Most organizations now run across multiple clouds, pursuing flexibility, better pricing, or regional availability. But while stateless applications move freely, databases often remain stuck. Each cloud provider offers its own managed database service (e.g., RDS, Cloud SQL, Azure Database) with distinct APIs, automation tools, and monitoring layers. Once you commit to one, moving becomes complicated and expensive.
That’s why so many “multi-cloud” architectures aren’t really multi-cloud at all. The applications may be portable, but the data sure isn’t. Vendor-specific services create invisible walls that make true portability nearly impossible.
Kubernetes changes this by providing consistent infrastructure across environments. It offers a single platform for running databases on any cloud or on-premises hardware, using identical configurations and automation workflows. Add Kubernetes operators, and the model becomes practical. Databases can deploy, scale, and recover anywhere with the same reliability you’d expect from a managed service.
That’s what real portability means: one architecture that runs wherever you need it, without starting over each time.
What true database portability means
Database portability means keeping behavior, performance, and management consistent wherever you run. A database is truly portable when it can be deployed, scaled, backed up, and recovered across environments without requiring reconfiguration or relying on provider-specific features.
Your database should behave identically everywhere.
That means using the same configuration files, monitoring stack, and automation workflows across clusters in the cloud or even on-prem. It means backups and restores follow the same process, and failover works the same way, regardless of where the workload lives.
Proprietary Database-as-a-Service (DBaaS) platforms prevent this by design, and each one has its own APIs, monitoring tools, and maintenance routines. Even basic operations can depend on vendor-specific implementations, and those dependencies are what lock you in.
Kubernetes replaces those differences with a consistent operating model. Using declarative configuration, you describe the desired state of your database once and apply it anywhere. Operators and automation then make sure each environment conforms to that definition. When everything is configured this way, portability simply works as part of the system.
Building a Kubernetes multi-cloud architecture
Running databases across multiple clouds starts with one principle: consistency. Every part of the architecture must behave the same way, no matter where it runs. Without that consistency, portability becomes a manual effort and troubleshooting instead of automation and control.
Storage abstraction comes first. Kubernetes uses PersistentVolumes and StorageClasses to connect databases to block or file storage in any cloud. By defining how data is provisioned and attached through Kubernetes rather than a cloud API, you keep that layer portable. StatefulSets maintain database identity across restarts and failovers, so even during scaling or recovery, the cluster behaves predictably.
Network reliability is next. Databases rely on low latency and stable connections, so the network between clusters matters. VPNs, service meshes, and private peering can help maintain secure and consistent communication across clouds. This is especially important for replication and high availability, where interruptions can cause data lag or failover drift.
Automation and orchestration bring repeatability to the system. Declarative configuration ensures that database deployments are defined once and applied consistently. Using CI/CD or GitOps workflows makes it possible to roll out changes safely and keep environments in sync, whether you’re updating configurations or patching software.
Observability provides the unified view. Unified monitoring through tools like Percona Monitoring and Management (PMM) keeps metrics consistent across environments. Shared dashboards and alerting enable teams to quickly detect performance or replication issues, without switching between multiple monitoring systems.
Security and compliance must be a consistent layer. Use encryption in transit and at rest, and centralize key management through services like HashiCorp Vault or cloud-native KMS. Apply the same access control and policy enforcement in every cluster so compliance doesn’t break.
Even with these building blocks, portability still needs one more layer: automation that understands the database itself. That’s where Kubernetes operators are essential.
Kubernetes operators: Automating the hard parts
Operators make database portability practical by translating database expertise into automation. They bridge the gap between what Kubernetes manages well (pods, volumes, services) and what stateful databases require (precise, procedural operations).
A good operator handles the repetitive database management tasks that consume DBA time. It understands the precise logic required for a safe minor version upgrade, how to bootstrap a new replica into a cluster, or how to promote a new primary and fence the old one during a failover. It keeps these workflows consistent across clusters and clouds, so running PostgreSQL on AWS looks and behaves exactly like PostgreSQL on-prem or in Azure.
Without operators, multi-cloud database operations often become fragile. Teams end up maintaining scripts and custom controllers for each environment, creating more complexity instead of less. Even simple tasks, such as version upgrades or replica management, can become one-off processes that compromise portability.
Operators solve this fragility. They transform Kubernetes from a general orchestration platform into a complete data operations framework that runs anywhere with confidence. However, the operator ecosystem itself presents a new set of choices, which typically fall into two main categories:
- Proprietary operators simplify deployment but usually come with strings attached. They are tied to commercial licenses or enterprise-only features, limiting flexibility to a single vendor ecosystem.
- Community operators take the opposite approach, offering full freedom but uneven quality. Many handle initial deployment well but fall short on Day 2 operations such as automated backups, upgrades, and monitoring integration. Those gaps lead to manual work and unpredictable reliability.
Percona Operators offer a different approach by combining the best of both worlds: enterprise-level reliability with open source flexibility.
Percona’s open source approach: Portability without compromise
Percona Operators provide a single, open source framework for running PostgreSQL, MySQL, and MongoDB anywhere you deploy Kubernetes. This consistency matters because many teams use a different operator for each database, each with unique CRDs, backup logic, and high availability models. This trades one form of vendor lock-in for operational fragmentation.
With the Percona Operators, your team learns one architectural pattern. The manifest for a 3-node HA PostgreSQL cluster looks and functions just like the one for a 3-node MongoDB replica set. The backup, monitoring, and scaling philosophies are the same, delivering portability for your data, your team’s skills, and your operational processes.
Each Percona Operator includes everything needed for reliable, enterprise-scale database management.
You get automated upgrades and rolling restarts, scheduled backups with point-in-time recovery, and built-in high availability using ProxySQL or HAProxy. Transport and at-rest encryption protect data across all clouds. Native integration with Percona Monitoring and Management (PMM) provides comprehensive visibility into performance, queries, and resource utilization through a single observability layer.
Every Percona Operator is also Red Hat OpenShift certified, ensuring production readiness in regulated and high-availability environments.
Proprietary operators lock some of these features behind license tiers. Percona’s open source Operators provide everything needed for production automation freely. Community operators often handle deployment well but lack enterprise hardening. Percona Operators are tested, supported, and built for production workloads that demand predictable behavior and uptime.
The result is a unified automation model that delivers consistency across clouds, freedom to choose your infrastructure, and full ownership of your operational model, without vendor dependency or hidden costs.
Building your portable data strategy
A true multi-cloud strategy demands a portable data layer, but relying on cloud-specific DBaaS creates operational silos and deep, expensive vendor lock-in.
By adopting a unified, open source set of operators, you build a flexible, cost-efficient data platform that serves your business, not one that locks you into a single vendor’s ecosystem. To see how this approach works in practice and what it means for long-term cost and scalability, explore the resources below.
Learn how to build and operate with full control
Read Take Back Control of Your Databases for detailed TCO models and step-by-step guidance on open source data architectures that work across any cloud.
See how Percona Operators make it possible
Visit the Percona Operators page to explore enterprise-grade automation for MySQL, PostgreSQL, and MongoDB across any environment.
Learn more about Percona Operators
