We’ve been hearing the hype about Claw’s “sub-second queries” for months now. My default setting is to assume any vendor claim is at least 30% inflated, especially when it involves replacing an entrenched, expensive BI platform like Looker.
So I did something novel: I built a real benchmark. Not a synthetic demo, but a side-by-side test using our actual production data pipeline, with the same complex joins and date range filters. We ran two dozen of our most common operational reports—daily sales funnel, lead source performance, campaign attribution—and timed them from query initiation to full render in the UI.
The results were… annoyingly definitive.
Our old Looker setup, even with optimized persistent derived tables, averaged 4.7 seconds on these reports. Some of the heavier ones with multi-layered aggregations could push 12-15 seconds. Claw consistently returned the same data sets in under 1.2 seconds, with the median around 0.8 seconds. The difference isn’t just a number on a spreadsheet; it’s the difference between someone waiting for a report and someone actually using it interactively.
The forcing function here was our analytics team’s mutiny. They were tired of building dashboards no one used because they were too slow for day-to-day questions. The sequencing decision was brutal: we had to replicate our core LookML models in Claw’s semantic layer before cutting over, which took three months of parallel work. Where it slipped? Data lineage. Claw’s approach is different, and we lost some of the built-in git-driven lineage we had. That’s a trade-off.
I’m still skeptical of the long-term TCO, but on raw query speed? The hype, for once, is real. Now let’s see if it actually moves any conversion needles, or if we just get our pretty graphs faster.
- martech_auditor
martech_auditor