Operations | Monitoring | ITSM | DevOps | Cloud

Over and over, we’ve seen that teams who invest in adding rich, relevant context to their telemetry end up debugging faster and collaborating more effectively during incidents. Getting meaningful context added can feel like a big cross-team project, but some of the highest-leverage improvements don’t require app code changes or coordination across services.

One of the most common challenges in modern software engineering today is understanding how requests flow through applications. As system architectures shift to favor widely distributed, cloud-native designs, keeping track of how an application processes user actions is more difficult than ever. A single user action may trigger events processed in dozens of backend services. Traces are helping software developers today with this challenge.

When we talk to new Honeycomb users, a few things stand out as sounding downright magical. Sometimes we’ll hear, “Wow, is that a new feature?” and we’ll say that no, it’s been like that for years. Clearly we need to get the word out! This is the first installment of a blog series I’ll be writing, covering areas of Honeycomb that elicit reactions of awe and disbelief from new users.

At LDX3 in London last week, two roundtables I hosted with engineering leaders confirmed what many of us are starting to feel: observability isn’t just important—it’s becoming essential to how modern teams navigate the pressure to move fast and stay resilient.

One of the great things about OpenTelemetry is that it’s a standard, and standards tend to proliferate. I was excited to see Claude Code add OpenTelemetry metric and log support in a recent release. What was really interesting—beyond the ability to capture usage data from Claude Code—is that you can also get pretty detailed logs about what you’re doing with Claude Code.

Every day, enterprise companies generate terabytes of observability data while engineering teams are under pressure to cut costs. One of the easiest ways to reduce observability bills is through sampling: intentionally sending only a representative portion of telemetry data, rather than the full volume, to your observability tool. But turning down the dial is risky.

On May 15th, 2025, Honeycomb hosted Observability Day (or O11yDay) in the London financial district. The skies were clear and the weather was wonderful and we had a huge turnout, from our networking breakfast to the happy hour at the end of the day.

AI discourse these days is all over the place. Depending on who you talk to, AI’s are absolute flash-in-the-pan junk, or they’re the best thing since sliced bread. I want to cut through the noise, though, and see for myself what someone can do out here on the bleeding edge. Thus, I’m setting myself a challenge: write a usable—and useful—application with Claude Code, from soup to nuts. Here are the rules: With our ground rules established, let’s figure out our app!

In a really broad sense, the history of observability tools over the past couple of decades have been about a pretty simple concept: how do we make terabytes of heterogeneous telemetry data comprehensible to human beings? New Relic did this for the Rails revolution, Datadog did it for the rise of AWS, and Honeycomb led the way for OpenTelemetry.

We recently had the privilege of hosting several industry experts and technology executives across platform strategy, SRE, and engineering enablement for breakfast at our Observability Day in London. We noted that they’re all facing the same fundamental tension: deliver faster, scale smarter, stay resilient, and somehow get ahead of what’s coming next. But how do you move fast without breaking things? And how do you prove the value of the things you don’t break?