Operations | Monitoring | ITSM | DevOps | Cloud

Note: Grafana Beyla has been donated to OpenTelemetry under the new project name OpenTelemetry eBPF Instrumentation. Beyla will continue to exist as Grafana Labs’ distribution of the upstream project. Grafana Beyla is an open source eBPF-based auto-instrumentation tool that helps you easily get started with application observability, allowing you to monitor and visualize traces without modifying the application code.

Today, BigPanda released our first-ever research report based on data gathered from our agentic IT operations platform. Our Monitoring and Observability Tool Effectiveness for IT Event Management report provides insights and benchmarks on incident detection and noise reduction for 130 enterprise organizations, including the monitoring and observability data sources integrated with BigPanda.

With grafana/otel-lgtm, observability is just one Docker command away. Over the past year, grafana/otel-lgtm has simplified observability setups, helping developers get a complete OpenTelemetry stack running in under five seconds. With integrations for metrics, logs, traces, and now profiles via Grafana Pyroscope, it has become a go-to solution for demos, development, and testing, as evidenced by its growing community (1k stars on GitHub and growing!) and notable adopters.

As companies adopt more artificial intelligence (AI) to stay competitive and simplify operations, they’re hitting a snag they’ve seen plenty of times before: complexity. Those user-friendly chatbots and impressive predictive models aren’t magic—they run on powerful GPUs like NVIDIA’s and rely on cloud services such as Azure OpenAI or Amazon SageMaker.

An enterprise client reports latency. Your dashboards say everything is fine. They blame you. You blame them. Nobody can prove it either way. This is where most monitoring efforts hit a wall. Too often, the conversation gets stuck on dashboards and tools instead of the one thing that really matters: truth. Observability isn’t about collecting metrics or building pretty dashboards.

LangChain apps are powerful, but they’re not easy to monitor. A single request might pass through an LLM, a vector store, external APIs, and a custom chain of tools. And when something slows down or silently fails, debugging is often guesswork. In one instance, a developer ended up with an unexpected $30,000 OpenAI bill, with no visibility into what triggered it. This blog shows how to avoid that using OpenTelemetry and LangSmith. With this setup, you’ll be able to.