14 Kubernetes Best Practices You Must Know in 2025

Kubernetes best practices are strategies and guidelines to run Kubernetes efficiently, securely, and while ensuring resilience. Implementing these practices allows organizations to streamline operations, ensure application performance, and enhance resilience against failures. A critical aspect is understanding the nuances of resource management, deployment methodologies, and security protocols that Kubernetes offers, ensuring full utilization of its capabilities.

We’ll briefly review 20 essential Kubernetes best practices, and link to resources where you can read more, get technical details and see examples.

This is part of a series of articles about Kubernetes management

Cluster Management Best Practices 

1. Manage Node Taints and Tolerations to Control Pod Placement

Node taints and tolerations are tools in Kubernetes for controlling where pods are scheduled. Taints prevent certain pods from being placed on nodes unless they have corresponding tolerations, which helps isolate different workloads based on their resource or performance needs. This mechanism ensures that critical applications receive priority and necessary resources, optimizing node utilization and balancing workloads efficiently.

Balancing resource usage with taints and tolerations helps avoid conflicts between different application requirements and enhances overall cluster stability. This management strategy fine-tunes resource distribution, enhancing application performance consistency while avoiding unwanted pod placements on specific nodes due to misconfigurations.

Learn more in our detailed guide to Kubernetes node 

2. Set CPU and Memory Quotas for Namespaces

CPU and memory quotas on namespaces enforce resource limits for applications within a Kubernetes cluster. Assigning quotas prevents any single namespace from monopolizing cluster resources, ensuring fair allocation among various workloads. It enhances predictability in resource consumption, allowing for better planning and scaling of applications based on known availability and constraints.

Establishing limits protects against unforeseen spikes or resource hogging, ensuring cluster stability and efficient resource utilization. This ensures applications can coexist without affecting each other adversely, promoting sustainability and reliability in operations.

3. Utilize Cluster Auto-Scaling

Cluster auto-scaling adjusts the size of the Kubernetes cluster dynamically based on current demand. It ensures that resources are scaled in response to workload needs, optimizing cost-efficiency and ensuring sufficient capacity for applications during peak times. Auto-scaling enhances flexibility, eliminating the need for manual intervention in scaling decisions.

Mechanisms like Cluster AutoScaler provide a responsive framework, adjusting seamlessly to changing conditions while optimizing node usage. It helps in maintaining consistency in performance and availability, reducing resource wastage during low activity periods. This adaptability results in improved operational management, aligning with business objectives by maintaining desired service levels cost-effectively.

4. Test Version Upgrades in Staging Environments Before Rollout

Testing version upgrades in a staging environment ensures that changes are compatible with existing setups before affecting production systems. This proactive testing identifies potential issues, minimizes disruption, and enhances reliability during upgrades. It verifies that applications function correctly with new configurations or updates, which mitigates the risk of unintended downtime or failure after deployment.

Deploying upgrades in a controlled environment enables thorough validation, ensuring that any modification integrates smoothly within existing workflows. This process provides a safety net by allowing for problem identification and resolution in a risk-free context, safeguarding production environments from unforeseen complications.

Learn more in our detailed guide to Kubernetes versions 

Kubernetes Deployment Best Practices 

5. Use Helm Charts for Declarative Deployment of Applications

YAML manifests offer a declarative approach to deploying applications in Kubernetes. Using Helm streamlines deployment processes by defining application architecture, resources, and configurations upfront. This method enhances reproducibility and simplifies version control, ensuring consistent deployments across different environments with minimal manual intervention.

Declarative deployment promotes standardization and reduces the potential for configuration drift by encapsulating application specifications within code. This practice facilitates automated deployment processes, resulting in quicker and more reliable production rollouts, and aligns with infrastructure as code principles, providing an auditable and easily manageable infrastructure landscape.

Learn more in our detailed guide to Kubernetes Helm

6. Leverage GitOps Tools to Automate Deployments from Source Control

GitOps tools automate deployment processes directly from source control repositories, integrating development and operations seamlessly. By leveraging these tools, application deployments become more predictable and are tightly coupled with version control, enabling faster rollbacks if issues arise. This approach enhances collaboration across teams, promoting a continuous deployment cycle.

Automating deployments from source control ensures code changes are systematically propagated, reducing human error. GitOps enables developers to manage infrastructure using a familiar Git workflow, which enhances troubleshooting capabilities and captures operational changes against a unified version history.

7. Monitor Deployment Progress and Roll Back if Issues Are Detected

Continuous monitoring of deployment progress is essential for identifying and addressing issues promptly. Real-time monitoring allows detection of potential problems during deployment, enabling immediate intervention to minimize service disruption. Implementing automatic rollback strategies ensures that applications revert to the last known stable state upon incident detection, maintaining service continuity.

