Spring AI Guide: Measure Token Usage Before Optimising LLM Costs

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A four-part developer guide on controlling LLM costs in Spring AI opens by emphasising measurement as the essential first step before making any optimisation decisions. The article explains that Spring AI integrates with Spring Boot's Micrometer-based observability to track token usage across ChatModel, EmbeddingModel, and ImageModel components. Built-in metric tags identify models and token types, but developers must add custom low-cardinality tags to attribute usage to specific features or clients within an application. The guide also recommends setting alerts for sudden spikes in token consumption and configuring provider-side spending limits as additional safeguards. Treating observability as step zero ensures every subsequent cost-cutting change has a measurable baseline for comparison.

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