AI containerisation drives agile, scalable digital infra shift

By Daryush Ashjari

We have all had or made lunchboxes. But why is the traditional lunchbox such a favourite? It is an ingenious way of bringing order to chaos, just like containerisation brings order to AI models.

A lunchbox, separated by different compartments, ensures flavours do not get mixed up, the food stays fresh, and it can be carried anywhere. Moreover, depending on how hungry a person is, one can easily add or remove containers from their lunchbox.

Similarly, in application containerisation, each application and its dependencies are packaged into separate containers isolated from each other. Organisations that have their applications running on the right platform can easily deploy, update, or move these containers across different environments, just like bringing a lunchbox from home to the office without worrying about compatibility or scaling issues.

AI Boom Drives Demand for Containers

The rise in AI during the last few years has created new opportunities for organisations. IDC found that more than three in four APAC organisations recognise the importance of modern cloud architectures, including containerisation, to optimise infrastructure, applications, networks, and costs.

This is why containers are critical in supporting AI models. With AI-based applications continuing to grow, organisations need to rely on containers that enable them to deploy these applications at scale, whether across public or private clouds, at the edge, or across data centres. This happens seamlessly, with the flexibility to move items between these diverse environments throughout the application lifecycle.

Nutanix’s 2025 Enterprise Cloud Index shows that one in two global organisations has containerised all their applications. However, it is significant to note that APAC organisations lag (16%) behind the rest of the world in containerising their applications, with both America and EMEA standing at the 7% level.

A clear indication of the increased reliance on containerised AI applications is that in recent years, spending on AI-related workloads, specifically AI life-cycle and text and media analytics, running on containers has doubled in the last five years.

In a highly competitive landscape, where customer expectations and data proliferation necessitate AI innovation, containerisation is no longer just a choice but a norm. It also makes a business case; APAC’s application container market is projected to reach around USD 10.7 billion by 2030, registering a CAGR of 34.4% from 2023.

It is Time to Accelerate Container Adoption

The data shows that containerisation is crucial for driving efficiencies in AI-based infrastructure. Its popularity is primarily due to its ability to provide better security, more efficient resource utilisation, faster deployment, and easier management, among other benefits.

While this sounds exciting, the APJ region risks losing out on the AI race if it does not embrace containerisation quickly.

In data-driven industries like streaming, giants like Netflix and Spotify manage vast content libraries to ensure uninterrupted user access. With 300 million subscribers and 675 million listeners, respectively, the two platforms employ containers for efficient content management.

Spotify hosts over 100 million tracks, six million podcasts, and 350,000 audiobooks. Both companies use containers to enhance user experiences. Netflix even developed Titus, an open-sourced container platform, to support video streaming, recommendations, machine learning, and big data operations.

Just like preparing a lunchbox, any containerisation strategy requires careful planning. By fostering a DevOps culture, organisations can create a more agile, efficient, and collaborative environment well-suited for managing containerised applications.

Kubernetes: A Container Backbone for the Win

In addition to adopting containerisation, organisations also need a robust container orchestration platform. Kubernetes has emerged as the de facto standard for managing containerised applications, providing powerful tools for automating deployment, scaling, and operations. By adopting Kubernetes, organisations can streamline their containerisation efforts and ensure they have the infrastructure to support large-scale AI deployments.

Karnataka Bank in India has embraced tech innovation to meet the needs of a digital-savvy customer base in a crowded and competitive market. The bank aims to offer faster, user-friendly digital services by streamlining its IT infrastructure. Containerised cloud-native apps managed by Kubernetes helped fulfil the operational and regulatory demands, providing a digital wallet to customers, ensuring a seamless and reliable user experience.

Just like no two lunchboxes look the same, there are endless possibilities and considerations in developing a modernised IT infrastructure. However, the current AI application boom has taught us that organisations embracing robust solutions like containers and Kubernetes are primed to succeed in an increasingly data-rich ecosystem.

The author is the Chief Technology Officer and Vice President of Solution Engineering for the Asia Pacific and Japan region at Nutanix.