AI Meets Storage: Comparing On-Prem, Cloud, and Hybrid Architectures Across the AI Lifecycle

In today’s evolving IT landscape, selecting the right storage architecture is critical for optimal performance, scalability, data governance, and cost-efficiency. Furthermore, AI workloads have uniquely influenced how we meet these demands from our storage infrastructure. This webinar provides a technical deep dive into three fundamental storage deployment models – on-premises, cloud, and hybrid – examining their architectures and operational trade-offs through the lens of two key concepts: indirection (accessing data through mapping layers that provide flexibility and abstraction) and redirection (rerouting data requests to enable failover, load balancing, and optimized performance). We are going to take some of the key stages of AI lifecycle development as sample use-cases (such as data ingestion, preparation, training, inferencing, and retrieval) and compare how each storage model can serve these use-cases across varying access patterns, data volumes, and performance requirements. Attendees will gain a practical framework for aligning AI workloads with the most suitable storage architecture, balancing cost, scalability, and latency. Whether you are building AI infrastructure from scratch or optimizing existing deployments, this session will help you make informed decisions for AI-ready storage. Learning Objectives and Key Takeaways: • Introduction to the 3 different types of storage deployment models – on-prem, cloud, and hybrid • Trade-offs for each deployment model • Importance of indirection and redirection • Understand how AI-specific data types and access patterns (e.g., embeddings, checkpointing) influence storage performance and design • Evaluate trade-offs in latency, scalability, security, and cost when choosing storage for different stages of the AI pipeline • Gain a decision-making framework for selecting the right storage model based on workload characteristics and infrastructure goals