Presented by F5
As enterprises move AI workloads from pilot to production, data delivery is often the determining factor in whether these systems can reliably scale. Point-to-point architectures that combine memory directly with compute hold up under demonstration conditions, but they often break down under continuous, parallel production traffic. The result is stalled inference pipelines, delayed RAG systems, underutilized GPUs, and SLA violations, all of which have direct business consequences.
"Organizations successfully deploy AI when their infrastructure is built to handle real-world failures, not just under controlled conditions." says Hunter Smith, senior manager of product marketing at F5.
Production traffic exposes architectural vulnerabilities
In the pilot, the aborted transfer is a nuisance, and in production, it’s a cut that someone now owns that stall. The underlying architecture is often the same in both cases: when the client is directly connected to the warehouse, the system becomes increasingly fragile under continuous, parallel production traffic, as this direct connection becomes unresponsive when a node fails or during traffic spikes. From there, retries and interruptions cascade, and the entire pipeline backs up when the job depends on the product.
"Point-to-point architectures where an S3 client connects directly to S3 storage are not robust," says Paul Pindell, principal solutions architect for technology alliances at F5. "If one storage node fails, all traffic to that cluster is degraded, and in some cases the cluster may fail completely."
The problem is that AI workflows, including RAG-based inference and agent AI, increasingly treat S3 storage as a first-class citizen in an AI cluster. However, the network connection between this memory and the cluster was never designed for the high-throughput, uninterrupted data movement needed to keep GPUs running optimally.
The real cost of stalled pipelines and underutilized GPUs
"Enterprise leaders tend to build their AI infrastructure around GPU usage, but what differentiates AI from traditional deterministic workloads is that the infrastructure continuously influences these outcomes with every interaction." says Tanu Mutreja, senior director of product management at F5. "In AI environments, infrastructure is no longer just a backend. It shapes customer experience, quality, sustainability and value with every transaction."
It can have significant business consequences. For example, when inference pipelines stop, it becomes an SLA and customer experience issue. When RAG systems are delayed, models lose access to timely, relevant context, resulting in inaccurate, out-of-date, or hallucinatory responses, all of which create operational, compliance, and reputational risks. At the same time, the infrastructure problems that create these problems can also increase costs by leaving expensive GPU resources idle or underutilized.
"When GPUs are underutilized, it represents infrastructure inefficiencies that limit scalability and responsiveness, increasing costs." Mutreja says. "The management question is, does the end-to-end AI infrastructure consistently deliver reliable, secure, high-quality, and manageable AI experiences in a sustainable unit economy?"
Establishing a production-ready data delivery level
F5 treats data delivery as a first-class infrastructure layer, rather than assuming that the network path will simply work. While application delivery optimizes the flow of requests between users and applications, data delivery optimizes the flow of data between memory, networks, and compute, including AI computing.
Making data delivery a first-level layer means creating three properties:
Observability provides real-time visibility into latency, throughput, and flow health.
Programmability enables policy-based control over how data moves through dynamic routing, traffic optimization, rate management, and automated failover.
Failure awareness builds resilience against degraded networks, memory throttling, and service interruptions.
in the year architecture F5 is designed for Dell ObjectScaleF5 BIG-IP sits between ObjectScale and AI computing as a programmable control point at the storage edge.
"We’ve seen cases where a misconfiguration in the AI compute layer effectively DDoSed the S3 storage infrastructure. " Pindell says. "Not in a hurtful way, but more like, ‘Oh no, what did I do?’ moment, but still took the memory for the entire organization."
Placing BIG-IP as an application delivery controller between the storage and compute layers protects storage with QoS, rate limits, and connection limits, keeping it robust and operational under such load. Test validated by SecureIQLab This protection doesn’t come at the cost of architecturally significant throughput, Pindell says.
"It is imperative to maintain and even improve transmission capacity," explains. "This allows you to work towards higher levels of functionality, robustness and enhanced security without sacrificing performance to get there."
The added complexity of hybrid and multi-cloud AI
In hybrid multi-cloud environments, AI applications have greater data delivery challenges due to heterogeneity. In other words, data passing through these environments must contend with inconsistent policies, security controls, identity systems, governance requirements, fragmented visibility, and different failure boundaries.
Programmable traffic management and monitoring capabilities solve this complexity together. Observability provides a holistic view of application, network and infrastructure health in otherwise disconnected environments. Programmable traffic management uses these insights to intelligently route, balance, and failover traffic in real-time. Together, they create a closed-loop feedback system that enforces consistent policies, improves resilience in areas of failure, and ensures reliable, high performance. AI data delivery regardless of where applications, data or users are located.
What separates production AI from eternal pilots
Organizations that go beyond regular pilots share a special engineering discipline, Smith says.
"Those who apply to production design, as usual, fail, are no exception." explains. "They will assume there will be delays, congestion and partial outages. Rather than hoping the network will catch up, they build an observable and failure-aware data path with clear mitigation for each degraded state."
Organizations stuck in perpetual pilots still optimize for the perfect lab result and only discover the real-world gap when the workload is active. The issue is not the quality of the model or the number of GPUs, but whether the data delivery layer is designed with the same rigor as the computation.
"Teams must understand that a real-world network behaves very differently than an optimized lab network," Pindell says. "They need a mitigation plan for production failures and performance bottlenecks."
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