Operations | Monitoring | ITSM | DevOps | Cloud

When developers monitor application performance, they pick one of two paths: traditional APM tools with distributed tracing and code profilers, or metrics-driven monitoring with Prometheus. The second approach — Prometheus APM — tracks the signals that matter most: request rates, error rates, latency, and resource utilization. No agents to install, no per-host pricing, just exporters and PromQL. For most teams, Prometheus APM is where monitoring starts.

In modern IT systems—distributed services, cloud-native platforms, and dynamic networks—just knowing that something is “up” isn’t enough. Green checkmarks on dashboards don’t tell you why performance shifted, why latency crept in, or why a perfectly healthy-looking service suddenly failed. This is where the conversation around visibility and observability begins. They sound similar, but they solve very different problems.

An incident’s in progress. Services are slow, customers are frustrated, and your dashboards… look fine. At least, until you search for payment metrics and get 47 different names for the same signal. Suddenly, the real issue isn’t latency — it’s inconsistency. The OpenTelemetry project recently published a three-part series on naming conventions to solve exactly this problem.

Sometimes Docker behaves in ways that catch you off guard—containers don’t start as expected, images pause during pull, or networking takes longer than usual to respond. In those moments, the Docker daemon logs are your best reference point. These logs capture exactly what the Docker engine is doing at any given time. They give you a running account of system state, performance signals, and events that help you understand what’s happening beneath the surface.

Are your Java applications running at their optimal performance, or is there room for improvement to make them faster and more efficient? With so many services depending on Java, keeping applications responsive and reliable is a core part of modern software engineering. This blog walks you through the leading Java Application Performance Monitoring (APM) tools, with a clear comparison to help you choose the right option for your needs.

It’s 3 AM. API latency just spiked from 200ms to 2s. Alerts are firing, and users are frustrated. You SSH into the first server: top, free -h, iostat — nothing unusual. On to the next host. And the next. That’s how most of us learned to debug. The tools worked, and we got good at using them. But as infrastructure became distributed and dynamic, this approach started to break down. Modern monitoring needs more than SSH and top. It needs unified telemetry.

Application Performance Monitoring (APM) helps you understand how your software runs in production. When you track the right metrics, you see how requests move through your system, where slowdowns happen, and how resources are being used. With this knowledge, you can spot issues early and keep your applications reliable for your users. In this blog, we discuss the key APM metrics to monitor, grouped into categories, and why each one matters for performance and user experience.

Microservices make it tough to understand how applications behave end-to-end. Most teams already rely on an Application Performance Monitoring (APM) tool to track system health. But as requests move across many services, you also need distributed tracing. Jaeger gives you that visibility. The real value comes from connecting the two. Instead of running APM and Jaeger in silos, you can combine their strengths, metrics from your APM, and traces from Jaeger, to get a clearer view of performance.

You've got Prometheus running in one cluster — maybe a dev environment, a single EKS cluster, or a proof-of-concept setup. The configuration is straightforward: node_exporter on a few EC2 instances, some service discovery for pods, and a single Prometheus server scraping everything. Storage is local, retention is 15 days, and you can keep all the default recording rules without worrying about costs.

Modern applications don’t process everything inside the request/response path. To keep APIs responsive, time-consuming work like image resizing, payment processing, or data syncs is moved into background queues. Workers then pick up these asynchronous jobs and run them outside the main thread. Asynchronous job monitoring is the practice of tracking these background tasks: Without this visibility, background workers become a blind spot.