Hey everyone, I've been spending the last few days really digging into the new cost estimation dashboard that Traceloop rolled out last week. On the surface, it's an absolutely fantastic addition—finally getting some visibility into the potential LLM spend for our different workflows is exactly the kind of feature we've been asking for.
But I have to say, after running it against some of our actual production traces, the projected costs feel... surprisingly low? Almost optimistic to a fault. We have a fairly complex pipeline that chains a few different models (GPT-4 for analysis, then a cheaper model for formatting, with some Claude for good measure on a different path), and the estimator is spitting out a number that's about 40% lower than what our actual billing has been for a comparable volume.
I'm trying to figure out where the gap might be. I have a few hypotheses:
* Is the estimator maybe not fully accounting for the overhead of the tracing itself? The tokens for the actual prompts and completions are one thing, but there's metadata and all that jazz.
* Could it be smoothing over retries and fallbacks? Our system will retry on certain failures, and that definitely adds up.
* I'm also wondering about context caching. If your system uses it, does the estimator know to reduce costs accordingly, or is it assuming a "cold" run every time?
* What about non-OpenAI/Anthropic providers? We use a couple of lesser-known ones via LiteLLM, and I'm unsure if their pricing schemas are factored in.
Has anyone else done a comparison yet? I'd love to hear if your experience matches mine or if I'm just configuring something incorrectly. The feature itself is a huge step in the right direction for RevOps and forecasting—we desperately need to tie LLM usage back to P&L and specific initiatives—but the accuracy is crucial for building trust and actually using it for budgeting.
Maybe we can crowdsource some findings? If you've run tests, share your setup: which models, average prompt/completion tokens, and the variance you're seeing between projected and actual.
TIL: Even with the best tools, there's no substitute for a good old-fashioned sanity check against the invoice.
Pipeline is king.
Your hypotheses are probably scratching the surface. The real kicker is they're likely using list prices for token counts, not the actual effective rates after your committed use discounts or enterprise agreements.
Also, "not fully accounting for the overhead of the tracing itself" is a good call. That metadata isn't free - it's extra bytes over the wire, stored somewhere, processed. That all lands on a compute instance or a managed service with a per-GB tag. The estimator is probably giving you the clean theoretical cost, not the bill with the vendor's tax added on top.
The 40% delta is a classic move. Make the sticker price look good so you build more dependency, then the real bill arrives.
-- cost first
That 40% delta is a feature, not a bug. It's how you sell monitoring tools.
You're on the right track, but I'd add caching to your list. Are you using any? The estimator probably assumes zero cache hits, which ironically would make the number higher. But if it's ignoring your actual, real-world cache rates entirely, the model is built on a fantasy workload.
Also, ask what input token count they're using for your system prompts. Those are static, but repeated every call. If they're estimating based on a trace that captured the *variable* user prompt but is using a default 500 tokens for the system role, and yours is 2000, the math gets very cheerful very fast.
trust your billing, not the demo.