Platform engineering for hybrid Kubernetes: cost and security optimization

The Kubernetes market continues to grow, solidifying its role in IT landscapes. According to the “Kubernetes Adoption Rate Statistics 2026″ report from fosspost.org, the Kubernetes market is valued at $3.13 billion this year and is projected to reach $8.41 billion by 2031, with a compound annual growth rate (CAGR) of 21.85%. A survey by the CNCF Annual Cloud Native Survey 2025, published in January of this year, found that 82% of container users are already employing Kubernetes in production environments.

Concurrently, hybrid infrastructures, which combine on-premises solutions with public and private clouds, are becoming the standard for large organizations. They offer flexibility but introduce complexities in management, security, and cost control. Platform engineering is an approach that enables not just the implementation of Kubernetes, but also its effective optimization within complex hybrid environments.

Growth of Kubernetes and operational risks in hybrid infrastructures

Kubernetes has become the de facto standard for container orchestration, allowing developers to quickly deploy and scale applications. However, in hybrid infrastructures, this is accompanied by a range of operational challenges. Managing Kubernetes clusters deployed across different platforms (e.g., on-premises, AWS, Azure, Google Cloud) requires unified approaches to configuration, monitoring, security, and access management.

Key risks include:

• Configuration Inconsistency: Discrepancies in settings and security policies for clusters across different environments, leading to vulnerabilities.
• Uncontrolled Costs (FinOps): Unpredictable increases in cloud resource expenses due to a lack of centralized control and optimization.
• Cybersecurity Gaps: Absence of uniform security standards, such as CISA Cross-Sector Cybersecurity Performance Goals, for all components of a hybrid system.
• Operational Complexity: Manual management of heterogeneous environments results in errors, deployment delays, and increased time-to-market.

Platform engineering as a response to rising IT cloud costs

Platform engineering is a discipline focused on creating and maintaining Internal Developer Platforms (IDPs) that offer self-service capabilities for development teams. According to Gartner’s forecast, 80% of large software development organizations will establish platform engineering teams this year (platformengineering.org).

In the context of hybrid infrastructures and Kubernetes, platform engineering addresses the issue of unpredictable IT costs through FinOps approaches:

• Standardization and Automation: Platforms enable the standardization of Kubernetes cluster and application deployments, automating resource provisioning, which eliminates manual errors and reduces downtime. This directly impacts resource utilization efficiency.
• Visibility and Control: Platform engineering tools integrate with cloud providers’ monitoring and billing systems, providing centralized cost visibility. This allows FinOps teams to track resource consumption, identify inefficient configurations, and optimize costs in real-time.
• Policies and Limits: Platforms allow for the implementation of resource usage policies, setting quotas and limits for development teams, preventing overconsumption and uncontrolled cost escalation.
• Resource Optimization: Through automated mechanisms, platform engineering can offer recommendations for optimizing cluster sizing, utilizing spot instances, and other strategies that reduce infrastructure costs without compromising performance.

For example, Softline, as a system integrator, frequently implements such platforms that enable clients to effectively manage their hybrid cloud environments and control costs.

A common mistake: ignoring process adaptation for the platform

A frequent error in implementing platform engineering is the belief that the mere presence of a platform will automatically resolve organizational and process issues. Clients invest in complex tools, expecting increased efficiency, but overlook the necessity of adapting internal processes, training teams, and changing culture. The platform is a tool, not an end in itself.

In practice, without clearly defined goals, revised workflows, and the involvement of all stakeholders (developers, DevOps, SRE, security), a platform can become just another isolated tool that fails to deliver expected value. Platform engineering is not just a technology but also an approach that requires changes in collaboration and responsibility distribution.

Practical Recommendation: Before implementing platform engineering, conduct an audit of current processes, identify bottlenecks, and clearly define the specific problems the platform is intended to solve. Only then should you proceed with selecting tools and their integration, while simultaneously investing in training and organizational culture change.

