As organizations embark on their AI journeys, many are exploring different paths forward. Some are laying the groundwork to implement full-scale AI factories, while others are looking for a faster, simpler way forward—one that leverages ready-made AI models to solve real-world problems instead of dedicating resources to create them from scratch. And while this approach can accelerate time to market, it still introduces infrastructure, data, and security considerations that must be met to ensure the trust, efficiency, and long-term viability of AI investments.
The Importance of a Solid Foundation
Even for organizations that aren’t actively investing in their own infrastructure, success with AI hinges on having the proper footing. A scalable, well-established foundation not only supports modern workload requirements but also prepares the business for future AI growth. In fact, 62% of executives are planning IT infrastructure and data center needs one to three years out due to rising AI demands.¹ As businesses grapple with challenges around data ingestion, scalability, security, and compliance, it’s clear that infrastructure remains the bedrock of successful AI initiatives—at a time when nearly half of AI projects are still failing to deliver expected outcomes.²
Drive Performance
Traffic management is an often overlooked element of AI deployments—supporting the flow of data, queries, and API responses across users, systems, and models. With AI workloads driving significantly higher volumes of network activity than conventional applications, many organizations struggle to keep pace. Without intelligent traffic control, latency, bottlenecks, and inconsistent outputs can degrade performance and user experiences. It can also increase infrastructure costs and introduce data privacy and compliance concerns. High-performance traffic management enables efficient data routing, adaptive load balancing, and real-time observability. This ensures reliable AI operations, faster iterations, and better infrastructure utilization to optimize AI models for maximum efficiency.
Connect Data
Foundational models depend on real-time access to relevant, high-quality data. Without it, outputs run the risk of becoming incomplete, delayed, or misaligned. As 94% of today’s organizations find themselves operating in distributed…
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