New research from Rafay Systems illustrates the ever growing role of platform engineering teams as critical drivers of digital transformation.

A platform engineering team is a group within an organisation responsible for building, managing, and optimising the infrastructure and tools that support software development and operations, enabling efficient deployment and scaling of applications across various environments, particularly in cloud-native architectures.

These teams influence pivotal decisions on cloud infrastructure, Kubernetes, and artificial intelligence (AI) technologies. However, as their roles expand, they face increasing challenges in managing costs and complexity.

According to Rafay’s report, 91% of organisations have already established a defined platform engineering team or a group performing similar functions. This number is expected to grow, with 87% of companies without a platform team planning to establish one within the next year.

These teams are responsible for addressing cross-cutting concerns such as security, scalability, and cost optimisation across diverse technology stacks, making them indispensable to successful digital transformation efforts.

Below, we’ll break down the key stats of the Rafay Systems report with regards to Kubernetes, cloud and AI – key technologies for platform engineering teams.

Kubernetes Adoption on the Rise

Kubernetes has become a cornerstone of modern IT infrastructure, and its adoption is set to increase significantly. Kubernetes is an open-source platform that automates the deployment, scaling, and management of containerised applications, making it essential for organisations that manage large-scale, cloud-based applications.

According to the report, nearly all organisations (95%) plan to expand their use of Kubernetes in the coming year, with 71% anticipating an increase in usage between 26% and 100%.

Despite its benefits, the complexity of Kubernetes presents challenges, particularly in the realm of environment provisioning – the process of setting up and configuring the necessary infrastructure, resources, and environments for deploying and managing applications, ensuring that they operate effectively and efficiently in various environments like development, testing, and production. Manual processes and infrastructure dependencies often hinder developers’ ability to deploy applications swiftly.

This has led to widespread dissatisfaction with existing provisioning processes, as 39% of platform teams identify the complexities of managing multi-cloud environments as a top inhibitor to productivity.

According to the report, an automated, self-service environment provisioning solution could significantly reduce these barriers, allowing developers to access Kubernetes-based environments more efficiently and thereby improving the overall developer experience.

Shift to Hybrid and Multi-cloud Architectures

As organisations seek to balance the advantages of on-premises and cloud-based deployments, hybrid cloud is emerging as the preferred mode of operation. Nearly half (49%) of organisations plan to use a hybrid cloud strategy—combining one private cloud with one public cloud—over the next year.

In addition to hybrid cloud, there is a growing interest in multi-cloud architectures, with more than a quarter (28%) of organisations planning to leverage multiple public clouds, with or without a private cloud, in the next year. This marks a significant shift from the current 15% using multi-cloud setups.

The move towards multi-cloud is seen as a proactive response to the complexities of the modern digital functionality, including the growth of generative AI, where diversification of cloud resources is viewed as a strategic advantage.

However, managing costs across these diverse cloud environments remains a significant challenge. Many enterprises struggle to view and report cloud spend across different infrastructures and internal departments.

As a result, there is a renewed focus on FinOps initiatives to accurately allocate Kubernetes cloud spend across clusters. In the coming year, reducing Kubernetes infrastructure costs will be a top priority for platform teams, according to the report, aligning with broader business efficiency goals.

Is AI and GenAI the Next Frontier for Platform Teams?

As cloud and Kubernetes adoption continues to grow, organisations are also investing heavily in AI and generative AI (genAI) capabilities.

The research reveals that 94% of organisations believe it is crucial to provide efficient methods for the development and deployment of AI and genAI applications. Platform engineering teams, having already navigated the complexities of cloud and Kubernetes, are now poised to play a critical role in the adoption of AI and genAI technologies.

Despite the recognised importance of AI, organisations face significant hurdles in its implementation. The majority are still in the early to middle stages of AI adoption, with less than a quarter citing sufficient levels of machine learning operations (MLOps) and large language model operations (LLMOps) implementation.

These challenges are reminiscent of the initial struggles with Kubernetes adoption, indicating that AI operations are following a similar trajectory of gradual integration into enterprise environments.

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The difficulties in AI implementation are particularly pronounced in the areas of experimentation and deployment. Nearly all organisations (95%) that have implemented MLOps report challenges in enabling their developers to experiment with and deploy AI applications.

A similar percentage (94%) face obstacles in deploying genAI applications. To overcome these challenges, organisations recognise the importance of providing self-service access to pre-configured AI workspaces.

In fact, 83% of respondents believe that such self-service capabilities could save their developers and data scientists 10% or more of their time each month, leading to substantial productivity gains.

The research underscores the critical role of platform engineering teams in modern enterprise technology environments. As the boundaries between traditional infrastructure, containerisation, and AI continue to blur, the adaptability and expertise of platform teams become increasingly valuable.

According to the report, by implementing standardised, efficient processes across cloud, Kubernetes, and AI domains, platform engineering teams can drive significant improvements in operational efficiency, developer productivity, and overall business agility.