Operations | Monitoring | ITSM | DevOps | Cloud

Alibaba Cloud is a major cloud provider in APAC, offering industry-leading foundational AI models in addition to compute, managed databases, object storage, and Kubernetes through its Container Service for Kubernetes (ACK). Teams choose Alibaba Cloud for its infrastructure availability across Asia Pacific and its managed services. For SREs and platform engineers, that often means running Alibaba Cloud alongside AWS, Google Cloud, or Microsoft Azure.

The Observability Gap shows up the moment you try to reproduce a production bug locally. Your traces tell you a request was slow. Your logs tell you which line printed. Neither tells you what was actually on the wire: the headers, the JSON body, the surprise field your client started sending last Tuesday. Until now, closing that gap meant SSHing to a node, attaching a debugger, or shipping a sidecar through change review.

Is your monitoring stack really giving you clarity, or just more alerts? Your monitoring stack is probably working exactly as designed. That is the problem. As systems grow, most IT and platform teams start to see the same patterns: At this point, traditional monitoring starts to feel limited. This is where teams begin exploring AI in observability. In this guide, we will explain what AI-powered observability actually means, how it works, and when it is useful.

We just wrapped O11yCon 2026, and this year's conversations hit differently. Agent-based software development is here, now. It's no longer an optional choice, and everybody is struggling to understand what their agents are doing and how to make them cost less and perform better. Over the course of fifteen talks, we saw clearly that the old assumptions on how and who (or what) writes our software has been upended. Here are some highlights. We'll have videos available in the near future.

Hybrid cloud has reshaped the way enterprises build, run, and troubleshoot digital services. Applications now stretch across on-premises infrastructure, cloud platforms, regional services, interconnects, and distributed dependencies that change constantly. Operational complexity has expanded with that footprint, yet many observability practices still reflect assumptions from an earlier era of simpler architectures and clearer boundaries. That gap shows up fast during an incident.

Modern engineering teams are drowning in telemetry data. A mid-sized Kubernetes cluster running 50 microservices can generate millions of log lines per minute. Add distributed traces, Prometheus metrics, cloud provider events, and application-level instrumentation and you're looking at terabytes of observability data every day. The problem isn't just volume. It's what you do with it.