AI Token & LLM Cost Engineering Guides
Deep-dive technical documentation written for engineers architecting, evaluating, and scaling production AI systems. No fluff—just equations, code examples, and verified benchmarks.
How AI Tokenization Works: BPE vs SentencePiece
An engineer's breakdown of Byte-Pair Encoding, byte fallbacks, subword vocabularies, and why character counts never equal token counts.
Tokens vs Words: The Definitive Conversion Guide
Exact conversion rules for natural English prose, source code indentation, JSON payloads, and multi-page PDF documents.
Prompt Caching Economics: How KV Caching Saves Up to 90%
Deep dive into Key-Value attention cache reuse, write-cache amortizations, TTL lifetimes, and breakeven hit-rate formulas.
LLM API Pricing Explained: Input vs Output & Reasoning Tokens
Why generation tokens cost 3x-5x more than input tokens, how internal chain-of-thought tokens are billed, and how to compute blended rates.
RAG Cost Architecture: Ingestion, Chunking & Vector Retrieval
A complete economic blueprint for Retrieval-Augmented Generation: embedding corpus costs, vector database searches, and top-k context sizing.
AI Agent Cost Modeling: Loops, Tool Calls & Context Bloat
How multi-turn autonomous loops compound token consumption exponentially, and how prompt caching mitigates ballooning agent expenses.