Did you know 94% of organizations deploy apps across multiple environments, including a combination of public cloud instances, private data centers, and edge systems? We’re living in the hybrid multicloud era, and it makes sense when you consider the benefits. In multicloud environments, businesses are no longer beholden to a specific vendor, and they can choose the services and instance types that work best for every workload they need to process, including AI.
However, multicloud AI faces unique challenges. To maintain AI’s accuracy and effectiveness, organizations need to feed it a steady stream of data, which means seamlessly connecting data repositories across environments. AI workloads are also highly dynamic, ranging from simple Q&A prompts to in-depth analyses or content creation that tax GPU resources unevenly.
Additionally, the cyberthreat landscape is more nuanced because AI has become its own attack vector through techniques such as prompt injection and model jailbreaking. AI also leverages numerous API connections—again due to its data-hungry nature—which broadens the attack surface and increases the risk of AI-originated lateral movement through a network.
And we haven’t yet addressed multicloud sprawl. It goes without saying that as you add more environments, more connections, and more complexity, it becomes exponentially more difficult to manage and maintain visibility over everything.
Overcoming multicloud AI challenges to maximize value
The good news is there’s a playbook for addressing these challenges, and it revolves around combining key infrastructure elements to create secure, high-performance pathways for AI. The setup generally looks like this:
- A private connectivity backbone is a superior choice over using the public Internet to connect separate environments. Private backbones provide secure, reliable connectivity that shields you from factors outside your control.
- Intelligent traffic management applies AI-aware routing instructions to more efficiently balance workloads across containers and prevent expensive GPU resources from being over or underutilized.
- Unified security frameworks implement the same protections (encryption, authentication, etc.) consistently across cloud boundaries. That means the same security and privacy policies applied everywhere—public cloud instances, private servers, and edge systems.
- Centralized orchestration platforms tie everything together and give you top-down visibility from end to end. The ideal orchestration platform will also surface AI-specific metrics, like model load times and inference latency, to drive your optimization efforts.
Checking these boxes gets you to the sweet spot of multicloud AI, where you can seamlessly and securely move data anywhere you need it to support model training, fine-tuning, inference, optimization, and powerful use cases like retrieval-augmented generation (RAG).
AI-ready multicloud infrastructure at your fingertips
Of course, the quickest way to realize your multicloud AI ambitions is to use tried and true solutions from trusted providers. F5 solutions complement the AWS services you’re likely already using—like Amazon Bedrock for managed AI models or Amazon SageMaker for building your own AI—and provides the connective tissue that links multicloud environments together into a unified infrastructure.
- F5 Global Network delivers high-performance, secure, private connectivity between environments without public Internet exposure. Multiple points of presence globally help keep your data local for fast response times.
- F5 AI Gateway enables intelligent traffic management with advanced load balancing for containerized AI. Integrated security controls and input/output validation also protect AI models from prompt injection and other abusive tactics that result in data leaks.
- F5 Distributed Cloud Services provide top-down visibility and control, with the ability to connect cloud, edge, and on-premises environments through one-click deployments. These services make it easy to apply IT and data governance policies consistently across all environments.
For NetApp customers, F5 and AWS also work with NetApp storage solutions including NetApp BlueXP and NetApp ONTAP, giving you access to all of these unified data management and migration capabilities to enable and enhance your multicloud AI projects.
RAG and edge are having their moments
RAG uses an organization’s proprietary data to boost model accuracy with domain-specific knowledge, and it’s become a de facto requirement for all but the most generic chatbots. However, because it’s proprietary data that’s being used, the data is more valuable and more vulnerable. F5 Distributed Cloud Network Connect safeguards this data as it moves through hybrid multicloud environments, with secure layer 3 connectivity and support for Amazon S3 protocols.
The network edge also has high potential to enhance AI by processing and inferencing data closer to where it’s generated, for faster results and lower latency. F5 Distributed Cloud Customer Edge makes it easier to securely connect edge locations to centrally managed models in Distributed Cloud Services. This solution also pairs with AI Gateway, so you can bring Kubernetes-based AI services and security to the edge, and it works with edge services like AWS Local Zones and AWS Outposts.
Take the complexity out of hybrid multicloud AI
Hybrid multicloud AI is complex, but it offers many advantages by making numerous data repositories and services available to your AI models, and vice versa, wherever you need them. The partnership between F5 and AWS addresses multicloud challenges by providing AI-specific security, unified management tools, and AI connectivity through a global network infrastructure. With these solutions, you can build the secure, flexible, and scalable AI you need to thrive in today’s AI economy.
Learn more at F5 on Amazon Web Services (AWS).
Also, be sure to check out my previous blog posts:
Protect AI innovation on AWS with API security
Build fast, secure data pipelines for AI training on AWS
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