In Part 1 we prototyped GenAI observability with the Camel CLI and TUI. This follow-up — Phase 3 (Operate) — shows the same gen_ai.* telemetry in a Spring Boot application wired to the observability stack Camel ships for local development: Prometheus, VictoriaTraces, and Perses. The runnable sample lives in the camel-spring-boot-examples repository at genai-observability (reworked in PR #192). Architecture Terminal 1: ollama serve Terminal 2: camel infra run observability Terminal 3: mvn spring-boot:run ┌─────────────────┐ scrape :9876/observe/metrics ┌──────────────┐ │ Spring Boot │ ───────────────────────────────► │ Prometheus │ │ Camel + 2 LLMs │ │ :9090 │ │ app :8080 │ └──────┬───────┘ │ mgmt :9876 │ │ └────────┬────────┘ ▼ │ OTLP (Micrometer Tracing) ┌──────────────┐ ▼ │ Perses │ ┌─────────────────┐ dashboards │ :3000 │ │ VictoriaTraces │ ◄────────────────────────────────└──────────────┘ │ :10428 │ └─────────────────┘ Two timer routes call two small Ollama models (llama3.

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AIHOWTOS

Once LLMs live inside Camel routes, the next question is always the same: how much are we spending, and where is latency coming from? Apache Camel 4.23 introduces GenAI observability — OpenTelemetry spans and Micrometer metrics for LLM producers, aligned with the OpenTelemetry GenAI semantic conventions. This is Blog 1 in a two-part series. We start with the fastest path to visible AI telemetry: the Camel CLI, a LangChain4j chat route, Ollama, and the Camel TUI — no Spring Boot required.

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AIHOWTOS

If you are building event pipelines, metrics dashboards, or log analytics, there is a good chance ClickHouse sits at the end of the line. It is fast, column-oriented, and built for append-heavy workloads. The question for Camel users is usually not whether to use ClickHouse, but how to connect to it cleanly from routes. You can already reach ClickHouse through JDBC or the generic SQL component. That works — but you miss native format streaming, server-side async inserts, and the tuning knobs that make ClickHouse fast at scale.

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HOWTOS