Optimizing Kubernetes in hybrid environments: practical aspects of platform engineering

Platform engineering provides specific mechanisms for optimizing Kubernetes in hybrid infrastructures:

• Automated Deployment and Management: Utilizing Infrastructure as Code (IaC) and GitOps tools for automated deployment and management of Kubernetes clusters and applications across various cloud providers and on-premises environments. This ensures consistency and reduces the risk of human error.
• Standardization of Configurations and Templates: Creating unified templates for application deployment (e.g., Helm charts) that incorporate best practices for security, monitoring, and scaling. This allows developers to quickly deploy applications without worrying about the underlying infrastructure.
• Centralized Security Policy Management: Implementing Policy as Code tools (e.g., OPA Gatekeeper) to ensure compliance with security policies and regulatory requirements (e.g., CISA Cross-Sector CPG) across all Kubernetes clusters, regardless of their location. This includes managing access control, network policies, and container security configurations.
• Unified Monitoring and Logging: Integrating monitoring systems (Prometheus, Grafana) and logging systems (ELK Stack, Loki) to collect data from all components of the hybrid infrastructure. This provides complete system visibility and enables rapid identification and resolution of issues.
• Self-Service Portals: Developing internal self-service portals that allow developers to independently create new environments, deploy applications, and manage their lifecycle, adhering to established policies and standards.

Companies like Softengi use these approaches to build automation and management solutions for clients working with Kubernetes in hybrid environments.

A typical scenario: ensuring data consistency in the banking sector

Consider a scenario in the banking industry. A bank has a hybrid infrastructure: critical customer data and core banking systems are hosted on-premises due to regulatory requirements, while new microservice applications for mobile banking are deployed in a public cloud on Kubernetes. The challenge lies in ensuring data consistency between on-premises databases and cloud microservices, as well as adhering to cybersecurity and compliance requirements (e.g., PCI DSS, ISO/IEC 27001).

Platform engineering addresses this challenge by:

1. Creating a Unified Data Platform: Deploying a message broker (e.g., Apache Kafka) as part of the internal platform, ensuring reliable replication and synchronization of data between on-premises and cloud environments.
2. Automating Kubernetes Service Deployment: Using standardized Helm charts for deploying microservices, which are automatically configured to work with the message broker and integrate with centralized monitoring systems.
3. Implementing Security and Compliance Policies: Applying Policy as Code to automatically ensure all Kubernetes clusters and applications comply with banking security standards, including API access control and data encryption.
4. Centralized Identity and Access Management (IAM): Integrating Kubernetes with the corporate IAM system for unified access management to resources across the hybrid environment.

In this scenario, solutions built on UnityBase (an open-source low-code platform developed by InBase) can provide data management and business logic crucial for banking systems. Companies like SL Global Service specialize in integrating such complex systems for financial institutions.

Implementing platform engineering in the banking sector not only optimizes Kubernetes utilization but also enhances security, ensures data consistency, and accelerates time-to-market for new products while adhering to regulatory requirements.

Platform engineering readiness checklist

• Strategy: Business goals and KPIs for the platform have been defined (e.g., 30% reduction in deployment time, decrease in configuration-related incidents).
• Infrastructure Audit: An analysis of the current state of Kubernetes clusters has been conducted, identifying configuration discrepancies between on-premises and cloud environments.
• FinOps: A unified resource tagging system has been implemented for all environments; dashboards for real-time cost monitoring have been set up.
• Team: A cross-functional team (DevOps, Security, SRE, developers) has been formed, and a Platform Owner has been designated.
• Standardization: Base Helm charts for typical applications, including security, logging, and monitoring configurations, have been developed and approved.
• Security and Compliance: Policy as Code tools (e.g., OPA Gatekeeper) have been implemented for automated verification of compliance with CISA CPG requirements and internal policies.
• Training: A training plan and internal documentation (knowledge base) have been created for developers on using the self-service portal and standard templates.