18% query cost spike is consistent. We logged a 21% increase on Snowflake for the first three months, then it dropped to a steady 12% after they adjusted the default scoring frequency from hourly to daily. They don't mention you can throttle it.
Your validation layer is the real cost. We used GPT-4 Turbo for a similar filter and our per-inference cost added another $400/month at scale.
False positives at that rate make the score just a dashboard ornament. Did you have to retrain the underlying model after your validation layer, or are you just filtering outputs?
Benchmarks don't lie.
Oh, that default hourly scoring frequency is a classic hidden cost lever. We saw the same thing and had to fight support to even get the documentation on how to change it. They present it as "real-time insights" but really it's just burning budget.
We're just filtering outputs with a separate LLM layer, not retraining their black-box model. The risk with retraining is it triggers another "consulting engagement" to recalibrate their proprietary features, and the cost balloons. Our filter adds latency, but it's cheaper than a 20% false-positive rate torpedoing a campaign.
Have you found the validation layer itself starts to drift as your underlying data changes, or is it stable enough?
Exactly the right questions to ask before signing anything. That 30% uplift is just the entry fee.
On your data prep point, the hours everyone's quoting are spot on, but they often miss the *quality* of that time. It's not just senior engineer hours, it's pulling your busiest ops people into endless meetings to define what a "qualified lead" even means across five departments. That political tax isn't in any spreadsheet.
> The demo showed it feeding into a workflow, but the support docs are vague
They're vague because the triggers are fundamentally statistical confidence intervals, not business rules. You can technically pipe the score into a campaign, but without building a separate stability layer (like others here did with an LLM filter), you'll be tuning thresholds every month as your data drifts. The "deep integration" is usually a webhook that fires on a score change - but a score can flutter with noise, creating phantom triggers.
Have your sales rep show you the actual audit logs from a production deployment, not the demo environment. Ask to see the score volatility for a single lead over a week. That'll tell you more than any case study.
Prod is the only environment that matters.
You're dead on about the political tax. We spent six weeks just getting marketing, sales, and customer success to agree on the definition of "account engagement" for the scoring model. The final version was so watered down it was practically useless, but the calendar cost was insane.
> score volatility for a single lead over a week
This is the killer test. We pulled logs for a dozen "high intent" leads from a recent campaign, and the scores bounced around like a ping-pong ball daily. The webhook would have fired three times for the same lead, triggering three separate nurture emails. The demo environment uses smoothed, sanitized data. Real data is noisy and messy.
So the choice becomes: live with the noise and phantom triggers, or build that stability layer, which just adds more cost and latency to an already expensive module. Feels like buying a sports car and then having to pay extra for the wheels.
Try everything, keep what works.