So we finally jumped on the "AI-powered insights" bandwagon, letting the new Looker feature auto-generate a set of exploratory dashboards from our product data. The promise was self-service, democratized analytics. The reality is our monthly Looker bill just landed and it's roughly 2.1x last month's.
I'm not shocked, but I am annoyed. The sales pitch was all about empowering business users, not about the meter running twice as fast. We're on a consumption-based plan, and it seems our friendly neighborhood AI has the query appetite of a starving intern.
Has anyone else been through this autopsy? I'm trying to piece together where the cost actually came from. Our best guesses:
* **Query volume explosion:** Each "smart" dashboard isn't a static view. It's generating a dozen underlying queries to find its "insights," and they refresh on open. A team of 50 opening these things daily adds up fast.
* **Model complexity tax:** The AI seems to favor complex joins and derived tables from our LookML that we normally avoid for frequent queries. It's picking the "interesting" path, not the efficient one.
* **The hidden training rounds:** I have a suspicion that the initial generation and subsequent "re-optimization" of these dashboards burned a ton of background compute that got billed to us. No one mentioned that part.
Before I go and turn the whole feature off, I'm curious if this is a common pattern now. Did you find specific settings to leash the AI's spending habits, or is the only viable move to scrap the auto-generated content and manually rebuild the useful bits? The whole point was to save analyst time, but not if it just converts that saved time directly into a larger invoice.
Your breakdown is on point. I've seen this pattern with other clients adding "smart" features. The initial generation phase you suspect is real, but it's often not a one-time cost.
Many of these systems run periodic background queries to revalidate or update their suggestions, which keeps the meter running. You should audit your query logs for scheduled refreshes or look for any "insight freshness" settings that might be enabled by default.
The model complexity issue is the real budget killer. AI will optimize for statistical significance, not query performance. It'll join every dimension available if it thinks there's a correlation. You might need to create a separate, simplified explore specifically for AI-generated content to enforce guardrails.
independent eye
You're right about the periodic queries, but calling them "background refreshes" is too kind. In my last Looker migration, the AI scheduler didn't just run a refresh, it re-ran the entire multi-join exploratory analysis on a six-hour cron, treating it like a fact table update. It was a silent budget hemorrhage.
That simplified explore you mentioned is the only real fix. You have to build a semantic cage for it - a star schema with pre-aggregated facts, no more than three allowed joins, and all the interesting dimensions hidden away. The AI will hate it, because its "insights" will be boring and obvious. That's the point.
The real joke is you'll spend more engineering hours building that cage than you ever saved with the auto-generated dashboards.
Expect the unexpected