Accelerate AI Maturity with a Multi-Cloud Approach to AIOps

Kevin Cochrane, the CMO of Vultr, explains how companies can accelerate their AI maturity by adopting a multi-cloud approach to their AIOps initiatives. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

“Insanity is doing the same thing over and over again and expecting different results.” This famous quote is often attributed to Albert Einstein, although its true origin is unknown. Regardless, it is a fitting analogy for IT leaders navigating the rapid advancements in AI innovation. The AI industry is accelerating at a breakneck pace, with companies making significant investments (and bets) to transform their industries and unlock new revenue streams.

However, the key to realizing AI Maturity and achieving success and ROI lies in the underlying infrastructure that supports these advancements and operations. For IT leaders, that means taking a hard look at their current infrastructure and not assuming what has worked in the past will work in the future with AI.

Enterprises that overlook the significance of AI Operations (AIOps) risk falling behind. Without continuous and thorough evaluation of their AIOps strategy, companies may miss out on critical market advantages and be surpassed by competitors who adapt more swiftly to AI advancements.

A crucial component for enterprises is a multi-cloud approach, which involves utilizing multiple cloud providers to address diverse needs within AIOps. As enterprises expand their AI initiatives, each element has unique requirements, varying workloads, and specific demands. By implementing a multi-cloud approach to AIOps, organizations can choose the optimal provider for each workload, enabling cost-effective scaling of AI initiatives and maximizing both performance and cost efficiency.

Multi-Cloud Fosters AI Maturity

According to a recent S&P Global research report commissioned by Vultr, enterprises pursuing AI maturity have, on average, 159 models in production concurrently. As they mature, they will increase the number of AI models in production concurrently and move more AI workloads to edge environments. To scale an AI program with many moving parts and is geographically distributed, enterprises need a multi-cloud approach that ensures adequate geographic coverage and availability of the right cloud infrastructure components to support AI inference in edge locations.

Sourcing infrastructure, tools, and services from numerous cloud providers can deliver the breadth and depth that enterprises need to scale AI across distributed geographies and marketplaces. Doing so can also build in the right level of redundancy to ensure that global AI operations will continue in the face of unexpected developments.

Managing such a robust program is no small task. Enterprise leadership should commit to developing a platform engineering solution that is purpose-built for AIOps to make AI orchestration operationally feasible. Such a solution can also enable the management of multiple cloud environments from a single user interface, an essential aspect of mature AIOps. There can also be challenges around data integration across clouds and governance and compliance issues to manage when splitting workloads across different cloud providers. However, accounting for these requirements in the design of a platform engineering solution can minimize these potential headaches.

Mature AI also requires that enterprises embrace the foundational principles and practices of responsible AI, including end-to-end observability of the full AI model lifecycle and robust data governance. When integrated into the platform engineering solution, these capabilities can be effectively managed across different cloud environments. The mantra within the enterprise should be “The best cloud environment for each AI workload.”

Benefits of Multi-Cloud

While a multi-cloud approach undoubtedly adds a level of complexity to AIOps workflows, its numerous benefits outweigh the challenges:

Flexibility

AI workloads can vary greatly for every company and every project. For example, training large language models requires significantly more power and storage than sentiment analysis. A multi-cloud strategy allows enterprises to choose the best provider for each specific task, much like selecting the right vehicle for different needs—a scooter for navigating short distances in congested areas, a car for picking up groceries, and a large truck for moving into a new house. This approach optimizes performance and efficiency by leveraging each platform’s unique strengths.

Cost Control and Flexibility

As enterprises scale their AI initiatives, cost management becomes crucial. Relying on a single cloud provider can lead to vendor lock-in and rising costs with little control. A multi-cloud strategy can mitigate that risk, particularly when infrastructure buyers seek out cloud providers that embrace composability, enabling enterprises to choose the most cost-effective provider for each task. This flexibility allows enterprises to adapt to changing business conditions. This helps them maintain a competitive edge and innovate while avoiding unnecessary expenses. Failure to select the right cloud provider for each specific AI workload can lead to paying for unnecessary services, causing AI-related costs to spiral out of control.

Global Resiliency

A multi-cloud approach enhances risk mitigation and elevates resilience, flexibility, and scalability on a global scale. This is particularly important in a world rife with extreme weather, natural disasters, political unrest, and other threats. Multi-cloud supports the modularity of AI operations, streamlining development, deployment, and ongoing optimization to maintain business continuity. This is particularly important for managing platform engineering solutions as engineering teams progress toward AI maturity.

Multi-Cloud Positions Enterprises for Ongoing Success

When implemented strategically, multi-cloud benefits outweigh the challenges. Multi-cloud provides a flexible, scalable framework that supports diverse AIOps needs, optimizing performance and minimizing costs globally. This strategic approach can also drive significant innovation and enhance ROI. As market pressures continue to seek a faster return on AI investments, having a multi-cloud strategy can mean the difference between becoming a leader or a laggard in the battle for AI supremacy and proven revenue streams.