AI Data Delivery: The Key to Scaling Reliable Production Workloads

When enterprises move AI workloads from pilot to production, data delivery often becomes the factor that determines whether those systems can scale reliably. Point-to-point architectures connecting storage directly to compute hold up under demonstration conditions, but they often break down under sustained, concurrent production traffic. The result is stalled inference pipelines, delayed RAG systems, underutilized GPUs, and SLA violations, all of which carry direct business consequences.

"Organizations successfully operationalize AI when their infrastructure is built to handle real-world failures, not just controlled conditions," says Hunter Smit, senior manager of product marketing at F5. Production traffic exposes architectural weaknesses. In a pilot, a stalled transfer is an inconvenience, while in production, that same stall is an outage someone now owns.

The underlying architecture is often identical in both cases: when a client is wired directly to storage, the system becomes increasingly fragile under sustained, concurrent production traffic because that direct connection has no answer when a node fails or traffic spikes. "Point-to-point architectures, where the S3 client connects directly to S3 storage, are not resilient," says Paul Pindell, principal solutions architect for technology alliances at F5. "If a single storage node fails, all traffic to that cluster degrades, and in some cases the cluster can fail entirely." The problem is that AI workflows, including RAG-based inference and agentic AI, increasingly treat S3 storage as a first-class citizen in the AI cluster.

However, the network connectivity between that storage and the cluster was never designed for the high-throughput, uninterrupted data movement that's needed to keep GPUs running optimally. The real cost of stalled pipelines and underutilized GPUs "Enterprise leaders tend to frame AI infrastructure around GPU utilization, but what makes AI different from traditional deterministic workloads is that infrastructure continuously influences those outcomes at every interaction," says Tanu Mutreja, senior director of product management at F5. "In AI environments, infrastructure is no longer just a back-end concern.

It shapes customer experience, quality, resilience, and cost with every transaction." There can be significant business consequences. For instance, when inference pipelines stall, it becomes an SLA and customer experience issue. When RAG systems are delayed, models lose access to timely, relevant context, which results in inaccurate, outdated, or hallucinated responses, all of which create operational, compliance, and reputational risks.

At the same time, the infrastructure issues that create those problems can also drive up costs by leaving expensive GPU resources idle or underutilized. "When GPUs are underutilized, it signals infrastructure inefficiencies that inflate costs while limiting scalability and responsiveness," Mutreja says. "The leadership question is whether the end-to-end AI infrastructure consistently delivers reliable, secure, high-quality, and governed AI experiences at sustainable unit economics." Building a production-ready data delivery layer F5 treats data delivery as a first-class infrastructure layer rather than assuming the network path will simply work.