The potential of AI has disrupted the business world almost overnight, with billions upon billions of dollars being invested by companies to enhance their AI maturity levels in a race for market dominance. Countless companies are betting on what AI can deliver for their business, investing in AI Operations (AIOps) to realize the promise of the future.
As many business leaders have noted, it’s better to be early than late. However, in their haste to become market leaders (or simply not fall further behind), many enterprises may risk their futures by not rethinking their AI hosting environment. With the economy and markets in flux, IT leaders have a fiscal and business responsibility to critically assess their approach and adopt a multi-cloud strategy. But when is the right time to do this?
Scenario #1: When you are moving beyond the AI Proof of Concept phase and looking to operationalize AI across your business operations
The rapid acceleration of LLMs (large language models) and the development of AIOps has fundamentally changed the list of considerations and requirements. According to an S&P Global study commissioned by Vultr, enterprises now have an average of 159 models in production concurrently, and that number is expected to grow. As enterprises scale their AIOps, there is an urgent need to address evolving requirements. Companies are rapidly integrating AI into a wide range of business processes to achieve AI maturity and maximize operational efficiency.
Enterprises aiming to support AIOps at scale must partner with cloud providers that have the resources to support mature AIOps. As the aforementioned S&P Global Study indicates, enterprises across industries are choosing cloud over on-premises environments for AI development, training and inference nearly two to one (62% to 38%). The seemingly easy choice for enterprises is to scale their AI initiatives with their current traditional hyperscaler, as they can manage the loads. But is that really the best choice?
Scenario #2: When you are evaluating your incumbent cloud provider’s ability to support your plans for AI at scale
A one-size-fits-all approach is great for hats, but it’s terrible for cloud providers. Different cloud providers have different strengths and pricing structures. Enterprises need a hybrid-cloud, multi-cloud approach for AIOps to ensure they have the flexibility to move workloads to the right-sized, most cost-effective provider.
Failing 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. It’s critically important for performance and cost considerations to embrace a multicloud approach for the AI transformation.
Scenario #3: When you realize that flexibility and cost containment are equally essential to scaling your AI operations
Composability is crucial for maintaining flexibility and efficiency in AI operations. Composability ensures that infrastructure, tools and services are interoperable for the customer’s benefit. Providers that commit to composability offer cloud customers the flexibility to choose the optimal tech stack components, supporting tools and services, and environment for each workload. Composability also offers greater flexibility across all layers of the cloud stack, giving enterprises the agility they need to manage their operations and maintain control over costs as they scale. It gives them the freedom and transparency to pay for what they need and the ability to adapt to dynamic business requirements.
Modularity via composable cloud also empowers organizations to assemble and reassemble cloud stack components without any lock-ins, restrictions, or compromises to performance. By leveraging the modularity of cloud-based solutions, cloud customers can streamline the entire AI model lifecycle, including development, production deployment and continual optimization of AI models.
A multi-cloud approach allows organizations to select best-in-class tools and services from various cloud providers, ensuring access to the most advanced AI and ML capabilities. This strategy also provides the flexibility to rapidly adapt to evolving requirements, a key element within the AI model lifecycle. Different stages—such as data preprocessing, model training, deployment and monitoring—may require specialized tools and infrastructure that are not uniformly available on a single cloud platform. Organizations can utilize the best technology for each phase and optimize performance and efficiency.
Scenario #4: When you are establishing a centralized operating model to support mature AI operations
Enterprises pursuing mature AIOps tend to embrace a centralized operating model that aggregates data science expertise in a center of excellence for initial model development and training, followed by regional deployment of models for fine-tuning and delivering inference in edge environments. This paradigm allows enterprises to reap the benefits of collaboration among their most talented AI engineers while streamlining the AI model lifecycle to support multiple models in production concurrently.
Inference at the edge is a growing trend. According to the S&P Global study, 85% of respondents in all industries say it is either likely or very likely they will conduct more training and inference in edge environments in the future. As more AI inference tasks move to edge environments, enterprises need to prioritize the geographic distribution of AI architecture to ensure their AIOps are optimized for performance and cost at the edge.
An additional benefit of a composable cloud/multi-cloud approach is support for diverse AI workloads. Different cloud providers excel in different capabilities, and relying on a single provider for all AIOps can mean missed opportunities for optimization. For example, some providers may offer superior tools for model development and training, while others might excel in edge computing acceleration or data management. By switching to or leveraging multiple cloud providers, enterprises can benefit from each of their unique strengths to optimize their AIOps. This approach ensures that enterprises have access to the best tools and services available, tailored to their specific needs.
It’s Always the Right Time to Evaluate
IT leaders should continually assess their current AI hosting environment by asking: How are the performance levels? Where are their cost inefficiencies? Where is the internal team struggling with a lack of flexibility? As IT leaders operationalize AI across an organization, the answers to these questions become even more important. This marks a pivotal juncture for many IT leaders. Can your provider(s) support the scale, complexity, and evolving nature of AI workloads, as well as provide the necessary infrastructure and support for seamless AI deployment, scaling, and management? AI operations are fluid, with constantly changing requirements and technological advancements.
As enterprises advance their AI maturity levels, embracing a multi-cloud approach can be a strategic move with significant benefits. By prioritizing flexibility, avoiding vendor lock-in, and optimizing costs, companies can better align their cloud strategy with their evolving business needs. A multi-cloud approach ensures that enterprises can leverage the best tools and services available, driving innovation and maintaining a competitive edge. An open AI ecosystem allows for the integration of best-in-class tools and services from various vendors. This openness and flexibility are key to staying ahead in the rapidly evolving AI landscape and successfully delivering your AI strategy today and well into the future.
