Enterprise AI Delivery and Security
The future is powered by connected and distributed AI models. F5 empowers enterprises to scale, connect, and secure AI workflows, optimizing performance and unlocking the full potential of AI.
AI applications demand unstoppable performance and security. F5 delivers.
AI applications are the most modern of modern applications, pushing the boundaries of innovation and complexity. F5 brings decades of unmatched expertise in application delivery and security, making F5 indispensable in ensuring AI workflows perform flawlessly, scale effortlessly, and stay secure against emerging threats.
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Harnessing AI to accelerate your business faster than your competition involves integrating your data, customer information, and intellectual property to maintain and increase your competitive edge. But without robust security, you risk data leaks, compromised models, and exploited APIs connecting your AI apps. By safeguarding AI at every layer, enterprises defend their brand, preserve trust, and unlock the true potential of AI-driven transformation. The F5 Application Delivery and Security Platform seamlessly protects AI workloads wherever they run. With adaptive, layered defenses, it provides unmatched resilience, scalability, and performance—empowering organizations to secure their AI investments with unified, powerful security from a trusted industry leader.
Data throughput bottlenecks throttle AI models. Without steady, protected data pipelines, GPUs sit idle, costs rise, and models miss their mark. High-performance AI networking and traffic management from F5 solves these challenges with secure, accelerated networking. The F5 Application Delivery and Security Platform keeps every AI-powered app fast, available, and fully under your control—wherever they live. By unifying industry leading application delivery and security in one programmable platform, F5 lets you deploy in any form factor, manage with a single policy, and automate the entire lifecycle.
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With our partners, F5 simplifies and secures AI application ecosystems.
F5 collaborates with the world’s leading AI innovators to form industry-leading technology alliance partnerships. Together, we provide integrated, secure, and streamlined solutions to support complex AI application ecosystems.
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Explore the AI Reference Architecture
Gain a foundational understanding of the seven AI building blocks with this framework designed to teach core concepts for developing and deploying AI applications. Explore best practices, security considerations, and workflow strategies to help teams navigate risks and improve performance across SaaS, cloud-hosted, edge-hosted, and self-hosted environments.
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Inference
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RAG
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RAG Corpus Management
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Fine-Tuning
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Training
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Agentic External Service Integration
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Development
Within the F5 AI Reference Architecture, we have defined seven AI building blocks required for cloud-scale AI infrastructure and AI factories: Inference, RAG, RAG Corpus Management, Fine-Tuning, Training, Agentic External Service Integration, and Development. Click through the seven building blocks to explore in detail; visit the AI Reference Architecture Interactive Experience to explore how to simplify and scale AI deployments with best practices, security insights, and tools for hybrid multicloud innovation.
Outlines the interaction between a front-end application and an inference service API; centers on sending a request to an AI model and receiving a response. This sets the groundwork for more intricate interactions.
Enhances the basic Inference by adding large language model (LLM) orchestration and retrieval augmentation services. It details retrieving additional context from vector databases and content repositories, which is then used to generate a context-enriched response.
Focuses on the data ingest processes required for Inference with retrieval-augmented generation (RAG). It includes data normalization, embedding, and populating vector databases, preparing content for RAG calls.
Aims to enhance an existing model's performance through interaction with the model. It adjusts the model without rebuilding it from scratch and emphasizes collecting data from Inference and Inference with RAG for fine-tuning workflows.
Involves constructing a new model from the ground up, although it may use previous checkpoints (re-training). It covers data collection, preprocessing, model selection, training method selection, training, and validation/testing. This iterative process aims to create robust models tailored to specific tasks.
This capability encompasses the seamless integration of AI with external services and APIs, known as agentic AI, enabling dynamic interaction, data retrieval, and action execution based on user requests or model inference. By leveraging external tools, databases, and MCP (Model Control Protocol), the AI extends its functionality and demonstrates agentic behaviors, autonomously making decisions or taking proactive actions as necessary. This enhances the system's ability to provide intelligent, context-aware responses and solutions by utilizing a wide array of external resources and services.
Encompasses workflows for developing, maintaining, configuring, testing, and deploying AI application components. It includes front-end applications, LLM orchestration, source control management, and CI/CD pipelines.
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Explore global AI security insights from leading enterprises, highlighting strategies to protect AI models and address vulnerabilities in an increasingly complex threat landscape.