Operations | Monitoring | ITSM | DevOps | Cloud

Today, many DevOps and security teams operate in a world of complex, hybrid, or multi-vendor environments. As more teams look to avoid lock-in by adopting open standards, OpenTelemetry (OTel) is quickly gaining adoption as the primary open source method for DevOps and security teams to instrument and aggregate their telemetry data. However, OTel alone may lack the advanced processing functions, native volume control rules, and hybrid environment support that large organizations need.

AI observability closes the gap between “something’s wrong” and “here’s what to fix.” If you run AI in production, you might have felt the whiplash. Yesterday, your LLM answered in 300 milliseconds (ms). Today p99 crawls, costs spike, and nobody’s sure if the culprit is model behavior, data freshness, or GPUs stuck at the ceiling. Dashboards light up, but they don’t tell you which issue puts customers at risk. That’s the gap AI observability closes.

Non-deterministic UIs are the future and will replace your dashboards, but they’re not here yet. So until then, we’re stuck with conversational interfaces. In an effort to try and describe what I consider the future of UIs to look like, I wrote about how you (and I) have been designing dashboards wrong. The core insight was that we've been designing for static representations of data that sit on a TV in the office, when the actual use case is someone at a desk using them to debug an issue.

In July, we introduced powerful new Honeycomb Telemetry Pipeline features that helped teams take control of their observability data with safe sampling, flexible rehydration, and a visual pipeline builder. Since then, we’ve built on that foundation. Today, we’re introducing the latest enhancements to Honeycomb Telemetry Pipeline, which give teams deeper visibility into pipeline health, more efficient access to archived telemetry data, and reduced operational complexity.

More and more enterprises are shifting toward private cloud and hybrid deployments for control, data residency, and security. At the same time, observability is no longer a “nice to have” tool. It's mission-critical for teams driving rapid change across cloud-native, multi-service architectures. Leaders are realizing they need deep visibility and rapid debugging everywhere their systems run.

Node.js applications power millions of APIs, microservices, and real-time systems. But without proper monitoring, performance issues, memory leaks, and errors can go undetected until they impact users. This guide explains how to monitor Node.js applications in production, what metrics to track, and which tools deliver the best results.

When we introduced Canvas in beta, our goal was to reimagine how teams explore and collaborate around their observability data without requiring manual querying. Canvas has quickly become the AI-guided workspace that helps teams transform raw telemetry into meaningful, shared understanding faster than ever before. And today, we’re thrilled to announce that Canvas is now Generally Available (GA) for all Honeycomb users.