F5 AI Roadmap
We are currently using F5 AFM for DDoS mitigation and plan to enhance our protection by adding WAF in production soon. Could you share insights on F5’s AI roadmap in the context of DDoS protection? Additionally, what AI-driven features or capabilities can we enable to strengthen our defenses? any ppt or pdf for RoadmapJust Announced! Attend a lab and receive a Raspberry Pi
Have a Slice of AI from a Raspberry Pi Services such as ChatGPT have made accessing Generative AI as simple as visiting a web page. Whether at work or at home, there are advantages to channeling your user base (or family in the case of at home) through a central point where you can apply safeguards to their usage. In this lab, you will learn how to: Deliver centralized AI access through something as basic as a Raspberry Pi Learn basic methods for safeguarding AI Learn how users might circumvent basic safeguards Learn how to deploy additional services from F5 to enforce broader enterprise policies Register Here This lab takes place in an F5 virtual lab environment. Participants who complete the lab will receive a Raspberry Pi* to build the solution in their own environment. *Limited stock. Raspberry Pi is exclusive to this lab. To qualify, complete the lab and join a follow-up call with F5.Webinar (08/21): Deliver and Secure Enterprise AI Applications
Upcoming Webinar: Deliver and Secure Enterprise AI Applications Date: August 21, 2025 Time: 10:00am PT | 1:00pm ET What's the webinar about? AI technology is evolving rapidly, moving from early predictive models and machine learning to the widespread use of generative AI. Enterprises are now deploying these generative capabilities and preparing for the next phase: autonomous agentic AI systems and physical AI that connects the digital and real worlds. Each phase brings increased demands on infrastructure, data delivery, and security. In this webinar, we will explore how enterprises can scale, secure, and manage modern AI workloads by focusing on two key critical areas: AI Application & Data Delivery and AI Security. As AI workloads extend across hybrid and multicloud environments, its crucial enterprises ensure low-latency performance, reliable data throughput, and robust protection against emerging threats. Join us to discover how to deliver and defend high-performance AI applications—and protect the data, models, and APIs that drive them. Learning Objectives: Protect AI applications, models, and data with full visibility, scalable security, and compliance-ready controls. Accelerate the delivery of AI applications and the high-throughput data flows that power them. Orchestrate seamless API and data connectivity to optimize inference pipelines across hybrid multicloud environments. Learn more, register now https://www.f5.com/company/events/webinars/deliver-and-secure-enterprise-ai-applicationsHow To Build An Agent With n8n And Docker - AI Step-By-Step Lab
OK, community... It’s time for another addition to our AI Step-By-Step Labs, from yours truly! I did a mini lab for installing n8n on a mac. It has been popular, but we thought it would be better as a Docker installation to fit into our lab. So I set out to do this on Docker and it was super easy. I'm not going to spoil it too much, as I'd like for you to visit the GitHub repository for our labs, but this is the flow you create: This lab walks you through installation in Docker and walks you through creation of an AI agent that proxies requests to the LLM instance you created with our first lab on installing Ollama. Please check out the repository on GitHub to get started using this powerful agentic framework in your lab, production or personal use today. Feel free to watch the video on our YouTube channel here:
Upcoming Webinar (09/09): Security, Performance, and Scalability with F5 and MinIO
Upcoming Webinar: F5 & MinIO: Redefining Enterprise AI Data Infrastructure: Security, Performance, and Scalability Date: September 9, 2025 Time: 10:00am PT | 1:00pm ET Speakers: Mark Menger, Solutions Architect, F5 Keith Pijanowski, AI Solutions Engineer, MinIO What's the webinar about? Learn how leading organizations are accelerating their AI workflows—from seamless data ingestion for model training and real-time RAG (Retrieval-Augmented Generation) corpus management to optimizing data repatriation from the cloud back to on-premises environments. Join us for an exclusive webinar with Mark Menger, Solutions Architect at F5, and special guest Keith Pijanowski, AI Solutions Engineer at MinIO. Discover expert insights on how F5 and MinIO are transforming enterprise AI data infrastructure. This webinar will showcase how F5 BIG-IP’s advanced L4-7 security and intelligent load balancing, combined with MinIO's AIStor robust object storage, provide a powerful integrated solution to streamline and enhance every stage of AI and ML pipelines. What