Why AI storage demands a new approach to load balancing

Mark MengerSolutions Architect

Griff ShelleyProduct Marketing Manager

This blog post is the fourth in a series about AI data delivery.

In the late 1960s, the rock classic, ”The Weight” by The Band (“Take a load off Fanny …”) told a simple story about burden: how responsibility gets passed along until someone quietly carries more than they should.

For decades, IT infrastructure worked much the same way. Systems absorbed loads as best they could, and uneven pressure was often tolerated as “good enough.”

AI changes that equation entirely. Training, fine-tuning, and retrieval workloads generate levels of concurrency and data access intensity that make invisible load-bearing assumptions dangerous. In modern AI environments, load balancing is no longer about fairness. It is about preventing collapse.

How AI changes the load-balancing conversation

AI workloads introduce extreme read fan-out, burst concurrency, and large-object access patterns that break long-standing assumptions about storage access.

Thousands of AI agents and data-loading processes may simultaneously pull the same datasets, write checkpoints, or retrieve embeddings. Traditional direct-to-storage architectures were never designed for this level of parallelism.

As a result, load balancing shifts from a convenience to a stability mechanism. It becomes essential for maintaining availability, protecting shared resources, and keeping AI jobs running predictably under pressure.

The new reality of AI data access patterns

What distinguishes AI data access is not just volume, but simultaneity. Training jobs often involve large numbers of workers reading identical data at the same time. Fine-tuning introduces frequent checkpoint writes. Retrieval-augmented generation (RAG) adds bursty, latency-sensitive read traffic during inference.

These patterns create what have come to be known as “hot spots”—concentrated stress on specific storage nodes, gateways, or network paths. When a single component becomes hot, the impact is rarely isolated. Slowdowns propagate quickly, turning localized congestion into stalled training runs, degraded retrieval performance, or outright job failures.

This changes the issue from one of compute scale to how we access data at scale.

How “do nothing” architectures break under AI load

Historically, many teams relied on simple approaches to storage access. Clients connected directly to storage endpoints. DNS round robin or client-side retries provided basic distribution. Storage systems managed internal placement and replication.

These approaches worked when traffic was steadier and fan-out was limited. Under AI workloads, they fail for three reasons. First, they assume relatively uniform request cost, which AI workloads violate. Second, they push failure handling and congestion control to clients or jobs that lack global context. Third, they amplify instability during bursts, as retries and reconnects pile more load onto already stressed components.

Doing nothing is no longer neutral. It is an architectural risk.

The limits of basic load-balancing techniques for AI

Simple load-balancing methods expose these weaknesses quickly. Round robin assumes each request places a similar load on the backend. DNS-based approaches lack real-time awareness of backend health or congestion. Storage systems can observe traffic, but they cannot shape or regulate it before damage is done.

In AI environments, one slow or overloaded node can throttle hundreds of workers. Retry storms magnify congestion instead of relieving it. Latency spikes become unpredictable, especially for retrieval workloads where response time directly affects application behavior. These are not edge cases. They are normal operating conditions for AI systems at scale.

Putting control in front of storage

A different design pattern is emerging: placing an application delivery controller (ADC) in front of storage for AI data access. Solutions such as F5 BIG-IP provide a single, policy-controlled front door for ingest and read traffic, rather than allowing every worker to connect independently.

This fundamentally changes system behavior. Traffic can be shaped before it overwhelms storage nodes. Noisy or misbehaving jobs can be isolated. Intelligent distribution prevents hot spots from forming rather than reacting after the fact. Failover becomes clean and predictable during node degradation, maintenance, or rebalance events.

For AI workloads, this control layer protects cluster stability during training spikes and RAG query bursts. It ensures that no single component quietly carries more than it should.

For decades, technologies such as F5 BIG-IP have been managing load, health, and failure across mission-critical applications. AI workloads extend those same principles into the data layer, where extreme concurrency and burst access patterns leave far less margin for error.

Health-aware traffic steering for AI workloads

In AI systems, health matters more than fairness. Storage nodes degrade under sustained load. Failures and rebalancing are routine, not exceptional. AI jobs, however, are intolerant of partial availability and inconsistent performance.

Health-aware traffic steering across storage nodes and clusters allows systems to adapt continuously. Unhealthy or overloaded targets are avoided automatically. Traffic rebalances during failures and scale events without forcing job restarts. Recovery is faster because congestion is prevented, not merely detected.

This capability directly supports reliable training, fine-tuning, and retrieval at scale.

Visibility as a hidden advantage

One of the least appreciated benefits of placing control in front of storage is visibility. Storage teams often lack insight into how AI jobs actually access data. Job-level latency patterns, error rates by workload, and read amplification during bursts are difficult to observe from within storage platforms alone.

By centralizing access through an ADC, teams gain actionable visibility into AI data behavior. Latency distributions, access hot spots, and early signs of instability become visible in real time. This insight enables better capacity planning, faster troubleshooting, and more informed architectural decisions as AI workloads evolve.

Securing AI data access without changing storage

AI data pipelines also demand stronger governance. Centralized enforcement of authentication, encryption, and rate limits at the storage edge secures sensitive training and inference data without modifying storage platforms or clients.

This approach scales naturally with AI concurrency. It protects data flows while preserving performance and avoids pushing security logic into every application or job.

Load balancing as an AI stability layer

AI data ingestion and access now define infrastructure limits. The era of assuming storage will quietly absorb uneven load is over. Load balancing is no longer optional, and it is no longer generic.

Placing intelligent, health-aware control in front of storage enables higher throughput, lower latency, and greater resilience for AI workloads. More importantly, it transforms load balancing into a stability layer that keeps AI systems running when concurrency, scale, and data intensity reach their peak.

F5 delivers and secures AI applications anywhere

For more information about our AI data delivery solutions, visit our AI Data Delivery and Infrastructure Solutions webpage. Also, stay tuned for the next blog post in our AI data delivery series focusing on optimizing AI infrastructure and designing for scalability and resilience.

F5’s focus on AI doesn’t stop with data delivery. Explore how F5 secures and delivers AI apps everywhere.

Be sure to check out our previous blog posts in the series:

Fueling the AI data pipeline with F5 and S3-compatible storage

Optimizing AI by breaking bottlenecks in modern workloads

Tracking AI data pipelines from ingestion to delivery

About the Authors

Mark MengerSolutions Architect

Mark Menger is a Solutions Architect at F5, specializing in AI and security technology partnerships. He leads the development of F5’s AI Reference Architecture, advancing secure, scalable AI solutions. With experience as a Global Solutions Architect and Solutions Engineer, Mark contributed to F5’s Secure Cloud Architecture and co-developed its Distributed Four-Tiered Architecture. Co-author of Solving IT Complexity, he brings expertise in addressing IT challenges. Previously, he held roles as an application developer and enterprise architect, focusing on modern applications, automation, and accelerating value from AI investments.

More blogs by Mark Menger

Griff ShelleyProduct Marketing Manager

Griff Shelley is a Product Marketing Manager at F5, specializing in hardware, software, and SaaS application delivery solutions. With a passion for connecting innovative technology to customer success, Griff drives go-to-market projects in global and local app delivery, cloud services, and AI data traffic infrastructure. Prior to his career in tech, he was a post-secondary education academic advisor and earned degrees from Eastern Washington University and Auburn University.

More blogs by Griff Shelley