I work at Google and was on the bigquery/dremel team. I've written on this topic in the past at [0]. Turns out analytic SQL workloads are incredibly volatile -

I work at Google and was on the bigquery/dremel team. I've written on this topic in the past at [0]. Turns out analytic SQL workloads are incredibly volatile - we know from operating these big ol Dremel/bq clusters in a multi tenant fashion. In my opinion boulos is correct to point out that the above analysis sets volatility of usage at 0, which is not representing the real world.. Think about it another way. Bigquery on demand pricing only charges you for the resources you use, rather than provision. This guarantees you don't pay for the luxury of deployed resources that sit idle. By definition this also means your "cluster utilization" is 100% with bigquery. It's rare to see provisioned databases get above 50%, even in cloud. Periscope data specifically called out running redshift at 50% utilization as a best practice - in other words it's a best practice to double your bill just for deployment reasons. There is, of course, bigquery flat rate pricing for larger use cases, which is incredibly cost competitive. Folks who migrate to bigquery also specifically call out cost as a major benefit. Some sites that call this out are kabam[1], sharethis[2], Yahoo [3], ny times[3], Motorola[4]. This logic is extrapolatable to any cloud pay-per-use pricing schemas (like queuing, storage, API calls, etc). It's just that in analytics usage tends to be incredibly volatile, and thus this is extra significant. Happy to chat further. [0] https://cloud.google.com/blog/big-data/2016/02/visualizing-the-mechanics-of-on-demand-pricing-in-big-data-technologies https://cloud.google.com/blog/big-data/2016/02/visualizing-t... [1]https://youtu.be/6Nv18xmJirs https://youtu.be/6Nv18xmJirs [2]https://cloud.google.com/customers/sharethis/ https://cloud.google.com/customers/sharethis/ [3]https://youtu.be/TLpfGaYWshw https://youtu.be/TLpfGaYWshw [4]https://cloud.google.com/blog/big-data/2016/07/supersize-it-how-motorola-transformed-its-data-warehousing-and-analytics-with-google-cloud-platform https://cloud.google.com/blog/big-data/2016/07/supersize-it-...