you'll learn: Streamlined AI Workflows: Unlock faster, more efficient AI deployments with optimized data pipelines—from data ingestion and model training to RAG corpus management. Cloud-to-On-Prem Data Control: Master best practices for securely repatriating data from the cloud to on-premises environments to maximize performance, compliance, and control. Enterprise-Grade Security & Scale: Explore how L4-7 security and intelligent load balancing ensure data integrity, application availability, and seamless AI workflow support at scale. Business Value Unlocked: See how F5 and MinIO’s combined solutions reduce complexity, cut costs, and enable scalable, future-ready AI architectures. Don’t miss this opportunity to learn how F5 and MinIO are powering the next generation of enterprise AI innovation. Learn more, register hereF5 and MinIO: AI Data Delivery for the hybrid enterprise
Introduction Modern application architectures demand solutions that not only handle exponential data growth but also enable innovation and drive business results. As AI/ML workloads take center stage in industries ranging from healthcare to finance, application designers are increasingly turning to S3-compliant object storage because of its ability to provide scalable management of unstructured data. Whether it’s for ingesting massive datasets, running iterative training models, or delivering high-throughput predictions, S3-compatible storage systems play a foundational role in supporting these advanced data pipelines. MinIO has emerged as a leader in this space, offering high-performance, S3-compatible object storage built for modern-scale applications. MinIO is designed to easily work with AI/ML workflows. It is lightweight and cloud-based, so it is a good choice for businesses that are building infrastructure to support innovation. From storing petabyte-scale datasets to providing the performance needed for real-time AI pipelines, MinIO delivers the reliability and speed required for data-intensive work. While S3-compliant storage like MinIO forms the backbone of data workflows, robust traffic management and application delivery capabilities are essential for ensuring continuous availability, secure pipelines, and performance optimization. F5 BIG-IP, with its advanced suite of traffic routing, load balancing, and security tools, complements MinIO by enabling organizations to address these challenges. Together, F5 and MinIO create a resilient, scalable architecture where applications and AI/ML systems can thrive. This solution empowers businesses to: Build secure and highly-available storage pipelines for demanding workloads. Ensure fast and reliable delivery of data, even at exascale. Simplify and optimize their infrastructure to drive innovation faster. In this article, we’ll explore how to leverage F5 BIG-IP and MinIO AIStor clusters to enable results-driven application design. Starting with an architecture overview, we’ll cover practical steps to set up BIG-IP to enhance MinIO’s functionality. Along the way, we’ll highlight how this combination supports modern AI/ML workflows and other business-critical applications. Architecture Overview To validate the solution of F5 BIG-IP and MinIO AIStor effectively, this setup incorporates a functional testing environment that simulates real-world behaviors while remaining controlled and repeatable. MinIO’s warp benchmarking tool is used for orchestrating and running tests across the architecture. The addition of benchmarking tools ensures that the functional properties of the stack (traffic management, application-layer security, and object storage performance) are thoroughly evaluated in a way that is reproducible and credible. The environment consists of: A F5 VELOS chassis with BX110 blades, running BIG-IP instances configured using F5’s AS3 extension, for traffic management and security policies using LTM (Local Traffic Manager) and ASM (Application Security Manager). A MinIO AIStor cluster consisting of four bare-metal nodes equipped with high-performance NVMe drives, bringing the environment close to real-world customer deployments. Three benchmarking nodes for orchestrating and running tests: One orchestration node directs the worker nodes with benchmark test configuration and aggregates test results. Two worker nodes run warp in client mode to simulate workloads against the MinIO cluster. Warp Benchmarking Tool The warp benchmarking tool (https://github.com/minio/warp) from MinIO is designed to simulate real-world S3 workloads while also generating measurable metrics about the testing environment. In this architecture: A central orchestration node is used to coordinate the benchmarking process. This ensures that each test is consistent and runs under comparable conditions. Two worker nodes running warp in client mode send simulated traffic to the F5 BIG-IP virtual server. These nodes act