Operations | Monitoring | ITSM | DevOps | Cloud

Serverless computing has changed the way developers build and scale applications. With event-driven execution, automatic scaling, and a pay-as-you-go model, it removes the need to manage servers and helps teams move faster. This is why industries like FinTech, e-commerce, and media streaming are adopting serverless at a rapid pace. But serverless also brings new monitoring challenges. Functions are short-lived, run in different places, and are triggered by many types of events.

Recently I was importing an Icinga configuration for testing purposes. Working with this configuration, I found that there were icon images assigned to the objects. Sadly, those didn’t display, because I didn’t have the icon set installed. So I thought of creating my own.

If you’re struggling to contain cloud costs in this suddenly volatile AI-fixated environment, it might be time to consider FinOps as an exercise in granular allocation and unit economics, with a focus on outcome.

There’s a lot of JavaScript developers these days not actually writing code. They whisper sweet prompts to our AI tools and hope for the best. Is it really any worse than copy-pasting from StackOverflow? Welcome to the era of vibe coding, where understanding your code is optional and “it works on my machine” has evolved into “the AI said it would work.”

The OpenTelemetry Astronomy Shop is a widely used demonstration environment designed to illustrate the concepts and practical implementation of observability in distributed systems. Built as a microservice-based e-commerce application, the demo provides developers with a near real-world environment where they can explore how telemetry data—metrics, logs, and traces—can be collected, processed, and visualized.

When a payment service runs across 12 pods — each serving different customer segments — and an authentication layer spans three namespaces, performance issues can originate in both the application code and the orchestration layer. The challenge is linking request-level performance data with what’s happening inside the cluster: container CPU limits, pod scheduling decisions, and node-level events.

Logic errors, failed HTTP requests, background jobs that ghost silently—software breaks in all kinds of fun ways. The difference between resilient systems and fragile ones isn’t about avoiding errors altogether. It’s about how fast and clearly you can see what went wrong, and fix it. Laravel gives you a solid foundation: structured logging, real-time introspection, and built-in performance monitoring.

Last week, we were busy at AppSignal with our first-ever Launch Week and Rails World 2025! In case you missed it or spent the time snacking on our stroopwafels in Amsterdam, here's a quick recap of what we launched, and what that means for your monitoring setup!

While headlines celebrate each new breakthrough in AI capabilities, businesses are quietly mastering a different art: deploying focused AI solutions that target specific operational challenges. This shift changes everything. We're moving from generic AI that tries to do everything to, as Gartner says, an ROI-driven implementation that does exactly what your business needs. The future of AI's successful adoption lies in smarter applications that solve real enterprise problems.

In 2022, we had three different query interfaces. Logs had a custom search syntax with no autocomplete. Traces only had predefined filters - no query builder at all. Metrics had a raw PromQL input box where you'd paste queries from somewhere else and hope they worked. Each system spoke a different language. An engineer debugging a production issue had to context-switch not just between data types, but between entirely different mental models of how to query data.