Presented by F5
Scaling AI Requires Robust Data Delivery
Transitioning artificial intelligence (AI) workloads from experimental phases to full production often hinges on the reliability of data delivery. While direct point-to-point connections between storage and compute may suffice for demonstrations, they frequently falter under the sustained, concurrent demands of real-world usage. This inadequacy can lead to stalled inference pipelines, delayed Retrieval-Augmented Generation (RAG) systems, underutilized graphics processing units (GPUs), and ultimately, violations of service level agreements (SLAs), all of which have tangible business repercussions.
“Organizations that successfully operationalize AI are those whose infrastructure is architected to withstand real-world failures, not just controlled scenarios,” observes Hunter Smit, senior manager of product marketing at F5.
Production Demands Expose Architectural Fragilities
In a pilot project, a data transfer delay might be a minor inconvenience; in a production environment, that same delay can escalate into a critical outage. The underlying issue often lies in point-to-point architectures where storage clients connect directly to storage systems. These configurations lack inherent resilience, becoming increasingly fragile when subjected to sustained, concurrent production traffic. A single node failure or a traffic surge can cascade through retries and timeouts, bottlenecking the entire pipeline precisely when business operations depend on its output.
“Direct connections between S3 clients and S3 storage lack resilience,” states Paul Pindell, principal solutions architect for technology alliances at F5. “If a storage node fails, all traffic to that cluster degrades, and in some instances, the entire cluster may become unresponsive.”
This vulnerability is particularly pertinent as AI workflows, including RAG-based inference and agentic AI, increasingly integrate S3 storage as a core component. However, the network connectivity historically designed for such storage was not optimized for the high-throughput, uninterrupted data movement essential for peak GPU performance.
The Hidden Costs of Stalled Pipelines and Underutilized GPUs
“Enterprise leaders frequently focus AI infrastructure discussions on GPU utilization, but the distinction with AI, compared to traditional deterministic workloads, is that the infrastructure continuously influences outcomes at every interaction,” explains Tanu Mutreja, senior director of product management at F5. “In AI environments, the infrastructure is no longer a secondary concern; it actively shapes customer experience, quality, resilience, and cost with each transaction.”
The business implications are substantial. Stalled inference pipelines directly impact SLAs and customer satisfaction. Delays in RAG systems can prevent models from accessing up-to-date context, leading to inaccurate or fabricated responses, thereby creating operational, compliance, and reputational risks. Concurrently, these infrastructure deficiencies can inflate costs by leaving expensive GPU resources idle or operating below capacity.
“Underutilized GPUs are a clear indicator of infrastructure inefficiencies that drive up costs while limiting scalability and responsiveness,” Mutreja notes. “The critical leadership question is whether the end-to-end AI infrastructure consistently delivers reliable, secure, high-quality, and governed AI experiences at sustainable unit economics.”
Establishing a Production-Ready Data Delivery Layer
F5 advocates for treating data delivery as a fundamental infrastructure layer, rather than an assumed background function. Just as application delivery management optimizes the flow of requests between users and applications, data delivery management focuses on optimizing data movement between storage, networks, and compute resources, including AI compute clusters.
Implementing data delivery as a primary layer necessitates embedding three key properties:
- Observability: This provides real-time insights into latency, throughput, and the overall health of data flows.
- Programmability: This enables policy-driven control over data movement through dynamic routing, traffic optimization, rate management, and automated failover mechanisms.
- Failure-awareness: This builds resilience against degraded networks, storage throttling, and service disruptions.
In the architecture F5 has engineered in collaboration with Dell ObjectScale, F5 BIG-IP serves as a programmable control point at the storage edge, positioned between Dell ObjectScale and the AI compute clusters.
“We’ve encountered situations where a misconfiguration within the AI compute layer inadvertently caused a denial-of-service (DoS) condition on the S3 storage infrastructure,” Pindell recounts. “This wasn’t malicious, but rather an accidental consequence that resulted in significant storage downtime for the entire organization.”
By deploying BIG-IP as the application delivery controller between the storage and compute layers, F5 protects the storage infrastructure through quality of service (QoS), rate limiting, and connection management, ensuring its resilience and operational continuity even under excessive load. Rigorous testing validated by SecureIQLab has confirmed that these protective measures do not compromise throughput—a critical architectural consideration.
“Preserving, and ideally enhancing, throughput is non-negotiable,” Pindell emphasizes. “This capability allows us to implement higher-level functionalities, resilience, and advanced security without sacrificing performance.”
Navigating Hybrid and Multicloud AI Complexity
AI deployments in hybrid multicloud environments present amplified data delivery challenges due to inherent heterogeneity. Data traversing these distributed environments must contend with inconsistent policies, disparate security controls, varied identity systems, fragmented governance frameworks, and distinct failure boundaries.
Programmable traffic management and enhanced observability work in concert to address this complexity. Observability offers a unified perspective on application, network, and infrastructure health across otherwise siloed environments. Leveraging these insights, programmable traffic management intelligently routes, balances, and facilitates failover for traffic in real time. Together, they establish a closed-loop system that enforces consistent policies, bolsters resilience across diverse failure domains, and guarantees reliable, high-performance AI data delivery, irrespective of the physical or logical location of applications, data, or users.
Distinguishing Production AI from Protracted Pilots
Organizations that successfully advance beyond pilot phases demonstrate a distinct engineering discipline, according to Smit.
“They approach production design with the mindset that failure is the norm, not the exception,” he elaborates. “They anticipate latency, congestion, and partial outages and engineer a data path that is observable and failure-aware enough to absorb these events, incorporating explicit mitigation strategies for every degraded condition rather than relying on the network to hold up.”
Companies remaining in perpetual pilot phases often continue optimizing for ideal laboratory outcomes, only to encounter the real-world gap when workloads go live. The core issue is not necessarily the quality of the models or the number of GPUs, but whether the data delivery layer has been engineered with the same level of rigor applied to the compute resources.
“Teams must recognize that real-world networks behave fundamentally differently from optimized lab environments,” Pindell advises. “A comprehensive mitigation plan for the inevitable failure states and performance bottlenecks encountered in production is essential.”
Business Style Takeaway: The transition of AI from pilot to production is critically dependent on a robust data delivery infrastructure, not just compute power. Businesses must view data delivery as a core engineering discipline, focusing on observability, programmability, and failure-awareness to ensure consistent performance, mitigate risks, and achieve sustainable economics for their AI initiatives.
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