as workload generators, allowing for the simulation of read-heavy, write-heavy, or mixed object storage exercises. Warp’s distributed design enables the scaling of workload generation, ensuring that the MinIO backend is tested under real-world-like conditions. This three-node configuration ensures that benchmarking tests are distributed effectively. It also provides insights into object storage behavior, traffic management, and the impact of security enforcement in the environment. Traffic Management and Security with BIG-IP At the center of this setup is the F5 VELOS chassis, running BIG-IP instances configured to handle both traffic management (LTM) and application-layer security (ASM). The addition of ASM (Application Security Manager) ensures that the MinIO cluster is protected from malicious or malformed requests while maintaining uninterrupted service for legitimate traffic. Key functions of BIG-IP in this architecture include: Load Balancing: Avoid overloading specific MinIO nodes by using adaptive algorithms, ensuring even traffic distribution, and preventing bottlenecks and hotspots. Apply advanced load balancing methods like least connections, dynamic ratio, and least response time. These methods intelligently account for backend load and performance in real time, ensuring reliable and efficient resource utilization. SSL/TLS Termination: Terminate SSL/TLS traffic to offload encryption workloads from backend clients. Re-encryption is optionally enabled for secure communication to MinIO nodes, depending on performance and security requirements. Health Monitoring: Intelligently performs continuous monitoring of the availability and health of the backend MinIO nodes. It reroutes traffic away from unhealthy nodes as necessary and restores traffic as service is restored. Application-Layer Security: Protect the environment via Web Application Firewall (WAF) policies that block malicious traffic, including injection attacks, malformed API calls, and DDoS-style app-layer threats. BIG-IP acts as the gateway for all requests coming from S3 clients, ensuring that security, health checks, and traffic policies are all applied before requests reach the MinIO nodes. Traffic Flow Through the Full Architecture The test traffic flows through several components in this architecture, with BIG-IP and warp playing vital roles in managing and generating requests, respectively: Benchmark Orchestration: The warp orchestration node initiates tests and distributes workload configurations to the worker nodes. The warp orchestration node also aggregates test data results from the worker nodes. Warp manages benchmarking scenarios, such as read-heavy, write-heavy, or mixed traffic patterns, targeting the MinIO storage cluster. Simulated Traffic from Worker Nodes: Two worker nodes, running warp in client mode, generate S3-compatible traffic such as object PUT, GET, DELETE, or STAT requests. These requests are transmitted through the BIG-IP virtual server. The load generation simulates the kind of requests an AI/ML pipeline or data-driven application might send under production conditions. BIG-IP Processing: Requests from the worker nodes are received by BIG-IP, where they are subjected to: Traffic Control: LTM distributes the traffic among the four MinIO nodes while handling SSL termination and monitoring node health. Security Controls: ASM WAF policies inspect requests for signs of application-layer threats. Only safe, valid traffic is routed to the MinIO environment. Environment Configuration Prerequisites BIG-IP (physical or virtual) Hosts for the MinIO cluster, including configured operating systems (and scheduling systems if optionally selected) Hosts for the warp worker nodes and warp orchestration node, including configured operating systems All required networking gear to connect the BIG-IP and the nodes A copy of the AS3 template at https://github.com/f5businessdevelopment/terraform-kvm-minio/blob/main/as3manualtemplate.json A copy of the warp configuration file at https://github.com/minio/warp/blob/master/yml-samples/mixed.yml Step 1: Set up MinIO Cluster Follow MinIO’s install instructions at https://min.io/docs/minio/linux/index.html The link is for a Linux deployment but choose the deployment target that’s appropriate for your environment. Record the addresses and ports of the MinIO consoles and APIs configured in this step for use as input to the next steps. Step 2: Configure F5 BIG-IP for Traffic Management and Security Following the steps documented in https://github.com/f5businessdevelopment/terraform-kvm-minio/blob/main/MANUALAS3.md and using the template file downloaded from GitHub, create and apply an AS3 declaration to configure your BIG-IP. Step 3: Deploy and Configure MinIO Warp for Benchmarking Retrieve API Access and Secret keys Log into your MinIO Cluster and click the 'Access' icon Once in 'Access', click