Browse all 11 articles in this editorial series. Clear explanations of tokens, generation costs, and the trade-offs behind model pricing.
Featured starting point
A practical way to read input, output, cache, and per-generation prices before you ship a feature.
Suggested learning path
01 → 11
A practical way to read input, output, cache, and per-generation prices before you ship a feature.
Compare rule-based, cost-aware, latency-aware, and quality-aware model routing with a practical decision framework.
Define the metrics that explain whether an AI gateway is healthy and how to turn them into operational decisions.
A current, source-backed guide to reading DeepSeek API pricing, estimating a real workload, and avoiding surprises from cache or peak-hour rules.
Understand what Claude API credits mean in practice, how token and prompt-cache rates are separated, and why cloud marketplace billing can look different.
How to compare Gemini API pricing tiers, separate prompt and output usage, and turn a model table into a production budget.
Compare model API prices without falling for headline rates: normalize tokens, caching, modalities, limits, and the cost of a successful request.
A practical guide to distinguishing trial credits, account quotas, and normal Token billing when evaluating a DeepSeek API free tier.
A current-pricing workflow for Grok API users: separate token rates, context, output, limits, and account billing before choosing a model.
How to compare Kimi API costs across model IDs, token mix, region, account, and gateway routing without confusing a reference price with a final bill.
Design a token-based billing system that keeps customer credits, provider usage, retries, refunds, and reconciliation explainable.
