As a database administrator, you are the guardian of the company’s most critical asset: its data. You live by performance, reliability, and security, ensuring every change maintains uptime and data integrity. That level of precision takes time, as every update, patch, and configuration is tested before it goes live.
Meanwhile, application teams have fully embraced Kubernetes, releasing and scaling new services in minutes.
This creates a two-speed IT model. While applications move fast, database provisioning still depends on tickets and manual processes that can take days or weeks to complete. Because databases are often viewed as too complex or risky for Kubernetes, they’re left to move at a much slower pace. That delay limits developer velocity and slows down business innovation.
Today, that divide is closing. Running databases on Kubernetes is now a proven, production-ready strategy used by enterprises worldwide.
This post provides a practical overview of what it means to operate databases in Kubernetes environments. It covers the risks to manage, the benefits to expect, and the best practices to follow for long-term success.
The (serious) risks: What DBAs need to watch out for
Migrating stateful workloads to Kubernetes requires planning, preparation, and new skills. It isn’t a simple lift-and-shift process, and there are several areas DBAs should evaluate before deploying to production.
For a more detailed look at the trade-offs involved, see Should You Deploy Your Databases on Kubernetes? And What Makes StatefulSet Worthwhile?, which explores both the advantages and challenges of this shift.
1. Skills gap and platform complexity
Kubernetes introduces its own operational model. DBAs must understand PersistentVolumes, StatefulSets, StorageClasses, and how Kubernetes networking handles service discovery and DNS.
This learning curve is real. The 2024 Data on Kubernetes Report found that 35% of organizations cite technical complexity as the top barrier to adoption.
2. Day 2 operations
Deployment is only the first step. Day 2 operations, such as backups, point-in-time recovery, failover, and zero-downtime upgrades, determine whether the environment is production-ready. Reliable backup and restore processes are especially critical.
As shown in Backup Databases on Kubernetes With VolumeSnapshots, using snapshots for data protection within Kubernetes simplifies recovery and reduces operational risk. Production success ultimately depends on automation tools that codify these best practices for recurring operations.
3. Performance and resource contention
Predictable performance is essential for databases. On shared Kubernetes infrastructure, “noisy neighbor” effects can occur when other workloads on the same node compete for I/O, CPU, or network bandwidth. To maintain consistent throughput, DBAs should monitor resource usage closely and isolate critical workloads with defined resource limits and Quality of Service (QoS) settings.
4. Governance and compliance gaps
Kubernetes changes how infrastructure is managed, but databases still operate under strict compliance and data governance requirements. If teams migrate stateful workloads without aligning policies for encryption, access control, and data residency, they risk introducing compliance gaps.
Using Kubernetes-native secrets management and audit logging, together with standardized operators, helps maintain consistent policies across environments. Governance should evolve in tandem with automation, not lag behind it.
The (strategic) benefits: Why it’s worth the effort
The challenges are real, but the rewards are significant. Teams that successfully run databases on Kubernetes achieve faster deployment, greater control, and stronger architectural flexibility across environments.
1. Automation through Operators
Kubernetes operators act as the automation engine for complex database tasks. As outlined in The Criticality of a Kubernetes Operator for Databases, operators are essential for managing stateful workloads at scale, automating everything from provisioning and failover to backups and upgrades.
With a mature operator, DBAs spend less time on manual maintenance and more time improving platform reliability.
2. Portability and freedom from vendor lock-in
A proprietary DBaaS, such as AWS RDS, is convenient… until it’s not. You’re locked into their platform, their pricing model, and their API. With an operator-based approach, your database is fundamentally portable. You can run the exact same PostgreSQL configuration on AWS, Google Cloud, Azure, or your on-premises data center with the same commands. This approach restores control over your architecture and future roadmap.
3. Cost efficiency and transparency
Proprietary DBaaS platforms often bundle hidden markups on compute, storage, and data transfer, making it difficult to predict costs as workloads scale. Running databases on Kubernetes provides teams with full visibility into infrastructure expenses and the freedom to optimize resources on their own terms. This transparency makes budgeting easier and supports long-term cost control.
4. Unified infrastructure and self-service
Running databases and applications on the same platform creates a consistent, automated workflow.
Developers gain self-service provisioning, while DBAs maintain centralized governance and policies for backups, security, and high availability. This model reduces bottlenecks and bridges the traditional divide between data and application teams.
5. Deep observability and performance visibility
Running databases on Kubernetes enables unified monitoring of infrastructure and query performance within the same observability stack. Tools such as Percona Monitoring and Management (PMM) provide end-to-end visibility that helps DBAs identify issues early, optimize performance proactively, and maintain stability across their environments.
Best practices for running a database in Kubernetes
Getting started requires a deliberate, phased approach to ensure long-term success.
Best practice 1: Don’t roll your own; use an enterprise-grade operator
This is the most critical best practice. Managing your own automation for database clustering quickly becomes unsustainable. The Kubernetes ecosystem offers three main types of operators: vendor-proprietary (which lock you in), community-built (which can vary in quality), and enterprise-grade open source.
As detailed in MongoDB Operators Explained: Features, Limitations, and Open Source Alternatives, vendor-specific operators often tie advanced automation to paid enterprise editions, whereas open source alternatives deliver the same functionality without licensing restrictions. A proven, open source operator provides the reliability of a commercial product without vendor lock-in, all while being supported by experts.
Best practice 2: Start with a pilot project (and not your tier-1 database)
Begin with a new, non-critical application or a development/staging environment for your first deployment. Use this as your sandbox to learn the basics, test the operator’s failover behavior, and build a repeatable playbook. This approach builds confidence and internal skills before you deploy tier-1 production workloads.
Best practice 3: Evolve your skills from Admin to Architect
The DBA role isn’t disappearing; rather, it’s becoming increasingly important. Instead of manually provisioning databases, you’re now designing the “database-as-a-service” platform for your entire organization. You define the standards for performance, security, and reliability that automation will enforce at scale.
Best practice 4: Rethink your monitoring
Legacy monitoring tools rarely deliver the visibility Kubernetes environments demand. A developer might see a “pod” running slowly, but you need to understand why. Is it a slow query, a disk I/O bottleneck, or network latency in the Kubernetes service mesh?
Percona Monitoring and Management (PMM), for example, provides query-level insights alongside Kubernetes resource metrics, offering a comprehensive operational view. To see how PMM integrates directly with operators for end-to-end visibility, check out Using Percona Kubernetes Operators with Percona Monitoring and Management.
From bottleneck to enabler
Deploying databases on Kubernetes is a strategic shift. The risks, particularly those related to complexity and the initial skills gap, are real. But they are temporary and solvable. The benefits, including automation, architectural freedom, and a unified development stack, are lasting and measurable.
With the right tools, such as an enterprise-grade operator, and a clear strategy, you can turn the database from a bottleneck into a scalable, self-service foundation that accelerates innovation.
Next steps
The path to cloud-native databases doesn’t end here. Explore the resources below to move from concept to implementation.
See the strategy: Learn how to make the business case and model total cost of ownership in our executive research paper.
Research: Take Back Control of Your Databases
See the tools: Explore the open source tools and operators that power cloud-native databases.
