Presented by F5When 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 weaknessesIn 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. From there, retries and timeouts cascade, and the entire pipeline backs up right at the moment the business is depending on the output.”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, inc …
