
Good GenAI cost management makes product teams faster because they can see the cost of design choices before the invoice arrives.