Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes, by default, adopts a permissive networking model where all pods can freely communicate unless explicitly restricted using network policies. While this simplifies application deployment, it introduces significant security risks. Unrestricted network traffic allows workloads to interact with unauthorized destinations, increasing the potential for cyberattacks such as Remote Code Execution (RCE), DNS spoofing, and privilege escalation.

In our increasingly serverless world, observability isn’t just a “nice to have”—it’s essential. Serverless functions such as AWS Lambda bring incredible benefits, but they also introduce complexities, especially around monitoring and debugging. In a previous article, I provided a quick, practical guide for sending AWS Lambda traces to Grafana Cloud using OpenTelemetry.

AI systems, from machine learning models to Large Language Models (LLMs) and autonomous AI agents, introduce unique observability challenges. Their non-deterministic nature, complex dependencies, and specialized performance characteristics require thoughtful instrumentation approaches. OpenTelemetry has emerged as the leading standard for implementing observability across these systems.

One of the most common things we hear from our users, is how AWS costs keep increasing with CloudWatch often playing a big role. CloudWatch has long been the default observability solution for AWS users. While it’s great for some use-cases, it’s also important to check out and weigh other alternatives which could be better suited for modern observability demands. Let’s examine some areas where modern observability platforms outweigh CloudWatch. Note.

The evolving digital technologies and artificial intelligence (AI) fundamentally reshape business dynamics. Analyzing the growth and impact of running online businesses, several organizations from different industries started adapting this modern approach to create revenue streams and enhance their customer experience. On one end, it turned out to be a brilliant strategy; on the other, managing the complex business data and systems was a big challenge.

Seems like you can’t throw a rock without hitting an announcement about a Model Context Protocol server release from your favorite application or developer tool. While I could just write a couple hundred words about the Honeycomb MCP server, I’d rather walk you through the experience of building it, some of the challenges and successes we’ve seen while building and using it, and talk through what’s next. It should be pretty exciting, so strap in!

Traditional monitoring approaches have served IT operations for decades, providing basic visibility into system health through predefined metrics and thresholds. However, these conventional methods face significant limitations when confronted with modern, complex environments: Static Thresholds and Rules Traditional monitoring relies heavily on manually defined thresholds and rules.

An overview of the benefits, challenges, and philosophy behind consolidating your observability tools Picture this: It's 3:00 a.m., and your phone is buzzing with alerts from what seems like a dozen different monitoring tools. As you blearily scroll through the notifications, you can't help but wonder, "How did we end up with so many tools, and why can't they just talk to each other?".

Remember when monitoring your apps meant checking if they were up or down? Yeah, those days are long gone. As systems have gotten more complex—microservices talking to other microservices, containers spinning up and down, serverless functions doing their thing—the approach to understanding system health has had to level up too. APM tools have been the bread and butter for DevOps teams for years, but now everyone's talking about observability.

Effective monitoring and optimization of an interdependent environment require a coordinated strategy. Through the integration of observability and digital experience monitoring (DEM) platforms, businesses can dismantle silos and obtain a real-time, comprehensive view of their whole digital infrastructure. This comprehensive strategy empowers enterprises to proactively handle problems and optimize the end-to-end digital experience, which also improves performance and risk management.