Chapter 6:
Case studies: The Kubernetes strategy in action
The theoretical benefits and TCO advantages of running open source databases on Kubernetes come to life through real-world implementation. The following case studies are evidence. Grouped by strategic business driver, they demonstrate how organizations are using a Kubernetes-based data platform to solve critical challenges related to cost, agility, and scale.
Theme 1: Driving cost efficiency and eliminating vendor lock-in
For many organizations, the primary driver for modernizing the data layer is the need to escape the compounding costs and architectural constraints of proprietary platforms.
Case study:
Minsait
Global digital transformation firm Minsait needed to migrate a tier-one telecommunications client's on-premises MongoDB deployment to Google Cloud Platform. The client’s primary objectives were to achieve significantly lower costs than their on-premises infrastructure and to retain complete control over the environment to avoid vendor lock-in with proprietary cloud services. Instead of defaulting to a managed DBaaS offering, Minsait conducted an architectural assessment that identified a Kubernetes-native approach as the optimal path.
Partnering with Percona, Minsait architected and deployed a new MongoDB environment on Kubernetes, leveraging the Percona Operator for MongoDB to automate the entire database lifecycle. For the initial six months post-launch, Percona provided managed services to stabilize the new environment while executing a comprehensive knowledge transfer to the client's internal IT team.
Key outcome: The Kubernetes-based approach delivered a dramatically lower infrastructure cost compared to DBaaS alternatives by eliminating vendor markups on compute and storage. The client retained full control of their data and architecture, ensuring the platform remains portable to other clouds or back on-premises without vendor-specific dependencies.
The strategic implication: Minsait's experience clearly validates the TCO model presented in Chapter 5. It demonstrates that the short-term convenience of a proprietary DBaaS comes at the direct expense of long-term financial control and architectural freedom. For CIOs managing cloud migration budgets, this case proves that a Kubernetes-based open source strategy is not just viable but financially superior.
Theme 2: Accelerating innovation and time-to-market
The primary goal for growth-focused companies is speed. A modernized data platform must directly enable developer velocity, allowing the organization to launch new products and features faster than the competition.
Case study:
Civo - Accelerating a new service launch
Civo, a Kubernetes-native cloud service provider, aimed to launch a new commercial MySQL DBaaS offering for its customers. Building a custom operator from scratch would consume significant engineering time and delay time-to-market, while community options lacked the enterprise-grade reliability needed for a customer-facing service with an SLA.
Civo chose to build its service on the enterprise-grade Percona Operator for MySQL. Percona's engineers worked with the Civo team to configure the operator for their specific multi-tenant architecture. The operator handled all the complex Day 2 automation—including provisioning, automated backups, failure recovery, and scaling—allowing Civo to focus its engineering efforts on its core cloud platform.
Key outcome: Civo launched its new DBaaS offering significantly faster than if it had built the automation in-house. They were able to enter the market and begin generating revenue sooner by building on a proven, reliable, and open source foundation.
The strategic implication: Civo’s story highlights the opportunity cost of "building vs. buying" automation. By leveraging an enterprise-grade open source operator, they treated the database automation layer as a component to be integrated, not a product to be built, allowing them to focus 100% of their development resources on their unique value proposition. This is a crucial lesson for any organization looking to accelerate product delivery.
Case study:
Kontron - De-risking a Greenfield Project
European IoT leader Kontron was developing a private 5G network core from scratch, a greenfield project that required using MongoDB, a database technology with which their team had limited experience. The primary risk was that architectural missteps in the data layer could lead to costly delays for the entire high-stakes project.
Kontron chose to de-risk the project by adopting a Kubernetes-native architecture from day one and partnering with database experts. Working with Percona engineers, they designed and deployed their MongoDB cluster, receiving direct guidance on architecture, replication, and high availability. The implementation used the Percona Operator for MongoDB to automate cluster deployment and codify operational best practices.
Key outcome: Kontron successfully deployed its MongoDB infrastructure without the extended trial-and-error cycle often associated with new technology adoption. This accelerated their development timeline and ensured the foundational data layer for their 5G product was reliable from the start.
The strategic implication: Kontron’s experience demonstrates how a Kubernetes and operator model, combined with expert guidance, can de-risk innovation. Instead of spending months on internal R&D, they effectively outsourced the operational best practices for a new technology, allowing their team to learn by doing on a production-ready foundation.
Theme 3: Achieving operational excellence at scale
For large enterprises or platform providers, the central challenge is managing complexity and scale efficiently. The goal is to support a growing and diverse set of database needs without a corresponding linear growth in specialized headcount.
Case study:
Nokia - Scaling DBaaS on a 700,000-core private cloud
Nokia operates one of the world's largest private cloud infrastructures, with over 700,000 cores. As their platform matured, internal teams demanded self-service database access. The challenge was to provide this service across multiple database technologies without hiring armies of specialized DBAs, which would violate their operational efficiency model.
After a multi-month internal evaluation, Nokia chose to build its internal DBaaS on Percona Operators running on their bare-metal Kubernetes clusters. The operators provided the consistent, Kubernetes-native automation that covered approximately 80% of their internal database requirements out of the box. Working with Percona's engineers, Nokia fine-tuned the solution for their unique two-tier storage model and workload profiles.
Key outcome: Nokia successfully launched a self-service DBaaS that supports both legacy and microservices applications at massive scale, all while maintaining a lean operational headcount. The standardized platform allows infrastructure to scale faster than the team required to support it.
The strategic implication: Nokia's case proves that the Kubernetes-based open source model is not just for public cloud deployments; it is a powerful strategy for achieving unprecedented operational efficiency in large-scale private cloud environments. Their success provides a blueprint for any large enterprise looking to build a sustainable, internal DBaaS.
Case study:
Lyrid - Building a scalable multi-database platform
Cloud platform provider Lyrid’s manual, single-instance database deployment process had become a major operational drag. It could not scale to meet the high-availability and multi-region demands of larger enterprise customers, creating a direct barrier to growth. The team needed to move from ad-hoc deployments to a standardized, automated "factory" model for databases that could support multiple technologies.
Lyrid built its new database service on Percona's unified software for running databases on Kubernetes. This solution utilizes Percona Operators to manage multiple open source databases through a single, consistent interface, enabling them to standardize their offerings with predefined, code-based templates for various database types and configurations.
Key outcome: Lyrid can now manage all customer database types through one unified, code-driven workflow. This has dramatically reduced deployment time and operational complexity, enabling them to offer the scalable, multi-region architectures required to win larger enterprise contracts.
The strategic implication: Lyrid exemplifies the move toward a platform engineering model. By abstracting the complexity of individual database technologies, they created enormous operational leverage, allowing a small team to deliver a sophisticated, multi-database service at scale.