LLM Cost Optimization and Model Routing: A Practical Guide for Engineering Leaders

Enterprise generative AI spending rose to an estimated $37 billion in 2025, driven by token-based pricing, large differences in model costs, and agentic workflows that can generate repeated calls, retries, and expanding context. Effective cost control requires forecasting by task type, model, and input versus output tokens, then applying static model tiers, prompt caching, reasoning limits, and batch processing before adopting more complex learned routing systems. Routing should balance savings against task-specific quality thresholds, using measures such as accuracy, accepted code changes, or resolution rates to avoid downstream costs from poor responses. Request-level attribution across developers, teams, projects, models, token types, retries, and outcomes helps organizations identify inefficient usage and relate spending to useful work. Broad cloud FinOps platforms, LLM observability gateways, and enterprise AI gateways address different needs, while the text positions Weave as an engineering-intelligence product that combines quality-aware routing with token and delivery-data tracking.

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