Enterprises are adopting container solutions to scale generative AI
Generative AI is exceptional, but it’s hungry for power. The traditional IT setup isn’t cutting it anymore. That’s why businesses are moving to containers, a smarter, faster way to deploy AI applications. Think of containers as digital shipping boxes: they neatly package everything an application needs to run, making it easy to move across different environments, public cloud, private cloud, or on-prem data centers. This kind of flexibility is exactly what enterprises need to scale AI without running into infrastructure roadblocks.
We’re seeing this shift happening fast. Nearly 90% of organizations have already started containerizing applications, and more than half have fully embraced containers across all workloads. It’s a massive efficiency upgrade. Kubernetes, the tool that orchestrates these containers, is making AI deployment smoother by automating tasks like scaling and resource allocation. In short, enterprises aren’t just adopting containers, they’re rearchitecting how AI operates in their business.
Generative AI is evolving, and businesses that want to stay ahead need infrastructure that moves as fast as their AI models. Containerization isn’t just an option; it’s the new foundation.
Increased IT infrastructure investments are key to support generative AI workloads
AI is math at scale. And that scale is huge. Training and running AI models demands enormous computing resources, and most companies simply don’t have enough. That’s why more than half of enterprises recognize the need to increase IT investments to keep up. Without this, AI adoption will stall before it ever reaches production.
Businesses are struggling to integrate AI workloads into their existing setups because these workloads demand more processing power, better data flow, and stronger security measures. Regulatory concerns also add another layer of complexity. Investing in the right infrastructure means fewer bottlenecks, faster AI deployment, and ultimately, a competitive edge.
“Companies that hesitate on AI infrastructure investment aren’t saving money, they’re falling behind. This is a classic case of adapt or be left behind.”
Hybrid cloud environments are invaluable
Generative AI doesn’t live in just one place. It moves, shifting between environments as it develops, tunes, and operates. That’s why hybrid cloud, combining public and private cloud resources, is becoming the default strategy for enterprise AI.
Here’s how it works: AI model training starts in the public cloud, where computing power is virtually unlimited. Once trained, models are fine-tuned and secured in private cloud environments, making sure sensitive data stays protected. Finally, when real-time responses are needed (think AI-powered chatbots or predictive analytics), inferencing happens at the edge, closer to the user, reducing latency and improving speed.
Lee Caswell, SVP at Nutanix, put it simply: AI is naturally a hybrid cloud workflow. You can’t confine it to one infrastructure. Containers make this whole process seamless, letting AI applications move between environments without breaking.
For enterprises, the message is clear: if your AI strategy isn’t hybrid, it’s incomplete.
Containerization is becoming the de facto standard
A few years ago, containers were mostly a public cloud thing. Not anymore. Today, they’ve become the standard for deploying AI workloads, whether on-prem, in private clouds, or across hybrid environments. The reason? AI needs scalability, reliability, and portability, and containers deliver all three.
Unlike traditional applications, AI models don’t just sit in one place. They need to move across different infrastructures while staying consistent. That’s exactly what containers do. They provide a structured, modular way to deploy AI, making sure that no matter where a workload runs, it operates efficiently. Kubernetes takes it further, automating deployment, scaling, and management, reducing the complexity of handling AI applications.
According to Gartner, more than 75% of AI deployments will be containerized by 2027, up from just 50% today. This shift is a direct response to the massive computing demands of AI. Companies that embrace containerization will scale AI faster, cut costs, and gain an operational advantage. Those that don’t? They’ll struggle to keep up.
Containerization is already the present. The sooner enterprises make the switch, the better positioned they’ll be in the AI-driven world.
Key executive takeaways
- Containerization drives scalability: Enterprises are using container solutions to deploy generative AI, leading to smooth transitions across public, private, and on-premises environments. Decision-makers should prioritize investment in container orchestration tools like Kubernetes to maintain operational agility and efficiency.
- Upgraded IT investments are key: More than half of organizations indicate that scaling AI demands enhanced IT infrastructure to manage increased computational loads and regulatory challenges. Leaders must invest in comprehensive IT systems to support the growing demands of AI workloads and secure data integrity.
- Hybrid cloud strategy is invaluable: Generative AI workflows benefit from a hybrid approach, utilizing public cloud for scalable training and private cloud for secure tuning and inferencing. Executives should craft strategies that integrate both environments to optimize performance and maintain data security.
- Standardization via containerization is emerging: With Gartner forecasting over 75% of AI deployments will use container technology by 2027, containerization is quickly becoming the industry norm. Decision-makers should adopt this approach to streamline operations, reduce costs, and stay ahead of the competition.