the 'Access Keys' button In ‘Access Keys’, click the ‘Create Access Keys’ button and follow the steps to create and record your access and secret key values. Update warp key and secret value In your warp configuration file, find the access-key and secret-key fields and update the values with those you recorded in the previous step. Update warp client addresses In your warp configuration file, find the warp-client field and update the value with the addresses of the worker nodes. Update warp s3 host address In your warp configuration file, find the host field and update the value with the address and port of the VIP listener on the BIG-IP Step 4: Verify and Monitor the Environment Start the warp on each of the worker nodes with the command warp client Once the warp clients respond that they are listening on the warp orchestrator node, start the warp benchmark test warp run test.yaml Replace test.yaml with the name of your configuration file Summary of Test Results Functional tests done in F5’s lab, using the method described above, show how the F5 + MinIO solution works and behaves. These results highlight important considerations that apply to both AI/ML pipelines and data repatriation workflows. This enables organizations to make informed design choices when deploying similar architectures. The testing goals were: Validate that BIG-IP security and traffic management policies function properly with MinIO AIStor in a simulated real-world configuration. Compare the impact of various load-balancing, security, and storage strategies to determine best practices. Test Methodology Four test configurations were executed to identify the effects of: Threads Per Worker: Testing both 1-thread and 20-thread configurations for workload generation. Multi-Part GETs and PUTs: Comparing scenarios with and without multi-part requests for better parallelization. BIG-IP Profiles: Evaluating Layer 7 (ASM-enabled security) versus Layer 4 (performance-optimized) profiles. Test Results Test Configuration Throughput Benefits 20 threads, multi-part, Layer 7 28.1 Gbps Security, High-performance reliability 20 threads, multi-part, Layer 4 81.5 Gbps High-performance reliability 1 thread, no multi-part, Layer 7 3.7 Gbps Security, Reliability 1 thread, no multi-part, Layer 4 7.8 Gbps Reliability Note: The testing results provide insights into the solution and behavior of this setup, though they are not intended as production performance benchmarks. Key Insights Multi-Part GETs and PUTs Are Critical for Throughput Optimization: Multi-part operations split objects into smaller parts for parallel processing. This allows the architecture to better utilize MinIO’s distributed storage capabilities and worker thread concurrency. Without multi-part GETs/PUTs, single-threaded configurations experienced severely reduced throughput. Recommendation: Ensure multi-part operations are enabled in applications or tools interacting with MinIO when handling large objects or high IOPS workloads. Balance Security with Performance: Layer 7 security provided by ASM is essential for sensitive data and workloads that interact with external endpoints. However, it introduces processing overhead. Layer 4 performance profiles, while lacking application-layer security features, deliver significantly higher throughput. Recommendation: Choose BIG-IP profiles based on specific workload requirements. For AI/ML data ingest and model training pipelines, consider enabling Layer 4 optimization during bulk read/write phases. For workloads requiring external access or high-security standards, deploy Layer 7 profiles. In some cases, consider horizontal scaling of load balancing and object storage tiers to add throughput capacity. Threads Per Worker Impact Throughput: Scaling up threads at the worker level significantly increased throughput in the lab environment. This demonstrates the importance of concurrency for demanding workloads. Recommendation: Optimize S3 client configurations for higher connection counts where workloads permit, particularly when performing bulk data transfers or operationally intensive reads. Example Use Cases Use Case #1: AI/ML Pipeline AI and machine learning pipelines rely heavily on storage systems that can ingest, process, and retrieve vast amounts of data quickly and securely. MinIO provides the scalability and performance needed for storage, while F5 BIG-IP ensures secure, optimized data delivery. Pipeline Workflow An enterprise running a typical AI/ML pipeline might include the following stages: Data Ingestion: Large datasets (e.g., images, logs, training corpora) are collected from various sources and stored within the MinIO cluster using PUT operations. Model Training: Data scientists iterate on AI models using the stored training datasets. These training processes generate frequent GET requests to retrieve slices of the dataset from the MinIO