Networking for Kubernetes solutions

Ingress for Kubernetes

As organizations standardize on Kubernetes for production applications, they require ingress that’s proven, secure, and production-ready. F5 delivers enterprise-grade ingress through F5 NGINX Ingress Controller and BIG-IP Container Ingress Services, combining open-source flexibility with commercial support and long-term stability.

Teams secure apps, APIs, and AIs at ingress with Kubernetes-native WAF protection, enabling consistent policy enforcement across the enterprise. BIG-IP users can seamlessly extend existing traffic management and security into Kubernetes—preserving operational models while enabling cloud-native modernization.

Gateway API

Modern Kubernetes platforms require a single gateway to deliver applications, APIs, and AI workloads—eliminating tool sprawl and complexity. NGINX Gateway Fabric implements a conformant Gateway API solution to provide a consistent, extensible connectivity model built on Kubernetes-native APIs and CRDs.

Enable advanced routing, traffic splitting, weighted load balancing, and policy-driven governance while cleanly separating platform and developer responsibilities. The same gateway delivers API, app, and AI inference traffic with visibility and control.

AI factory load balancing

Scaling AI inference and training workloads requires more than traditional load balancing. GPU-intensive prompts consume memory and compute unpredictably, creating contention, cache pressure, and uneven utilization across clusters.

F5 delivers high-performance L4–L7 traffic management purpose-built for Kubernetes-based AI environments, including GPUaaS and on-prem deployments. By continuously evaluating memory availability, KV cache pressure, request queue depth, and backend health, F5 intelligently routes traffic to balance GPU load, reduce latency, improve stability and maximize infrastructure efficiency.

Multi-cluster and multi-tenancy

As Kubernetes expands to multi-cluster, hybrid, and multicloud deployments, teams need a consistent application connectivity layer for ingress, security, and observability. Without it, ingress sprawl leads to policy drift, inconsistent TLS, duplicated tools, and poor visibility.

An enterprise-grade traffic and security plane integrated with Kubernetes automation solves this. Using declarative CRDs and orchestration events, teams can automate routing, load balancing, TLS, DNS steering, and policy per cluster—ensuring multi-tenant isolation, HA/DR, and end-to-end telemetry without new silos.

Egress for Kubernetes

AI workloads often require multi-tenant Kubernetes clusters to maximize GPU investments and infrastructure ROI. As AI apps integrate sensitive data, secure access becomes critical.

While Kubernetes namespaces provide logical separation inside a cluster, true zero trust security demands the separation is maintained in the network segmentation beyond it.

Organizations need a Kubernetes-aware solution that maps namespaces and policies to network-level controls—enforcing isolation, protecting data, and securing AI environments end to end.

Observability for Kubernetes

Kubernetes workloads are dynamic and ephemeral, making consistent visibility into app performance difficult.

Telemetry is often fragmented across logs, metrics, and traces, slowing troubleshooting across hybrid and multicloud clusters.

A modern observability strategy uses OpenTelemetry for standardized data pipelines and eBPF-based telemetry for low-overhead, kernel-level insight—without app changes. Combined with automation, teams gain real-time visibility into traffic, latency, errors, and dependencies, enabling proactive performance and policy management at scale.