Why AI Systems Become Expensive: Tokenization, Chunking, and Retrieval Design in the Cloud (AWS)

When building modern AI knowledge systems, discussions often jump directly to prompts, retrieval pipelines, or model selection. However, long before a model generates an answer, something more fundamental happens that your data must be transformed into a format that models can understand and retrieve efficiently. This transformation typically involves several foundational steps: 1. Tokenization – Converting raw text into model-readable units 2. Chunking – Splitting documents into manageable segments 3. Vectorization – Converting text into embeddings 4. Indexing – Storing vectors for efficient similarity search These steps form the foundation of retrieval-based AI systems, and design decisions at this stage often have a greater impact on system performance than prompt engineering or model tuning. These architectural considerations are also increasingly relevant for modern AI development tools such as Claude Code, OpenAI Codex–based systems, and other AI-powered coding assistants. Althou