cluster. Model Validation and Inference: During validation, the pipeline accesses specific test data objects stored in the cluster. For deployed models, inference may require low-latency reads to make predictions in real time. How F5 and MinIO Support the Workflow This combined architecture enables the pipeline by: Ensuring Consistent Availability: BIG-IP distributes PUT and GET requests across the four nodes in the MinIO cluster using intelligent load balancing. With health monitoring, BIG-IP proactively reroutes traffic away from any node experiencing issues, preventing delays in training or inference. Optimizing Performance: NVMe-backed storage in MinIO ensures fast read and write speeds. Together with BIG-IP's traffic management, the architecture delivers reliable throughput for iterative model training and inference. Securing End-to-End Communication: ASM protects the MinIO storage APIs from malicious requests, including malformed API calls. At the same time, SSL/TLS termination secures communications between AI/ML applications and the MinIO backend. Use Case #2: Enterprise Data Repatriation Organizations increasingly seek to repatriate data from public clouds to on-premises environments. Repatriation is often driven by the need to reduce cloud storage costs, regain control over sensitive information, or improve performance by leveraging local infrastructure. This solution supports these workflows by pairing MinIO’s high-performance object storage with BIG-IP’s secure and scalable traffic management. Repatriation Workflow A typical enterprise data repatriation workflow may look like this: Bulk Data Migration: Data stored in public cloud object storage systems (e.g., AWS S3, Google Cloud Storage) is transferred to the MinIO cluster running on on-premises infrastructure using tools like MinIO Gateway or custom migration scripts. Policy Enforcement: Once migrated, BIG-IP ensures that access to the MinIO cluster is secured, with ASM enforcing WAF policies to protect sensitive data during local storage operations. Ongoing Storage Optimization: The migrated data is integrated into workflows like backup and archival, analytics, or data access for internal applications. Local NVMe drives in the MinIO cluster reduce latency compared to cloud solutions. How F5 and MinIO Support the Workflow This architecture facilitates the repatriation process by: Secure Migration: MinIO Gateway, combined with SSL/TLS termination on BIG-IP, allows data to be transferred securely from public cloud object storage services to the MinIO cluster. ASM protects endpoints from exploitation during bulk uploads. Cost Efficiency and Performance: On-premises MinIO storage eliminates expensive cloud storage costs while providing faster access to locally stored data. NVMe-backed nodes ensure that repatriated data can be rapidly retrieved for internal applications. Scalable and Secure Access: BIG-IP provides secure access control to the MinIO cluster, ensuring only authorized users or applications can use the repatriated data. Health monitoring prevents disruptions in workflows by proactively managing node unavailability. The F5 and MinIO Advantage Both use cases reflect the flexibility and power of combining F5 and MinIO: AI/ML Pipeline: Supports data-heavy applications and iterative processes through secure, high-performance storage. Data Repatriation: Empowers organizations to reduce costs while enabling seamless local storage integration. These examples provide adaptable templates for leveraging F5 and MinIO to solve problems relevant to enterprises across various industries, including finance, healthcare, agriculture, and manufacturing. Conclusion The collaboration of F5 BIG-IP and MinIO provides a high-performance, secure, and scalable architecture for modern data-driven use cases such as AI/ML pipelines and enterprise data repatriation. Testing in the lab environment validates the functionality of this solution, while highlighting opportunities for throughput optimization via configuration tuning. To bring these insights to your environment: Test multi-part configurations using tools like MinIO warp benchmark or production applications. Match BIG-IP profiles (Layer 4 or Layer 7) with the specific priorities of your workloads. Use these findings as a baseline while performing further functional or performance testing in your enterprise. The flexibility of this architecture allows organizations to push the boundaries of innovation while securing critical workloads at scale. Whether driving new AI/ML pipelines or reducing costs in repatriation workflows, the F5 + MinIO solution is well-equipped to meet the demands of modern enterprises. Further Content For more information about F5's partnership with MinIO, consider looking at the informative overview by buulam on DevCentral's YouTube channel. We also have the steps outlined in this video.
