Intelligent CIO asked Patrick Smith, Field CTO EMEA, Pure Storage, if businesses that avoid the power of multi-cloud will struggle to catch up. Here is his response:
We’re seeing that pretty much all of the large enterprises are deploying multi cloud today often – whether they know it or not. So we’re seeing large enterprises certainly starting off with on-prem cloud, where the next step beyond that is typically the move to public cloud.
But beyond that we find that people are not just using one public cloud, they’re looking at more than one public cloud provider. Often they go into that looking at arbitrage between public clouds but actually it’s about risk mitigation rather than financial upcharge.
So today often people are using multi cloud whether they know it or not; whether they’re simply on-prem and SaaS or whether they’re on- prem, public and SaaS.
People also talk about hybrid cloud, on-prem and public. We view it genuinely as multi cloud and we think most of our customers are moving in that direction.
The other thing I think that’s worth saying is as we look at the public cloud, it’s not one size fits all. We’re seeing people moving workloads into the public cloud and also coming back out of it for workloads that aren’t optimal in the public cloud.
So if you look at our product set, one of the products that we’re talking about a lot is our AI ready infrastructure which is a joint development with Nvidia.
It combines Nvidia’s DGX-1 AI compute servers with our FlashBlade unstructured data storage and high speed networking and a preconfigured software stack that really provides rapid time to market for AI.
Our view is that the public cloud for AI provides a great proof of concept proving ground.
But as soon as people go beyond proof of concept with the data volumes involved, the amount of processing involved in AI workloads means that they’ll reach a critical mass and bring that capability back on-prem.
What we are seeing is as part of an AI infrastructure is people want to keep it as busy as possible. People want to be processing and keeping those and Nvidia compute servers busy 100% of the day 24/7 365 days a year.
And to be able to keep AI servers busy you need really fast data processing and data throughput to the servers. This is where FlashBlade comes in – with its parallel low latency performance that scales all the way through the A.I. lifecycle be it initial data capture and the properties associated with the initial data capture through data cleansing and then A.I. training. This has a completely different workflow characteristic to the initial data capture.
So FlashBlade’s multi-dimensional performance covers all of the sequential right type of access together with the pure random read access that you associate with AI training.
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