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Eight LLM and AI agent observability platforms compared: what each tracks, pricing and free tiers, self-hosting options, and who each is built for. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

I'm investigating repeated errors in my e-commerce application, and I need to get enough context in a single Honeycomb query to piece the entire picture together. Each query returns events based on the event's WHERE clauses, but I want to know several things from outside of the event that recorded an error. Things like: Those attributes are all over the trace. That's going to make a single query tough, right? Wrong!

A critical system has to stay reliable without new failures or added downtime, and live debugging, confirming the root cause without stopping the system, is often the only way to do that. In practice, this means having runtime context: on-demand evidence generated at the point of failure rather than logging configured months earlier, which is what keeps MTBF up, MTTR, and MTTA down.

If you are able to build it better than your vendor, then change your vendor. Not build it. Rishi builds large-scale observability systems at Last9, focusing on reliable and cost-efficient telemetry infrastructure, and writes about the practical lessons learned while operating ClickHouse, VictoriaMetrics, and OpenTelemetry in production.

Observability spend is outgrowing infrastructure budgets. What drives the cost up, how pricing models work, and a practical framework to manage it. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

It's been one year since we issued an AI mandate inside Honeycomb, and we've been doing a lot of reflecting internally on how far we've come and how far we have yet to go.

Sampling is a core skill that everyone who runs an observability pipeline at scale will learn. There are lots of tradeoffs within the various decisions you'll make from reducing bandwidth, CPU, and memory, to reducing costs and making the observability backend's performance better for users. Historically, there have only been three mechanisms, each with their own tradeoffs: However, there is a secret fourth option: adaptive tail sampling—which changes those tradeoffs.