Short, practical explainers on how AI models count tokens, how pricing works, and where teams quietly overspend. Written for developers shipping on LLM APIs.
Tokens explained: chunking, words-to-tokens conversion, context windows and why every API bill is counted in tokens.
Read guide → Cost optimizationHow OpenAI, Anthropic, Google and DeepSeek cache prompts — writes, reads, TTLs — and how to save 50–90% on repeated input.
Read guide → Cost optimizationModel right-sizing, batching, output caps, history trimming and more — ranked by effort-to-savings ratio.
Read guide → ReferenceAll 51 supported models ranked by context size, with approximations for words, pages and codebases that fit inside.
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