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Home Technology Infrastructure & Platforms Cloud, Virtualization, Operating Environments and Middleware

Your cloud strategy is now your AI strategy, too

Lee Sustar by Lee Sustar
August 15, 2024
Photo by SevenStorm JUHASZIMRUS: https://www.pexels.com/photo/low-angle-photography-of-high-rise-buildings-435895/

Photo by SevenStorm JUHASZIMRUS: https://www.pexels.com/photo/low-angle-photography-of-high-rise-buildings-435895/

Corporate cloud strategies that yesterday centered on the unglamorous transition of legacy apps from out of the data center, along with some new cloud-native apps, must now incorporate transformative technologies with radically different value propositions — all without breaking the IT budget. Public cloud provider AI managed services such as Amazon Bedrock, Azure AI, and Google Cloud’s Vertex have put large language models at the fingertips of both IT teams and business users.

Rise of the AI cloud

My colleague Mike Gualtieri and I wrote The Rise of The AI Cloud, a report that provides guidance on integrating AI into your cloud strategy and that examines the potential and limitations of running AI workloads in the cloud. Certainly, cloud isn’t always the best infrastructure for AI workloads. Why? See Mike’s comprehensive evaluation of the AI infrastructure market and his related blog. But for many users, the accessibility of cloud AI managed services may be the fastest on-ramp.

Our report on the AI cloud isn’t an evaluation or ranking of any particular cloud provider. Rather, it’s an overview of the key considerations that cloud customers should have as they sort out which cloud offerings fit and which don’t — and how this can change market dynamics. AI workload requirements may align well with your current cloud strategy in some cases; in others, they may collide.

For starters: Does the cloud provider actually have the GPU resources that are needed to meet an organisation’s goals? Is cloud providers’ custom AI-oriented silicon — currently on the market from AWS and Google and announced by Microsoft — good enough, or is NVIDIA the must-have choice? Are NVIDIA-rich upstarts like CoreWeave and Vultr viable alternatives? Or does Oracle Cloud Infrastructure’s SuperCluster have a place in the mix?

AI strategy

The answers to the above questions will differ based on an organisation’s priorities and AI strategy. In some cases, AI capabilities may come built in by providers that leverage hyperscalers as a platform, such as Salesforce Hyperforce or Snowflake Data Cloud. In each case, successful delivery of AI infrastructure requires maximising performance for a range of AI workloads, including data preparation, model training, and inferencing.

The explosion of easy-to-use cloud AI options and the pay-as-you-go options have been alluring to cloud customers, if cloud provider earnings reports are any indication. But just because it’s easy to get going with an AI cloud doesn’t mean that it’s the best choice for AI infrastructure in every case. Cost and complexity can quickly mount as users wrestle with data and optimise inferencing, which can lead to greater spending then anticipated. Our report explains why certain core AI infrastructure capabilities can make cloud an awkward and pricey fit in many scenarios. That’s a big reason why some major cloud providers are backing an open-source effort to optimise Kubernetes for AI/ML workloads.

Whichever approach users take to AI/ML, it’s bound to reshape cloud strategy, either by embracing it fully, using it selectively, or by avoiding cloud AI managed services in favor of on-premises alternatives.

Originally posted in Forrester

Related:  IDC outlines the competitive landscape for AI in 2021
Tags: cloud strategyForresterIT strategy
Lee Sustar

Lee Sustar

Sustar serves IT infrastructure and operations professionals. As principal analyst for Forrester, his research focuses on public cloud, containers, modernization, and the wider transition to cloud-native computing and practices. He assists enterprises in creating pragmatic cloud strategies and developing execution roadmaps. Most notably, he seeks to reflect organizational context (e.g., industry, investments, regulations, skills, location) in these deliverables. Sustar’s strong background in both financial services cloud/container strategies and governance and risk gives him a unique perspective. Key markets that Lee covers include multicloud container development platforms, public cloud enterprise container platforms, and public cloud infrastructure and development platforms. Previous Work Experience Sustar has more than two decades of experience in information technology, cybersecurity, and cyber risk management. Prior to joining Forrester, Lee worked as independent security officer at the Federal Home Loan Bank of Chicago, a $100 billion government-sponsored enterprise, where he had oversight of logical and physical security controls, served on the bank’s cloud center of excellence (CCoE), and reported on cyber risk to the executive team and board of directors. Previously, Sustar worked at Discover Financial Services as a member of the infrastructure architecture team leading public cloud deployment and, later, on the cybersecurity architecture team, where he helped develop container security architecture and led projects to onboard several security tools. He has also served as a technical writer for leading technology vendors, including Microsoft, IBM, Oracle, Cisco, AT&T, and Dell. As a longtime technology and cybersecurity journalist, Sustar has been published in SC Magazine, Computerworld, PC Week, Communications Week, and other tech publications. Education Sustar earned a Bachelor of Arts degree in American studies from Northwestern University. He holds the CISSP certificate from (ISC)2 and the GSEC certificate from SANS.

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