SigNoz Telemetry Profiler: Cutting Storage by 95%

SigNoz's telemetry profiler reduced span storage by 95%, from 13,404 to 672, while preserving all 31 injected error traces, demonstrating that aggressive sampling of high-volume spans can slash storage costs without losing diagnostic visibility.

SigNoz Telemetry Profiler: Cutting Storage by 95% The results were honestly embarrassing. By replaying identical loads, I watched span storage plummet from 13,404 down to 672. That is a 95% reduction in storage overhead without losing a single one of the 31 injected error traces. It turns out the database was just hoarding useless data like a digital packrat. If you're trying to optimize your AI workflow or LLM agent monitoring, you know that telemetry can explode faster than your actual application logs. This is basically a real-world deep dive into why "collect everything" is a terrible strategy for your wallet. For those wanting to implement a similar cost-reduction deployment: 1. Identify High-Volume Spans: Find the endpoints that trigger the most telemetry but provide the least diagnostic value. 2. Sample Aggressively: Keep 100% of errors but slash the sampling rate for "200 OK" health checks. 3. Profile and Replay: Use a tool to replay traffic and verify that your storage footprint drops while your visibility into crashes stays intact. It's a practical tutorial in common sense: stop paying for data that you'll never actually look at during an outage. Next iPhone Photography: Why Mobile Tech Changed the Lens → /en/threads/3554/