Production, Observability & Staying Current
Ship AI features to production safely — streaming for perceived latency, Micrometer + Grafana for observability, cost control and guardrails, and a system for staying current with a fast-moving ecosystem.
Streaming Responses: Fixing the Spinner Problem
Stop making customers stare at a spinner for a 6-second reply. Stream tokens as they're generated with ChatClient.stream(), a Server-Sent Events endpoint, and a browser EventSource.
Micrometer Observability: Answering 'How Many Tokens Did We Spend?'
The question nobody could answer in this roadmap's first guide, finally answered — Spring AI's built-in Micrometer instrumentation, the real metric names, and adding your own custom metrics.
Grafana Dashboards: From Metrics to an Actual Dashboard
Turn the Micrometer metrics from the last guide into a real Grafana dashboard — token throughput, latency percentiles, cost estimates, active streams, and the alerts that page someone before a customer complains.
Cost Tracking, Prompt Caching & Guardrails
The token-budget alert from the last guide just fired. Here's what actually brings the number back down — per-feature cost tracking, prompt caching, rate limits, and a cost-aware model router.
Staying Current — and What You Actually Built
This roadmap will be stale the day a new Spring AI version ships. Here's the system for keeping up with it, Embabel, and LangChain4j — plus what all seven phases actually add up to.