AI infrastructure solutions
Architect AI for success with secure, reliable data pipelines, AI factories, and inference delivery across hybrid multicloud environments.
Architect AI for success with secure, reliable data pipelines, AI factories, and inference delivery across hybrid multicloud environments.
The F5 Application Delivery and Security Platform (ADSP) helps AI-native and AI-enabled enterprises scale AI applications and workloads by ensuring data pipelines, GPU environments, and inference services run reliably and securely across environments. By optimizing AI traffic flows end to end, the F5 ADSP improves performance predictability, increases graphics processing unit (GPU) usage, simplifies operations, and protects AI data—accelerating innovation while reducing cost, complexity, and risk.
Get AI into production faster with reliable access to data for training, fine-tuning, and retrieval-augmented generation (RAG).
Use GPUs more efficiently by eliminating traffic and data bottlenecks across AI workloads.
Simplify AI operations with unified traffic management, orchestration, and security.
Protect AI data pipelines while delivering fast, secure access to S3-compatible storage.
AI data delivery and ingestion solutions help organizations move, protect, and access the data AI depends on—fast and at scale. F5 BIG-IP for AI data delivery secures and optimizes data ingestion for S3-compatible storage deployments supporting AI training, fine-tuning, and RAG workloads, while abstracting storage backends to avoid lock-in across hybrid multicloud environments.
With intelligent traffic control, real-time health monitoring, automated failover, and deep visibility, teams get reliable, compliant AI data pipelines that reduce risk, prevent disruption, and keep AI initiatives moving forward.
Routes AI data to the best location based on performance, latency, system health, and policy.
Balances AI workloads across clusters to make better use of resources and deliver fast, consistent performance.
Safeguards AI data delivery from disruptive volumetric and protocol-level attacks.
Secures AI data pipelines against cyberattacks, data poisoning, and unauthorized access.
Provides fast, hardware-accelerated AI data delivery at massive scale, ensuring high performance for demanding workloads.
Provides flexible, high-performance AI data delivery across modern and hybrid data centers.
Scaling AI workloads demands infrastructure that keeps GPUs busy, latency low, and traffic flowing to the right workloads.
F5’s AI Factory Load Balancing solutions intelligently route traffic across GPU clusters and Kubernetes environments—on-prem or graphics processing unit as a service (GPUaaS) deployments—to maximize performance and cost efficiency.
With centralized orchestration, multi-tenancy isolation, and intelligent task routing, F5 helps teams scale AI training and inference predictably, securely, and without wasted
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Balances AI workloads across clusters to make better use of resources and deliver fast, consistent performance.
Uses Kubernetes-native traffic management to route AI training and inference workloads efficiently and at scale.
Provides scalable ingress and intelligent traffic management for containerized AI workloads
Delivers high-performance, API-driven traffic routing to keep distributed AI inference services and agents fast, reliable, and efficient.
Optimizes AI application traffic in Kubernetes, delivering fast, low-latency performance at scale.
AI inference only delivers value when it’s fast, reliable, and secure. F5 helps enterprises deliver AI inference services—models, APIs, and agent runtimes—with predictable latency and high availability across hybrid and multicloud environments.
By providing a secure front door, intelligent Layer 7 routing, elastic scaling, and deep visibility, F5 ensures AI-powered applications stay responsive, protected, and ready for real-world demand.
Balances AI workloads across clusters to make better use of resources and deliver fast, consistent performance.
Routes AI data to the best location based on performance, latency, system health, and policy.
Provides fast, hardware-accelerated AI data delivery at massive scale, ensuring high performance for demanding workloads.
Provides flexible, high-performance AI data delivery across modern and hybrid data centers.
Delivers high-performance, API-driven traffic routing to keep distributed AI inference services and agents fast, reliable, and efficient.
SOLUTION OVERVIEW
SOLUTION OVERVIEW
AI infrastructure is the integrated hardware and software stack that allows AI initiatives to move from proof of concept to production at scale. It encompasses the systems that ingest, move, secure, and deliver data to GPU environments for training, fine-tuning, and inference, across on-prem, cloud, and hybrid multicloud deployments. Unlike traditional IT infrastructure, AI infrastructure must handle extreme data throughput, distributed GPU clusters, and highly variable traffic patterns with predictable performance, security, and operational control.
Neoclouds are emerging because hyperscale cloud economics and architectures are not optimized for sustained, high-density AI workloads. Enterprises and AI service providers need GPU-optimized cloud environments with tighter cost control, predictable performance, and greater flexibility in how storage, networking, and compute are combined. Neoclouds are purpose-built to provide the foundation of AI factories and AI data centers—specialized environments optimized for GPU use and data movement.
Large model training is fundamentally constrained by how fast data can be delivered to GPUs and how efficiently workloads are distributed across clusters. High-speed networking reduces synchronization latency, improves GPU utilization, and enables scalable parallel and distributed training across nodes and sites. Without intelligent traffic management and optimized data paths, network bottlenecks can negate investments in GPUs and storage, slowing training cycles and increasing cost per model.
AI training infrastructure is throughput-driven and batch-oriented, optimized for moving massive datasets into GPU clusters as efficiently as possible over sustained periods of time. Inference infrastructure is latency-sensitive and request-driven, focused on reliably delivering models, APIs, and agentic services to applications and users in real time. While both rely on the same foundational components—networking, security, and traffic management—they require different strategies to ensure performance, availability, and cost efficiency in production.
AI workloads depend on consistent, high-throughput access to distributed storage, often across S3-compatible object storage platforms, regions, and cloud environments. Load balancing ensures data requests are intelligently distributed based on health, performance, and policy—preventing hotspots, reducing bottlenecks, and eliminating single points of failure. For AI training, fine-tuning, and RAG pipelines, load-balanced storage is critical to keeping GPUs fed with data, maximizing GPU utilization, and maintaining predictable performance at scale.
