Operations | Monitoring | ITSM | DevOps | Cloud

Gartner recognizes Last9 in their latest Cool Vendor report for unified telemetry and agentic SDK—moving teams from reactive monitoring to proactive ops. Founder at Last9. Loves building dev tools and listening to The Beatles.

If you’re building high-performance systems, Golang has probably earned a spot in your stack. Its speed, lightweight concurrency, and quick compile times make it ideal for scalable APIs, microservices, and distributed systems. But those same qualities that make Go powerful can make performance monitoring tricky. Goroutines run fast and in parallel, which means a simple CPU or memory graph doesn’t always tell you what’s slowing things down.

Most developers use auto-instrumentation as it’s meant to be used — run the Java agent, add NODE_OPTIONS, and telemetry starts flowing. When it stops, though, figuring out why can be tricky. Maybe the agent didn’t load, maybe there’s a framework version mismatch, or something else entirely. Understanding how auto-instrumentation works makes it easier to spot and fix these issues.

PHP powers a large swath of the web — from blogs to storefronts to APIs. But with microservices, third-party dependencies, and scaling complexity, performance can slip in subtle ways. Your app might mostly work, but small—noted delays, occasional spikes, or hidden bottlenecks build up. An APM tool helps you see inside the black box: which functions are slow, which DB queries are hogging time, which external calls are failing or stalling.

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.