SigNoz Telemetry Profiler: Cutting Storage by 95%

Stop guessing how much your observability bills will be and just measure the waste. I put together a telemetry cost profiler for SigNoz because looking at a cloud bill is a great way to get a panic attack, but it doesn't actually tell you which specific spans are burning your money.

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.