Operations | Monitoring | ITSM | DevOps | Cloud

Observability Without Tradeoffs: Introducing Powerful New Honeycomb Telemetry Pipeline Features

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.

Highlight reel: Futureproof Your AI Investment With Observability

Artificial intelligence is changing the way modern systems are built—and how teams are expected to and operate them. But as AI-driven complexity grows, so too does the need for deep, reliable, and fast visibility into what’s really happening inside our. In this timely and thought-provoking session, Christine Yen, CEO and Co-founder of Honeycomb, explores how practices must evolve to keep pace with.

Honeycomb Observability Day London: A Jam-Packed Day of Great Talks

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.

Tales From the Trench: Building With LLMs and Honeycomb

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!

It's The End Of Observability As We Know It (And I Feel Fine)

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.

MCP = Observability + Code, a Real-life Example

Our bot is hitting an error. We can see it in the distributed trace. Here, see what happened when we noticed it: Austin fired up Claude Code (hooked up to Honeycomb with its MCP tool) and got it to find the error, fix it, deploy, and check that the fix worked. It got a little overconfident at first, but the ending is happy. IRL this took 22 minutes; the video speeds up the AI agent interactions and cuts out waiting. This video includes Austin Parker, Jessica Kerr, and Ken Rimple.

Beyond Shift Left: Engineering Leaders Increase Speed and Resilience With Observability

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?