Spring AI Token Usage: Measure Cost Before You Pick a Model — LLM Cost Control 1/4

Cutting LLM costs in Spring AI starts with two choices: which model answers a request, and what defaults your ChatClient adds to every one it sends. Neither is worth changing until you can see where the tokens go. That is why this article starts with measurement. This is Part 1 of four, and it covers the first three of ten cost drivers. Driver #0 tells you where the money actually goes; #1 and #2 are the two decisions that shape every request your application sends. The remaining seven attach to what you build here. A note on the numbers: where a price ratio matters for the argument (input vs. output, cache read vs. write), this series quotes real July 2026 list prices with a link. All other examples use a flat rate of $1 per million input tokens, so you can redo the calculation with your own provider's price sheet. You should do that, because these prices change every few months. Driver #0 — Spring AI observability measurement: you cannot cut what you cannot see Provider invoices and usage dashboards usually show your spending by model and by token type — input, output, and cached. That is useful, but it is not enough. The numbers cannot tell you which feature, client, or advisor inside your application was responsible for that usage. Spring AI integrates with Spring Boot's Micrometer-based observability to fill this gap. Its core AI components automatically emit that data. ChatModel , EmbeddingModel , and ImageModel implementations (support varies by provider) publish model-level observations, including token usage where available. ChatClient (including advisors) and VectorStore report execution observations and traces rather than token usage metrics. Each metric includes built-in tags, such as the model name and token type. These tags separate models and providers, but not callers: every request to the same model carries the same tag values, so they cannot tell two features apart on their own. Spring AI marks tags as low- or high-cardinality: low-cardinality tags