Why cloud-Native AI is the future of enterprises

Cloud computing is undergoing a major change thanks to the emergence of artificial intelligence (AI), which is transforming how businesses operate. Traditional cloud-native infrastructure, designed for transactional workloads with predictable data flows, is proving insufficient for the AI-driven future. And this is where cloud-native AI is playing a pivotal role, believe experts.

From cloud-native to AI-native

Cloud-native leverages containers, service meshes, microservices, immutable infrastructure, and declarative APIs to build scalable technology environments.

Dave Pearson, Research Vice President, Infrastructure Systems, Platforms and Technologies Group at IDC, explains that the rapid advancement of AI/ML (Machine Learning) demands infrastructure capable of handling massive workloads, including the unlimited storage and computational power needed for trillion-parameter models, along with on-demand scalability. Data security is also paramount due to the sensitivity of training data. While on-premises infrastructure offers control, it entails significant upfront costs and ongoing resource management.

“Cloud-Native AI addresses these challenges by enabling automated deployment and scaling of secure cloud infrastructure, allowing AI engineers to focus on model development to handle massive data and computing needs,” he said.

Cloud-native applications, optimised for cloud environments, utilise microservices architectures for scalability and flexibility. Tools like Kubernetes manage container deployment and scaling, while DevOps practices, CI/CD enhance agility. However, as Pearson pointed out, AI pipelines, especially with unstructured data, challenge these systems. He believes unlike cloud-native's proficiency with structured data, AI often requires direct analysis of raw data, creating a demand for high-throughput access and real-time processing that strains existing infrastructure.

This necessitates AI-native systems designed to minimise latency and data movement for efficient processing of unstructured data at scale. Matt Kimball, VP & Principal Analyst, Datacenter compute, storage, AI chips, edge, CI at Moor Insights & Strategy said, “While cloud-native focuses on lightweight, stateless operations and horizontal scaling, AI requires massive data and compute power, often leveraging specialised hardware like GPUs.” Consider enterprises using AI models for semantic search or data labelling. These models demand tight integration of compute and storage for large-scale, parallel data processing, needing high-throughput access to raw data for rapid transformation into structured insights.

Moreover, Kimball said that AI workloads raise critical security concerns beyond data protection in transit or at rest. “Data integrity and trustworthiness are paramount. Verifying data provenance – tracing a dataset's entire journey – becomes crucial to prevent bias and unreliable results. AI workloads need infrastructure that tracks, audits, and verifies data, ensuring it hasn't been compromised, a level of data tracking not typically built into cloud-native systems,” he said.

The future is Cloud native AI

Experts believe AI is accelerating cloud adoption by automating complexities, improving decisions, optimizing costs, and reducing the need for specialised skills. This lowers barriers for businesses of all sizes to modernise legacy applications in cloud-native environments, fostering innovation and growth.

Paul Nashawaty, Practice Leader and Lead Principal Analyst at The Futurum Group stated that the future points towards autonomous, self-optimising cloud systems that intelligently anticipate and react to application and user needs without manual intervention. These systems will dynamically allocate resources, remediate security risks, and even evolve application logic.

“Integrating AI/ML with cloud-native architectures will also fuel innovation in serverless computing, edge intelligence, and multi-cloud orchestration. Addressing data privacy and explainability will unlock further automation in critical sectors like healthcare and finance,” said Pearson.

He cautioned that the convergence of AI/ML and cloud-native represents a fundamental shift in how businesses build, scale, and deliver value. “While operational, cultural, and technical challenges remain, the potential for agility, innovation, and competitive advantage is substantial,” he said.

That said, organisations investing in AI/ML and cloud-native convergence will lead the market, developing intelligent applications that redefine user expectations and industry standards.