Reasoning models bill their thinking tokens as expensive output tokens, and costs can spiral quickly. Token-budget-aware reasoning (TALE) matches the token budget to problem complexity, with techniques like Chain of Draft and TALE-EP cutting output tokens by 49–67% with minimal accuracy loss. However, prompt-level budgeting has limits — models ignore overly tight constraints, and you still pay for repeated queries. Semantic caching (storing vector embeddings of prompts and serving cached responses on similar queries) can eliminate 30%+ of inference calls. Complexity-based routing sends simple queries to cheaper models, with studies showing up to 85–98% cost reductions. Persistent agent memory prevents re-deriving conclusions across sessions. OpenTelemetry GenAI conventions provide token tracking metrics. Redis Iris and LangCache are presented as infrastructure solutions for semantic caching and agent memory, with claimed 73% lower inference costs and 15x faster cache-hit responses.
