The AI Cost Optimization Blog
Practical, in-depth writing on reducing AI spend and building efficient LLM systems — prompt caching, model routing, RAG, agents, and infrastructure.
English vs Chinese vs Japanese Tokens: A Reproducible Experiment
Compare nine official Gemini token counts against one character heuristic—and see why the frozen Japanese sample was underestimated by 13.5% to 25.6%.
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Batch API vs Standard API: The Real Cost After Reruns
See how much of the 50% Batch token discount remains after application quality checks, selective reruns, result reconciliation, and provider-specific operating limits.
TokenCostAI Editorial Team
August 23, 2026 · 11 min read
Prompt Cache Break-Even: When Caching Actually Saves Money
Calculate how many successful cache reuses are needed to offset write premiums or TTL storage—and why best-effort caching must be modeled from observed hit rates.
TokenCostAI Editorial Team
August 22, 2026 · 11 min read
AI API Pricing in August 2026: A Reproducible 11-Model Baseline
Normalize input, cached input, output, context, promotional, and peak-rate differences across 11 current API models with downloadable JSON, CSV, and a reproducible scenario.
TokenCostAI Editorial Team
August 22, 2026 · 9 min read
Three Reproducible AI Cost Scenarios: Support, RAG, and Agents
Three transparent, simulated workloads with published inputs, formulas, and results you can reproduce in the TokenCostAI calculator.
TokenCostAI Editorial Team
August 22, 2026 · 9 min read