Operations | Monitoring | ITSM | DevOps | Cloud

For observability adoption to scale, you must eliminate the bottlenecks. A self-service approach is the only sustainable model, enabling all teams–not just a select few–to access, implement, and scale observability easily. But making the shift requires more than access: you have to design for it.

In modern microservices architectures, container observability is crucial for maintaining reliability and performance. It helps teams detect issues early and optimize distributed systems. This guide will walk you through the essentials of container observability, including advanced techniques and troubleshooting strategies to ensure your containerized applications run smoothly.

In cloud environments, logs are often spread across numerous services, making it difficult to track down issues or gather meaningful insights. For AWS users, this challenge can become especially time-consuming. Centralized logging in AWS helps by bringing all your logs into a single platform, making management and analysis easier.

Kubernetes is widely used for deploying, scaling, and managing systems and applications and is an industry standard for container orchestration. Google engineers originally developed Kubernetes as an open-source project. Its first release was in September 2014, and since then, it has matured into a graduate project maintained by the Cloud Native Computing Foundation (CNCF). With the complexities of scale and distributed systems, debugging in Kubernetes environments can be difficult.

"Imagine spending hours manually sifting through error logs, trying to pinpoint the root cause of a critical issue. This is a common challenge in software development." This is a problem that many engineers experience today and this process can be greatly improved an automated using AI agents. These AI agents will require as much context about the issue or problem as possible to be effective and have any opportunity to help improve this process.

Let’s be real, Prometheus and Grafana are great tools. They’ve earned their place in enterprise IT by offering solid infrastructure monitoring and visualization. But in complex, multi-middleware environments, these tools hit their limits. Picture this: a business-critical transaction is delayed or missing. Dashboards look fine. CPU and memory are stable. But something still feels off.

AI is a hot topic right now, but it’s only valuable when it drives action. The real goal is business intelligence—insight that’s timely, trustworthy, and tied to decisions that move the business forward.

Hey Elixir friends, great news! Honeybadger now offers performance monitoring for your Elixir, Phoenix, and Oban applications. Last year, we launched Honeybadger Insights, a new logging and performance monitoring tool bundled with Honeybadger. Insights allows you to query your logs and events to diagnose performance issues, perform root-cause analyses, and create charts and dashboards to see what's happening in real time.

Monitoring doesn't always need to be complex. In this guide, we'll show you how to turn raw logs into usable metrics using a lightweight open-source setup with no ELK stack and no heavy lifting. We'll use Loki, Python, and Telegraf to convert logs into Graphite metrics you can easily monitor or alert on. This is perfect for system admins, DevOps beginners, or anyone curious about building more innovative monitoring pipelines from scratch.