Understanding AI-native cloud: from microservices to model-serving

5 things you need to know about AI-native cloud

AI is the core technology: In a traditional cloud, AI is an add-on. In an AI-native cloud, every layer—from storage to networking — is designed to handle the high-throughput, low-latency demands of large models.
GPU-first orchestration: AI-native clouds prioritize GPUs and TPUs. This requires advanced orchestration tools like Kubernetes for AI to manage distributed training and inference economics
The vector foundation: Data modernization is the price of entry. AI-native clouds rely on vector databases to provide long-term memory for AI models, allowing them to access proprietary enterprise data in real-time without hallucinating
Rise of neoclouds: 2026 will seei the rise of specialized neocloud providers (like CoreWeave or Lambda) that offer GPU-centric infrastructure that hyperscalers are often struggling to match in terms of raw performance and cost.

From AIOps to agenticops: The goal isn’t just a faster system; it’s a self-operating one. AI-native cloud allows for agentic AI can autonomously manage network traffic, resolve IT tickets, and optimize cloud spend.

Cloud computing has fundamentally transformed the way enterprises operate. Initially built for more basic, everyday computing tasks, its capabilities have expanded exponentially with the advent of new technologies (such as machine learning and analytics).

But AI — particularly generative AI and the emerging class of AI agents — presents all-new challenges for cloud architectures. It is resource-hungry, demands ultra-fast latency, and requires new compute pathways and data access. These capabilities can’t simply be bolted on to existing cloud infrastructures.

Simply put, AI has upended the traditional cloud computing paradigm, leading to a new category of infrastructure: AI-native cloud.

Understanding AI-native cloud

AI-native cloud, or cloud-native AI, is still a new concept, but it is broadly understood as an extension of cloud native. It is infrastructure built with AI and data as cornerstones, allowing forward-thinking enterprises to infuse AI into their operations, strategies, analysis, and decision-making processes from the very start.

Differences between AI-native and traditional cloud models

Cloud computing has become integral to business operations, helping enterprises scale and adopt new technologies. In recent years, many organizations have shifted to a ‘cloud native’ approach, meaning they are building and running apps directly in the cloud to take full advantage of its benefits and capabilities. Many of today’s modern applications live in public, private, and hybrid clouds.

According to the Cloud Native Computing Foundation (CNCF), cloud native approaches incorporate containers, service meshes, microservices, immutable infrastructure, and declarative APIs. “These techniques enable loosely coupled systems that are resilient, manageable, and observable,” CNCF explains.

AI-native cloud is an evolution of this strategy, applying cloud-native patterns and principles to build and deploy scalable, repeatable AI apps and workloads. This can help devs and builders overcome key challenges and limitations when it comes to building, running, launching, and monitoring AI workloads with traditional infrastructures.

The challenges with AI in the cloud

The cloud is an evolution of legacy infrastructures, but it was largely built with software-as-a-service (SaaS) and other as-a-service models in mind. In this setting, AI, ML, and advanced analytics become just another workload, as opposed to a core, critical component.

But AI is much  more demanding than traditional workflows, which, when run in the cloud, can lead to higher computing costs, data bottlenecks, hampered performance, and other critical issues.

Generative AI, in particular, requires:

Specialized hardware and significant computational power

Infrastructure that is scalable and flexible

Massive and diverse datasets for iterative training

High-performance storage, high bandwidth and throughput, diverse data sets, and low-latency access to data

AI data needs are significant and continue to escalate as systems become more complex; data must be processed, handled, managed, transferred, and analyzed rapidly and accurately to ensure the success of AI projects. Distributed computing, parallelism (splitting AI tasks across multiple CPUs or GPUs), ongoing training and iteration, and efficient data handling are essential — but traditional cloud infrastructures can struggle to keep up.

Existing infrastructure simply lacks the flexibility demanded by more intense, complex AI and ML workflows. It can also fragment the user experience, meaning devs and builders have to move back and forth between numerous interfaces, instead of a unified plane.

Essential components of AI-native cloud

Rather than the traditional “lift and shift” cloud migration strategy — where apps and workloads are quickly moved to the cloud “as-is” without redesign — AI-native cloud requires a fundamental redesign and rewiring of infrastructures for a clean slate.

This refactoring involves many of the key principles of cloud-native builds, but in a way that supports the development of AI applications. It requires:

Microservices architecture

Containerized packaging and orchestration

Continuous integration/continuous delivery (CI/CD) DevOps practices

Observability tools

Dedicated data storage

Managed services and cloud-native products (like Kubernetes, Terraform, or OpenTelemetry)

More complex infrastructures like vector databases

Data modernization is critical for AI; systems require data flow in real time from data lakes, lakehouses or other stores, the ability to connect data and provide context for models, and clear rules for how to use and manage data.

AI workloads must be built in from the start, with training, iteration, deployment, monitoring, and version control capabilities all part of the initial cloud setup. This allows models to be managed just like any other service.

