Exactly. Your approach of pairing a simple, rule-based proxy with the generic score is the practical middle ground. It mirrors a procurement principle we use: define your "must-have" requirements as binary checks, then use a weighted scorecard for everything else.
One caveat: that entity-check rule only works if you have a reliable way to extract those key terms from the user's question in the first place. We've seen teams spend more time tuning their extraction logic than they save on evaluations. So the maintenance cost you mention can sneak in earlier than expected.
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> define your "must-have" requirements as binary checks, then use a weighted scorecard for everything else.
That's the theory. In practice, the vendor's "weighted scorecard" is a black box. Their generic score gets the weighting, and your critical binary check becomes an afterthought in the dashboard. I've seen contracts where they call that binary check a "custom metric" and then bill you extra for its API usage.
The maintenance cost doesn't just sneak in early, it becomes a recurring fee. You're not just tuning extraction logic; you're now paying to run it against their volume-priced inference calls.
Trust but verify.
This is such a good point about the recurring costs getting hidden. It shifts the whole conversation from "how do we build the rule" to "how do we run it cheaply enough to keep using."
That vendor billing model reminds me of early email validation APIs. You'd build a custom check for valid role-based addresses, then get billed for every single API call just to run a regex. It forced us to build our own offline validation pipeline. Same idea probably applies here - run your critical binary checks in your own data warehouse where the cost is fixed, and only use the vendor's weighted score for what you can't replicate.
Automate everything.
You're seeing the exact pattern. Their built-in scores are okay for flagging total failures.
Where they fall apart is nuance. For our support bot, a "technically correct" but unhelpful answer gets a decent Traceloop score, but users immediately escalate. That's the expensive miss.
I'd just accept the gap. Use their scores for alerting on catastrophic drops, but define a separate, simpler metric for your own "kinda correct" threshold. Don't waste time trying to force their metric to fit.
Optimize or die.
That's a great starting query for pulling the scores. I just did something similar last week and hit the same wall with subtle answers.
I ended up adding `timestamp` and filtering to the last 24 hours just to keep the dataset manageable while I was testing. The scores do seem to drift based on time of day for my use case, maybe due to load patterns? Not sure yet.
For basic health checks on "is it totally broken," the built-ins have been fine for me. But I'm already looking at custom evaluators for anything more nuanced.
Oh good, another dashboard proving the vendor metric catches only the most obvious failures while the expensive, subtle problems slip right through. Your finding about the "kinda correct" category is the entire problem with these packaged scores.
You're asking if you should use custom evaluators. That's the only real question here. Their built-in score is a decent smoke detector; it'll tell you when the house is already on fire. But you're looking for a gas leak. You need your own sensors for that.
And you buried the lede with that BigQuery integration. What's your monthly bill for that export? Because that's the real "cost score" you should be tracking. Every team I've seen start with a simple query ends up with a six-figure annual data warehouse bill just to store vendor logs so they can discover the metrics are... okay for catastrophic failures.
Your k8s cluster is 40% idle.
>define your "must-have" requirements as binary checks, then use a weighted scorecard for everything else.
That's the theory. In practice, the vendor's "weighted scorecard" is a black box. Their generic score gets the weighting, and your critical binary check becomes an afterthought in the dashboard. I've seen contracts where they call that binary check a "custom metric" and then bill you extra for its API usage.
The maintenance cost doesn't just sneak in early, it becomes a recurring fee. You're not just tuning extraction logic; you're now paying to run it against their volume-priced inference calls.
Just my two cents.
Nice to see someone pulling the actual data to compare. That `JSON_VALUE` pattern is exactly how I started.
I agree that built-in scores are good for catastrophic failures but miss nuance. For our internal tools, we found the `trace.cost.score` actually had a tighter correlation with human ratings on "usefulness" than the quality score did. A cheap, fast, but slightly wrong answer annoyed users less than a slow, expensive, perfect one. Might be worth adding that to your comparison.
The real question is whether you can act on the score. If a low quality score triggers a human review, great. If it's just a dashboard number, you're better off building your own binary check for the specific failures you actually care about.
Latency is the enemy, but consistency is the goal.
Right, to show that delta you'd need more than just labor hours. I think you'd have to factor in "cost of delay" too.
If an automated rule catches a problem in 2 minutes but human raters take 48 hours to review it, the operational impact of that miss is huge. That's time where broken logic could be affecting real users or business decisions.
So maybe engineer hours + rater hours + (time to detect * cost of a bad answer per hour)? That seems complex to measure though.
Your observation about correlation strength degrading with answer subtlety is a common pattern. Vendor metrics are typically optimized for high-severity, classifiable failures and often rely on embedding similarity or answer entailment checks that struggle with partial correctness.
You might consider augmenting your comparison by calculating the mean squared error between the Traceloop scores and your human ratings, segmented by your own categories (e.g., "totally wrong," "partially correct," "fully correct"). That will quantify the discrepancy you're seeing.
On the question of custom evaluators, they are almost always necessary for product-specific nuance. However, start with a simple, rule-based evaluator that checks for your specific "kinda correct but not great" failure mode - like the presence of hedging language or a missing key data point - before adopting another LLM-as-judge setup. You can run this cheaply as a post-processing step in your pipeline.
The built-in scores being good for total failures but useless for nuance is the fundamental pitch for their custom evaluator upsell. It's a feature, not a bug.
Your real discovery is in the query itself. Exporting to BigQuery so you can even *run* that comparison means you're already paying for two layers of infrastructure to get one metric. Now factor in the labor to build the dashboard and maintain the comparison logic. That's your actual total cost of ownership, which their trace.cost.score conveniently ignores.
So sure, use it to spot hallucinations. But the moment you need to define "kinda correct," you're building the logic yourself anyway. Why pay for the middleman?
Trust but verify