Lab: Have a Slice of AI from a Raspberry Pi
Have a Slice of AI from a Raspberry Pi Services such as ChatGPT have made accessing Generative AI as simple as visiting a web page. Whether at work or at home, there are advantages to channeling your user base (or family in the case of at home) through a central point where you can apply safeguards to their usage. In this lab, you will learn how to: Deliver centralized AI access through something as basic as a Raspberry Pi Learn basic methods for safeguarding AI Learn how users might circumvent basic safeguards Learn how to deploy additional services from F5 to enforce broader enterprise policies This lab takes place in an F5 virtual lab environment. Participants who complete the lab will receive a Raspberry Pi* to build the solution in their own environment. Register Here *Limited stock. Raspberry Pi is exclusive to this lab. To qualify, complete the lab and join a follow-up call with F5. Sun, Sand, and Security Set not included.Hey DeepSeek, can you write iRules?
Back in time... Two years ago I asked ChatGPT whether it could write iRules. My conclusion after giving several tasks to ChatGPT was, that it can help with simple tasks but it cannot write intermediate or complex iRules. A new AI enters the competition Two weeks ago DeepSeek entered the scene and thought it's a good idea to ask it about its capabilities to write iRules. Spoiler alert: It cannot. New AI, same challenges I asked DeepSeek the same questions I asked ChatGPT 2 years ago. Write me an iRule that redirects HTTP to HTTPS Can you write an iRule that rewrites the host header in HTTP Request and Response? Can you write an iRule that will make a loadbalancing decision based on the HTTP Host header? Can you write an iRule that will make a loadbalancing decision based on the HTTP URI header? Write me an iRule that shows different ASM blocking pages based on the host header. The response should include the support ID. I stopped DeepSeek asking after the 5th question, DeepSeek is clueless about iRules. The answer I got from DeepSeek to 1, 2, 4 and 5 was always the same: when HTTP_REQUEST { # Check if the request is coming to port 80 (HTTP) if { [TCP::local_port] equals 80 } { # Construct the HTTPS URL set host [HTTP::host] set uri [HTTP::uri] set redirect_url "https://${host}${uri}" # Perform the redirect HTTP::redirect $redirect_url } } While this is a solution to task 1, it is plain wrong for 2, 3, 4 and 5. And even for the first challenge this is not a good. Actually it hurts me reading this iRule... Here for example task 2, just wrong... For task 3 DeepSeeks answer was: ChatGPT in 2025 For completeness, I gave the same tasks from 2023 to ChatGPT again. Briefly said - ChatGPT was OK in solving tasks 1-4 in 2023 and still is. It improved it's solution for task 5, the ASM iRule challenge. In 2023 I had two more tasks related to rewriting and redirecting. ChatGPT still failed to provide a solid solution for those two tasks. Conclusion DeepSeek cannot write iRules and ChatGPT still isn't good at it. Write your own iRules or ask the friendly people here on devcentral to help you.