Monitoring assists in capturing metrics associated with deployments, providing insights for improving processes and minimizing downtime. Rollback mechanisms enhance service resilience, allowing systems to recover swiftly from changes that introduce instability.

8. Define Liveness and Readiness Probes for All Workloads

Liveness and readiness probes ensure that Kubernetes workloads are running optimally and are ready to handle traffic. Liveness probes routinely check if applications are functioning correctly, restarting them as necessary. Readiness probes determine whether an application is prepared to accept requests, holding off traffic until initialization is complete.

Probes provide continuous assurance that services are operating as expected, minimizing downtime and preventing unresponsive applications from serving traffic. They foster self-healing capabilities within the cluster, automatically managing the health of workloads, which leads to increased stability and robust performance.

Learn more in our detailed guide to Kubernetes deployment

Kubernetes Configuration Management Best Practices 

9. Use ConfigMaps for Non-Sensitive Configuration Data

ConfigMaps store non-sensitive configuration data, separating it from application code, facilitating easier management and updates without rebuilding container images. By externalizing configuration details, they allow for greater flexibility and reusability, supporting consistent configuration deployment across environments. This enhances development processes by abstracting configurations into dedicated, manageable resources.

Ensuring configurations are stored in ConfigMaps helps maintain a clean separation between code and run-time parameters, aligning with best practices of application management. Reusability and simplification of management lead to reduced operational complexities, as developers can modify configurations without service interruption.

10. Manage Configuration Drift by Auditing Environment-Specific Settings

Regular auditing of environment-specific settings is crucial for managing configuration drift in Kubernetes environments. Constantly verifying configurations ensures consistency across different environments, preventing gradual deviations in settings that can lead to performance degradation or deployment failures. By routinely inspecting configurations, discrepancies are detected early, maintaining alignment with intended configurations.

Auditing assists in validating that changes are both intentional and documented, enhancing control over configuration states. By preventing drift, reliability and predictability in deployments are preserved, reducing troubleshooting time and effort.

Kubernetes Security Best Practices 

11. Control Traffic Flow Between Pods and Services

Network policies in Kubernetes define how pods can communicate with each other and external endpoints, controlling traffic flow at a granular level to enhance security. Enforcing strict policies minimizes unauthorized access, ensuring that only legitimate traffic is allowed, which guards against threats like network-based attacks and data breaches.

Network policies provide an additional security layer that limits exposure to vulnerabilities by isolating workloads. By carefully managing ingress and egress traffic, overall system security is enhanced, safeguarding sensitive data and ensuring compliance with security standards.

12. Use Pod Security Admission for Consistent Security Enforcement

Pod Security Admission is a mechanism that enforces security standards at a pod level across Kubernetes clusters. These tools provide frameworks to define and apply consistent security practices, ensuring that deployed pods adhere to organizational security guidelines, minimizing vulnerabilities related to misconfigurations.

Security enforcement via these methods ensures baseline security compliance, mitigating risks associated with privilege escalation or unauthorized resource access. Centralizing security policies enhances maintenance and auditing processes, providing a robust defense strategy against potential threats, and ensuring uniform application of security measures across cluster environments.

13. Use Seccomp and AppArmor Profiles for Container Isolation

Seccomp and AppArmor profiles restrict kernel features available to containers, enhancing isolation and security by reducing attack surfaces. These profiles confine applications to defined actions, minimizing the risk of exploitation through system calls or unauthorized operations, which strengthens the overall security posture of the Kubernetes environment.

By implementing these profiles, Kubernetes administrators can enforce strict execution policies, enhancing container stability and reliability. Isolation policies prevent containers from affecting host systems or other applications, improving system resilience against exploitation attempts and ensuring a controlled execution environment.

14. Scan Images to Detect Vulnerabilities Before Deployment

Image scanning in Kubernetes detects vulnerabilities in container images before deployment, preventing known threats from entering production environments. Early detection during the build phase allows for remediation of insecure packages or configurations, ensuring that deployments are resilient to exploitation attempts and maintaining compliance with security best practices.

By incorporating image scanning into the CI/CD pipeline, organizations minimize risks associated with vulnerable components in their containers. Automated vulnerability assessments provide continuous feedback, supporting proactive security measures and ensuring that images meet quality assurance standards before entering operational use.

Simplifying Kubernetes Management with Komodor

Komodor is the Continuous Kubernetes Reliability Platform, designed to democratize K8s expertise across the organization and enable engineering teams to leverage its full value.

Komodor’s platform empowers developers to confidently monitor and troubleshoot their workloads while allowing cluster operators to enforce standardization and optimize performance. 

By leveraging Komodor, companies of all sizes significantly improve reliability, productivity, and velocity. Or, to put it simply – Komodor helps you spend less time and resources on managing Kubernetes, and more time on innovating at scale.

If you are interested in checking out Komodor, use this link to sign up for a Free Trial.