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OpenTelemetry Collector Configuration for LLM Observability
A complete, annotated OpenTelemetry Collector configuration for LLM observability: receiving traces over OTLP, normalizing OpenInference/OpenLLMetry schemas onto GenAI semantic conventions, redacting sensitive prompt and completion content, managing volume without sampling away conversations, exporting to Honeycomb, plus validation steps and a troubleshooting table.
How Adaptive Tail Sampling Works in the OpenTelemetry Collector
A technical deep dive into Honeycomb's adaptive tail sampling processor for the OpenTelemetry Collector: how decisions get made, the samplers available, how thresholds compose with the rest of a sampling pipeline, performance benchmarks, deployment limitations, and how it compares to Refinery.
Bringing the Most Advanced Sampling to the OpenTelemetry Collector
Honeycomb is donating its adaptive tail sampling processor, built on years of Refinery experience, to the OpenTelemetry Collector. See how adaptive sampling, trace fingerprinting, and sample rate attribution work, and how to try it today with the Honeycomb Collector Distribution.
Taming Log Noise With the OpenTelemetry Collector's Drain Processor
Do you receive 50 million log lines per day and struggle to see what actually matters? Health checks, heartbeat pings, connection pool messages—they all drown out the errors and anomalies you're trying to find. What if the Collector could figure out the patterns for you?
Managing OpenTelemetry Semantic Convention Migrations With the Collector
This is what a semantic convention migration looks like in practice: not a clean cutover, but months of coexistence where old and new attribute names overlap. In this post, I'll explain why this happens, how the OpenTelemetry Collector's schema processor is designed to automate migrations in both directions, and what we're actively working on to get it into a state where everyone can use it.
