ai-observability

You can monitor token usage and latency in Spring Boot AI applications by wiring Micrometer and Prometheus into your AI calls. This Skill provides built-in instrumentation to track generation latency, token consumption, and AI client metrics automatically.

Yes, you can estimate AI API costs per model using this Skill's custom cost estimation feature. It calculates usage costs based on per-model pricing and exposes them alongside your standard Micrometer and Prometheus metrics.

Setting up audit logging for Spring AI chat interactions involves configuring the provided Java and Spring components to persist prompts and responses. This Skill generates persistent audit logs with metadata to improve reliability and governance.

Yes, this observability Skill works directly with existing Prometheus setups by relying on Micrometer integration. You simply add the Micrometer dependencies and enable metrics exposure in your Spring Boot YAML configuration to expose the endpoints.

The best way to track per-model token usage in Spring AI evaluation pipelines is to apply custom AI metrics and auditing. This Skill instruments model calls to capture structured logs detailing latency, tokens, and specific model usage.

Yes, there are configuration gotchas when enabling tracing for AI observability. This Skill provides guidance for safely exposing management endpoints, configuring tracing, and handling safety considerations specific to Spring Boot integrations.