Hey everyone! 👋 I've been deep in Traceloop for a few months now, and while I love the visibility it gives into our AI calls, our bill was starting to creep up as we scaled. Our biggest cost driver? A handful of high-volume, low-complexity agents that didn't really need 100% tracing.
After some testing with our RevOps team, we found a way to cut our monthly spend by about 35% without losing the insights we actually need.
The key is **sampling**. You don't have to trace every single call for every single agent. Traceloop's filters and sampling rates are perfect for this.
Here’s what we did:
* **Identified the "noise."** We looked at our trace volume and pinpointed 3-4 agents that handle simple, repetitive tasks (think: FAQ bots, simple routing agents). These accounted for ~60% of our trace volume but maybe 10% of our debugging needs.
* **Set up targeted sampling rules.** Instead of a global sampling rate, we used Traceloop's configuration to sample these specific high-volume agents. For example:
* Agent: `faq_retrieval_bot` → Sampling Rate: 10%
* Agent: `email_router` → Sampling Rate: 5%
* **Kept full tracing on critical paths.** Our complex sales assistant and negotiation analyzer agents remained at 100%. This is where we need the full fidelity for debugging and improvement.
The result? A massive drop in ingested traces (and cost) from the noisy agents, while we still get a representative sample to monitor for regressions. We still get 100% of the traces from the agents where it matters most.
If you're on a growth plan and watching costs, I highly recommend auditing your trace distribution. A few targeted sampling rules can make a huge difference.
Has anyone else tried a similar approach? Curious if you used different criteria for choosing which agents to sample.
— Dan
spreadsheet ninja
Sampling is the right idea, but starting with agent-level rules is putting the cart before the horse. The first filter should be based on *user* or *session*.
If you have a logged-in production user, trace it. If it's an anonymous visitor hitting your public FAQ bot for the tenth time today, drop the sampling rate to 1% or even 0.1%. You'll still catch outlier failure patterns, but you won't pay for every single repetitive interaction.
The critical nuance is that a "low-complexity" agent can still be part of a critical user journey, so your filter logic needs to account for context, not just agent name.