AI-native cloud infrastructures must also support continuous AI evolution. Enterprises can incorporate AIOps, MLOps, and FinOps practices to support efficiency, flexibility, scalability, and reliability. Monitoring tools can flag issues with models (like drift, or performance degradation over time), and security and governance guardrails can support encryption, identity verification, regulatory compliance, and other safety measures.

According to CNCF, AI-native cloud infrastructures can use the cloud’s underlying computing network (CPUs, GPUs, or Google’s TPUs) and storage capabilities to accelerate AI performance and reduce costs.

Dedicated, built-in orchestration tools can do the following:

Automate model delivery via CI/CD pipelines

Enable distributed training

Support scalable data science to automate ML

Provide infrastructure for model serving

Facilitate data storage via vector databases and other data architectures

Enhance model, LLM, and workload observability

The benefits of AI-native cloud and business implications

There are numerous benefits when AI is built in from the start, including:

Automation of routine tasks

Real-time data processing and analytics

Predictive insights and predictive maintenance

Supply chain management

Resource optimization

Operational efficiency and scalability

Hyper-personalization at scale for tailored services and products

Continuous learning, iteration and improvement through ongoing feedback loops.

Ultimately, AI-native cloud allows enterprises to embed AI from day one, unlocking automation, real-time intelligence, and predictive insights to support efficiency, scalability, and personalized experiences.

Paths to the AI-native cloud

Like any technology, there is no one-size-fits-all for AI-native cloud infrastructures.

IT consultancy firm Forrester identifies five “paths” to the AI-native cloud that align with key stakeholders including business leaders, technologists, data scientists, and governance teams. These include:

The open-source AI ecosystem

The cloud embedded Kubernetes into enterprise IT, and what started out as an open-source container orchestration system has evolved into a “flexible, multilayered platform with AI at the forefront,” according to Forrester.

The IT firm identifies different domains in open-source AI cloud, including model-as-a-service, and predicts that devs will shift from local compute to distributed Kubernetes clusters, and from notebooks to pipelines. This “enables direct access to open-source AI innovation.”

AI-centric neo-PaaS

Cloud platform-as-a-service (PaaS) streamlined cloud adoption. Now, Kubernetes-based PaaS provides access to semifinished or prebuilt platforms that abstract away “much or all” of the underlying infrastructure, according to Forrester. This supports integration with existing data science workflows (as well as public cloud platforms) and allows for flexible self-service AI development.

Public cloud platform-managed AI services

Public clouds have taken a distinctly enterprise approach, bringing AI “out of specialist circles into the core of enterprise IT,” Forrester notes. Initial custom models have evolved into widely-used platforms including Microsoft Azure AI Foundry, Amazon Bedrock, Google Vertex, and others. These provided early, easy entry points for exploration, and now serve as the core of many AI-native cloud strategies, appealing to technologists, data scientists, and business teams.

AI infrastructure cloud platforms (neocloud)

AI cloud platforms, or neoclouds, are providing platforms that minimize the use of CPU-based cloud tools (or eliminate it altogether). This approach can be particularly appealing for AI startups and enterprises with “aggressive AI programs,” according to Forrester, and is also a draw for enterprises with strong and growing data science programs.

Data/AI cloud platforms

Data infrastructure providers like Databricks and Snowflake have been using cloud infrastructures from leading providers to hone their own offerings. This has positioned them to provide first-party gen AI tools for model building, fine-tuning, and deployment. This draws on the power of public cloud platforms while insulating customers from those complex infrastructures. This “data/AI pure play” is attractive to enterprises looking to more closely align their data scientists and AI devs with business units, Forrester notes.

Ultimately, when pursuing AI-native cloud options, Forrester advises:

Start with your primary cloud vendor: Evaluate their AI services and develop a technology roadmap before switching to another provider. Consider adding new vendors if they “dangle a must-have AI capability” your enterprise can’t afford to wait for. Also, tap your provider’s AI training to grow skills throughout the enterprise.

Resist the urge of “premature” production deployments: Projects can go awry without sufficient reversal plans, so adopt AI governance that assesses model risk in the context of a particular use case.

Learn from your AI initiatives: Take stock of what you’ve done and assess whether your technology needs a refresh or an “outright replacement,” and generalize lessons learned to share across the business.

Scale AI-native cloud incrementally based on success in specific domains: Early adoption focused on recommendation and information retrieval and synthesis; internal productivity-boosting apps have since proved advantageous. Start with strategy and prove that the technology can work in a particular area and be translated elsewhere.

Take advantage of open-source AI: Managed services platforms like AWS Bedrock, Azure OpenAI, Google Vertex, and others were early entrants in the AI space, but they also offer various open-source opportunities that enterprises of different sizes can customize to their particular needs.

Conclusion

AI-native cloud represents a whole new design philosophy for forward-thinking enterprises. The limits of traditional cloud architectures are becoming increasingly clear, and tomorrow’s complex AI systems can’t be treated as “just another workload.” Next-gen AI-native cloud infrastructures put AI at the core and allow systems to be managed, governed, and improved just like any other mission-critical service.