From Cloud-Native to AI-Native: The Infrastructure Evolution

Designing infrastructure for traditional cloud-native applications has always focused on scalability, flexibility, and resilience. These systems are built to handle transactional workloads with predictable data flows, using microservices and container orchestration to scale effortlessly. But as AI reshapes entire industries, we’re facing a new reality—one where cloud-native solutions aren’t enough. AI demands a new approach, a rethinking of how we handle massive data and compute needs.

The AI Data Pipeline: New Challenges, New Solutions

Cloud-native systems have advanced significantly, with tools like Kubernetes and cloud-based AI services enabling the handling of many data workloads. However, AI pipelines, particularly those involving unstructured data, introduce challenges for traditional cloud-native solutions.

Imagine a project requiring automated labeling of a large backlog of images for quality control, where no metadata exists. While cloud-native tools can process structured data efficiently, AI models must analyze raw images directly, creating labels from scratch. This requires high-throughput access to large datasets and real-time processing that stretches cloud-native infrastructure to its limits.

AI-native systems, designed for these demands, minimize latency and data movement, allowing for faster, more efficient processing. While cloud-native environments handle structured data well, AI pipelines often need infrastructure built specifically to manage unstructured data at scale. Every extra copy and transfer eats into performance, adds cost, and introduces delays that cloud-native architectures were never designed to handle.

Data’s Influence on Compute: Preparing for AI

Cloud-native environments are optimized for short, stateless operations. They scale horizontally to handle traffic spikes, but these systems are built for lightweight, transactional tasks. AI changes the game. AI models need massive amounts of data and compute power, often with specialized hardware like GPUs, which excel at the kind of parallel processing AI requires.

Imagine an enterprise running multiple AI models to analyze vast amounts of unstructured data. These models aren’t necessarily being trained from scratch but are instead being used to generate vector embeddings for semantic search or to label data for further processing. In this case, compute resources must be tightly integrated with storage to handle large-scale, parallel data processing. Each model relies on high-throughput access to raw data, quickly transforming it into structured, searchable insights.

Security and Data Provenance: A New Priority

In AI workloads, security isn’t just about protecting data in transit or at rest. It’s about ensuring the integrity of the data itself. For AI models, the quality and trustworthiness of the data used during training are critical. If you can’t verify where your data came from or how it’s been transformed, your AI model risks being biased or producing unreliable results.

That’s why data provenance—being able to trace every step of a dataset’s journey—is so crucial in AI. AI workloads need an infrastructure that tracks, audits, and verifies every piece of data, ensuring that it hasn’t been altered in ways that would compromise the model’s integrity. In traditional cloud-native systems, this kind of deep data tracking isn’t built in. AI requires a higher standard, and infrastructure that can maintain trust every step of the way.

The Necessity of New Infrastructure

AI workloads have changed the way we need to think about infrastructure. Traditional cloud-native systems aren’t equipped to handle the vast data volumes, high-speed processing, and rigorous security demands that AI workloads require. The VAST Data Platform was designed with AI in mind. It’s built to handle the unique challenges of AI infrastructure—low-latency, high-throughput data access, tightly integrated compute, and rigorous data provenance.

With VAST, data moves less, costs stay lower, and AI models have the data they need, right when they need it. It’s not just about scaling for the future—it’s about building infrastructure that empowers AI to thrive today.

Learn more about VAST’s approach to solving AI data pipeline efficiencies in a new analyst report from Steve McDowell of NAND Research. For enterprises implementing AI and service providers supporting AI initiatives, proactively addressing these challenges is essential.