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Dev tools · August 5, 2025

RF

Rotel – Fast and Efficient OpenTelemetry Collection in Rust

Project Page: https://rotel.dev Github: https://github.com/streamfold/rotel Hi HN, Ray and Mike here! We’re building Rotel, a new high performance, resource efficient approach to OpenTelemetry collection. Rotel is open source (Apache License 2.0) and runs as a standalone process and collects telemetry from external processes or other collection agents. It consumes 75% less memory and 50% less CPU in benchmarks https://streamfold.github.io/rotel-otel-loadtests/benchmarks, so it is particularly well-suited for environments where resource…

What it does

In the maker’s words, at launch

Project Page: https://rotel.dev Github: https://github.com/streamfold/rotel Hi HN, Ray and Mike here! We’re building Rotel, a new high performance, resource efficient approach to OpenTelemetry collection. Rotel is open source (Apache License 2.0) and runs as a standalone process and collects telemetry from external processes or other collection agents. It consumes 75% less memory and 50% less CPU in benchmarks https://streamfold.github.io/rotel-otel-loadtests/benchmarks, so it is particularly well-suited for environments where resource optimization is paramount. Mike and I love working on Observability solutions. We’ve built and optimized telemetry data planes for products such as Librato, Papertrail and AppOptics. As we’ve scaled high volume data processing systems over the years, we’ve often found ourselves under pressure to improve SaaS margins or the resilience of complex distributed systems. This has led us to the same question again and again: How can we do this more reliably and efficiently? We believe telemetry collection demands high levels of efficiency, and Rotel is our first step toward addressing that need. Rotel’s Lambda extension https://github.com/streamfold/rotel-lambda-extension has already unlocked the promise of OpenTelemetry in production serverless environments, where resource constraints previously made this nearly impossible. With Rotel we’re also rethinking DX for OpenTelemetry collection. Its lightweight footprint allows for packaging directly in runtimes like Python and Node.js, eliminating the need for additional configuration and deployment of sidecars. Rotel’s Python Processor SDK enables you to build telemetry processors right from your favorite IDE with the ease and expressiveness of Python and the performance of Rust. Getting started with Rotel is easy using prebuilt Docker containers, just run… `docker run -ti -p 4317-4318:4317-4318 streamfold/rotel --debug-log traces --exporter blackhole` to start Rotel listening for OTLP traffic on ports 4317 & 4318. Check out the Getting Started guide for more install and config instructions: https://rotel.dev/docs/setup/getting-started. We’re super excited to share Rotel with you and would love to hear what you think!

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