Databases Go Cloud-Native: Kubernetes Paves the Way to Resilience and Scalability in the AI Era

Percona’s Bennie Grant offers commentary on databases going cloud-native and how Kubernetes paves the way to resilience in the AI age. This article originally appeared in Insight Jam, an enterprise IT community that enables human conversation on AI.

Containerization has been the backbone of modern application development for some time. By allowing developers to spin up the environments and resources they need and delivering automation-driven increases to performance, productivity, and efficiency, containerization has become the gold standard architecture for enterprise development. However, for a long time, that same architecture was seen as too risky or volatile for stateful applications like databases.

“What if a node fails?” “Can I really trust all that automation?” “What if performance sags?” While not wholly unfounded concerns, the reality is that more and more organizations today are realizing the benefits of containerization—especially using open source solutions such as Kubernetes (a.k.a. K8s)—far outweigh the potential risks.

In fact, in the 2024 Data on Kubernetes (DoK) Community Report, databases ranked as the number one workload on Kubernetes for the third year in a row. Nearly half of organizations now run 50% or more of their database workloads in production on Kubernetes, with the most advanced running over 75% in production.

Organizations Double Down on Cloud-Native Database Deployments 

This trend is only accelerating as highly demanding and dynamic AI workloads become a larger and more significant part of the average enterprise data footprint. In this new reality, systems like Kubernetes offer unparalleled flexibility, resilience, and scalability—all of which are invaluable in today’s data landscape.

At the same time, open source solutions like Kubernetes are enabling more on-premises and hybrid deployments, providing organizations with more flexibility and control over their data infrastructure. With Kubernetes as the foundation, databases can now run close to users, across geographies, or within regulated on-premise environments—all with minimal effort and without the need to build and rebuild data pipelines.

Modernizing the database layer is ultimately about enabling the business to innovate without being constrained by brittle infrastructure or manual processes. Organizations that adopt Kubernetes-native architectures will be better positioned to deliver reliable, cost-effective, and flexible AI systems as the landscape continues to accelerate.

Where Traditional Approaches Fall Short and How Cloud-Native Closes the Gap

Traditional database deployments rely on a couple of assumptions that are becoming increasingly unrealistic in today’s environment. First, they assume stability, or the notion that architectures will remain relatively constant, as will processes and pipelines. Second, is that manual oversight will be involved in most (if not all) aspects of database management and administration.

But in a world of ephemeral containers, multi-region failover, and API-driven provisioning, this approach simply isn’t tenable. The volume, variety, and speed of modern data operations are such that traditional deployments are quickly becoming problematic. As a result, enterprises are turning to cloud-native architectures to help overcome a variety of challenges and achieve a wide range of benefits, including:

• Increased Scalability: Real-time, on-demand scaling and resizing of databaseresources based on given workloads or cost constraints.
• Enhanced Resilience: Streamlining operations through things like automated failover and recovery, requiring minimal human intervention.
• Improved Portability: Unified deployment models across clouds, regions, and edge environments allow for unprecedented agility and flexibility.

More generally, tools like Kubernetes allow organizations to encode operational expertise into software rather than relying on human intervention. Once these rules are defined, Kubernetes can apply them consistently across environments, ensuring continuity and efficiency as one’s database estate diversifies and expands.

K8s Unlocks Core Benefits for Today’s AI-Driven Database Environments

While cloud-native architectures offer benefits regardless of workload, there are certain considerations that make Kubernetes and containerization ideally suited for AI. AI workloads introduce a level of resource volatility that makes traditional capacity planning and cost management approaches ineffective. Training cycles, feature extraction, embedding generation, and vector search can all trigger sudden spikes in CPU, memory, and storage utilization. Environments that rely on static provisioning frequently end up either overprovisioned or under provisioned, both of which increase costs and operational risk.

Kubernetes-native architectures provide a dynamic foundation for managing this volatility. Autoscaling mechanisms allow systems to expand and contract in response to real-time conditions, ensuring that resources are consumed only when required. Quotas and limits provide guardrails that prevent individual workloads from exhausting cluster capacity or creating cascading failures. These automated controls provide a level of predictability that is difficult to achieve in manually operated environments.

Elastic scaling also provides a major leg up for managing unpredictable AI-driven demands. AI workloads rarely follow steady consumption patterns. Ingestion spikes, training cycles, and vector search operations can generate rapid shifts in resource utilization. Kubernetes allows clusters to scale horizontally or vertically as needed. This ensures databases can respond to changing workload intensity without sacrificing performance or stability.

Finally, the visibility Kubernetes brings through continuous telemetry is a game-changer in the age of AI. Metrics on utilization, latency, I/O behavior, and workload patterns allow teams to assess not only what the database is doing but how efficiently it is doing it. With these insights, organizations can right-size deployments, adjust autoscaling rules, and refine resource allocations.

Kubernetes & Cloud-Native Deployment Paves the Way for Tomorrow’s Data Landscape

By embracing Kubernetes and cloud-native principles, enterprises unlock a new class of operational efficiency. They stand to gain operational agility, with less friction experienced in spinning up and tearing down environments.

Meanwhile, automated tuning and elastic scaling help to contain costs and provide greater control over one’s database environment. Ultimately, the cloud-native approach redefines databases not as static infrastructure, but as programmable, composable services that evolve with the pace of business.

As AI-driven, multi-database environments continue to evolve and become the norm for modern enterprises, efficiency, flexibility, and control are paramount for database operations. While some may continue to drag their feet, it is only a matter of time before Kubernetes and cloud-native architectures will become standard practice across industries.