Category: AI Cost Optimization
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Prompt Caching for AI APIs: When It Saves Money—and When It Costs More
Last reviewed: August 30, 2026. API interfaces and product settings change; verify current official documentation before production deployment. LLM prompt caching can reduce repeated input processing, latency, and cost when many requests share a stable prefix. It can also add cache-write cost without meaningful hits when prompts change constantly or traffic is too sparse. Measure…
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Input Tokens vs Output Tokens: How AI API Pricing Really Works
Understand input tokens vs output tokens, context windows, cached tokens, reasoning usage, agent loops, cost formulas, and practical ways to lower API spend.
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LLM Self-Verification: Can Cheap Models Beat Frontier Models on Cost?
Learn when multiple affordable-model candidates plus validation can cost less than one frontier-model call, and how to test the tradeoff safely.