When you’re managing a handful of nodes, the flat list in the nodes tab works fine. When you’re managing hundreds or thousands, it becomes a wall of hostnames. You end up applying the same filters repeatedly: all the production database servers, all the nodes in eu-west, all the Kubernetes workers in the staging cluster. The filters work, but they don’t persist, and there’s no way to share them with the rest of your team. Node groups solve this.

Performance optimization often feels like searching for a needle in a haystack. You know your code is slow, but where exactly is the bottleneck? This is where continuous profiling comes in. In this blog post, we’ll explore how continuous profiling with Alloy and Pyroscope can transform the way you approach performance optimization.

Imagine you're troubleshooting a production issue: your application is slow, the CPU is spiking, and users are complaining. You turn to your profiler for answers—after all, this is exactly what it's built for. The profiler runs, collecting thousands of stack samples. eBPF profilers, including the OpenTelemetry eBPF profiler, operate at the kernel level, so they capture raw program counters: memory addresses pointing into your binary.

Chris Watts is Head of Enterprise Engineering at OpenRouter, building infrastructure for AI applications. Previously at Amazon and a startup founder. As large language models become core infrastructure for more and more applications, teams are discovering a familiar challenge in a new context: you can't improve what you can't see.

Building AI services with large language models and agentic frameworks often means running complex microservices on Kubernetes. Observability is vital, but instrumenting every pod in a distributed system can quickly become a maintenance nightmare. OpenLIT Operator solves this problem by automatically injecting OpenTelemetry instrumentation into your AI workloads—no code changes or image rebuilds required.

Large language models don’t work in a vacuum. They often rely on Model Context Protocol (MCP) servers to fetch additional context from external tools or data sources. MCP provides a standard way for AI agents to talk to tool servers, but this extra layer introduces complexity. Without visibility, an MCP server becomes a black box: you send a request and hope a tool answers. When something breaks, it’s hard to tell if the agent, the server or the downstream API failed.