Operations | Monitoring | ITSM | DevOps | Cloud

The AI SRE landscape has exploded over the past year, with vendors racing to add artificial intelligence capabilities to their platforms. For engineering leaders evaluating these solutions, the sheer number of options can feel overwhelming. Some vendors are building AI-native solutions from scratch, while others are retrofitting AI onto existing workflows. Cloud providers are embedding agents into their ecosystems, and observability platforms are adding intelligence layers to their telemetry data.

When you run services on Quarkus, you need a steady stream of signals to understand how the application behaves—CPU trends, request timings, memory patterns, and how each endpoint responds under load. Metrics give you that visibility. They help answer questions like: OpenTelemetry fits well here because it gives Quarkus a common way to generate and export metrics without locking you into a specific monitoring tool.

Running distributed systems means you need clear visibility into how your services behave. Prometheus has been the standard for metrics for a long time, and OpenTelemetry is now giving teams a more consistent way to collect telemetry across their stack. In many setups, you'll have both: existing Prometheus instrumentation that's already in place, and new components instrumented with OpenTelemetry.

When you're shipping LLM features, a lot of the work goes into keeping the model's behavior predictable. You deal with questions like: These are everyday concerns when you integrate LLMs into production systems. Guardrails AI provides a Python framework that helps you enforce those expectations. You define the schema or constraints you need, and the framework validates both the inputs going into the model and the outputs coming back.

As systems grow, understanding how a request moves across multiple services becomes harder. Traces help bring this picture together by showing the exact path a request takes, along with the timings that matter. Grafana Tempo is built for this kind of workload. It stores traces efficiently, works well with OpenTelemetry, and keeps the operational overhead low.