Operations | Monitoring | ITSM | DevOps | Cloud

You can instrument Jenkins with OpenTelemetry using the official plugin and an OpenTelemetry Collector, then send the data to a backend like Last9 to understand where pipeline latency and failures actually originate. Jenkins provides job status and console logs, but it doesn't show how time is distributed across stages, agents, plugins, and external systems. OpenTelemetry fills that gap by emitting traces, metrics, and logs in a standard format that any OTLP-compatible backend can process.

You don’t have to choose between capturing every signal and keeping costs predictable. Modern observability stacks blend full-fidelity storage (time series or columnar systems like ClickHouse and Apache Druid), tail-based sampling for heavy traffic, and tiered storage (hot/warm/cold with S3-backed archives). This gives you full-fidelity incident forensics with the day-to-day cost profile of a sampled setup.

You can use an observability platform that automatically discovers your services and provides ready-to-use dashboards with minimal setup. If you're running a system where microservices come and go, containers shift around, or serverless functions scale up quickly, this kind of experience saves you a lot of time. You gain visibility as soon as something goes live, without requiring any additional steps on your part. In this blog, we talk about the top seven platforms that offer these capabilities.

You can cut your log costs by removing repetitive, low-value logs early and keeping only the parts that genuinely help you understand issues. Modern systems generate logs far faster than you expect. Even when your workload stays stable, infrastructure components, retries, and background workers continue producing a steady stream of repeated entries.

OpenTelemetry now supports maps, heterogeneous arrays, and byte arrays across all signals. Here’s where these new types shine — and where simple primitives still fit naturally. If you’ve been working with OpenTelemetry for a while, you’re likely familiar with the straightforward key-value approach to attributes. It’s simple, fast, and works well with how most telemetry backends store, index, and query data.

As cloud applications moved from VMs to containers and then to microservices, the amount of background work needed to keep everything running grew just as quickly. You gain speed and flexibility, but you also end up managing clusters, scaling rules, and capacity choices that don’t really add to the product you’re building. AWS Fargate steps in right there. It lets you run your ECS tasks without looking after any servers at all.

You know that uneasy pause before opening your monitoring dashboard? The one where you're hoping nothing's broken—but a part of you knows something probably is. Performance issues often start quietly: a few slow endpoints, a checkout that takes longer than usual, a graph that looks a little off. Before long, those small signals turn into alerts and support tickets.

When you run Kubernetes at scale, one of the first challenges is understanding what the cluster is actually doing. Workloads shift around, pods restart for normal reasons, and traffic doesn't always follow the patterns you expect. Having clear signals makes day-to-day operations much easier. That's where kube-prometheus-stack helps. It brings Prometheus, Grafana, Alertmanager, and supporting components together as a single package.

Some parts of a system don’t lend themselves to quick instrumentation changes. You might have a production binary that hasn’t been rebuilt in years, or a stack made of several languages where each team manages telemetry differently. In those situations, getting consistent signals often means touching code you’d rather leave alone or coordinating updates across many services. OpenTelemetry eBPF Instrumentation (OBI) approaches this from the kernel side.

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.

Application configs change over time, often in small ways that are easy to miss. They may start simple — a few environment variables, one exporter, nothing unexpected. As your instrumentation grows, you add rules for filtering health check spans, adjust sampling based on attributes, or introduce environment-specific resource settings. Each change makes sense on its own. But months later, the picture can look different across dev, staging, and production.