Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

Okay, I'm going to say something that would make 2016 Charity want to throw her laptop across the room: we're making a major investment in metrics at Honeycomb. I know, I know. "But Charity, you literally called them ‘shit salad!’" I did. Also "nerfed dimensions." I said they would "fucking kneecap you." For most of the past decade, I've been social media’s most reliable anti-metrics evangelist. Have I repented? No.

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.

Recently I've been to Lisbon's Web Summit conference, a 3 day, 70,000 participants, 15 stages, 800+ speakers event. Even though there was a track called "AI Summit", all the talks were about AI and AI Agents and how the future of the web, business, economy is more and more AI, and how businesses and people should take steps to adapt as soon as possible to an online world managed and operated by Artificial Intelligence.

Struggling with latency, congestion, or compliance issues? Discover when it’s time to move from public internet to private connectivity. Network operations have never demanded more than they do now, leading many network managers to question whether the public internet is enough. While many organizations begin their network journey with VPNs over the public internet, they often bump into limitations quickly and begin exploring the natural next step – private connectivity.

As major incidents like AWS’s October 2025 outage illustrate, modern systems are immensely interconnected. A failure in one can lead to a cascade of downstream problems. In this case, issues with DNS resolution for DynamoDB led to widespread disruptions with other AWS services and, subsequently, thousands of applications and services that rely on that infrastructure.

As cloud applications moved from VMs to containers and then to microservices, the amount of background work needed to keep everything running grew just as quickly. You gain speed and flexibility, but you also end up managing clusters, scaling rules, and capacity choices that don’t really add to the product you’re building. AWS Fargate steps in right there. It lets you run your ECS tasks without looking after any servers at all.

OpenTelemetry now supports maps, heterogeneous arrays, and byte arrays across all signals. Here’s where these new types shine — and where simple primitives still fit naturally. If you’ve been working with OpenTelemetry for a while, you’re likely familiar with the straightforward key-value approach to attributes. It’s simple, fast, and works well with how most telemetry backends store, index, and query data.

Everyone wants to move faster with AI, but few are truly ready for it. This is where organizations can position themselves better as AI readiness isn’t just about algorithms, it’s about whether your data, systems, and teams are prepared to support intelligent automation safely and effectively.