K80s are 2014 and people still use them at scale. So 3 years isn't unreasonable, right? A major drawback of having this sort of hardware on-prem (and this uni

K80s are 2014 and people still use them at scale. So 3 years isn't unreasonable, right? A major drawback of having this sort of hardware on-prem (and this unit in particular) is that it doesn't include local storage for a 100TB+ scale dataset. There's not just the admin cost of hard drives and RAID, but there's the infra cost of synchronizing data to the machine. An average, reasonable, product-oriented Engineering Manager could easily choose cloud over a $100k savings if it means less headaches. Especially since GCloud will give big discounts if they're trying to outbid Amazon. On the other hand, if Lambda offered some sort of NAS solution plus caching software (Alluxio! :), that would make the offering a lot more competitive. Oh and maybe throw in a network engineer to set up peering and such.. ;) What might make the comparison more compelling is to take anonymized workloads from your customers, examine how often the workload results in idle (wasted) time, and to factor that into the figures. If a user's workload is elastic (many are, especially in R&D), then the user would be drawn towards ML Engine, Sagemaker, Floydhub, Snark, etc... The downside to these services is that they almost always involve expensive copying of data from NAS to worker machines. But if user utilization is high, and the dataset is static, the on-prem machine is a prime investment. While the savings is a big and noteworthy number, iteration speed is of principle concern to readers here and likely a good segment of your customers. I love the Lambda offerings, but there are a lot of deep learning hackers who can't make the most of bare metal. The 1080TI server is probably the killer solution at 1/10th the cost. But NVidia legal blah blah blah :(