Ultimate guide to multicloud Kubernetes

In recent years, there’s been a growing interest among organisations to transition toward multi-cloud solutions, specifically, Kubernetes-based multi-cloud solutions. This trend is hardly surprising given the benefits and advantages they offer.

These benefits include vendor diversity and flexibility, which enable organisations to avoid vendor lock-in and enjoy the best services from different providers. It also improves reliability and availability while allowing geographic diversity for better latency and local regulation compliance.

Additionally, multi-cloud Kubernetes can help reduce operational costs, achieve superior disaster recovery strategies by distributing workloads, and mitigate risk by improving overall security.

However, there’s a catch. Deploying multi-cloud Kubernetes comes with numerous challenges, and if implemented incorrectly, it can create more problems than solutions.

That’s where this article can help. In it, you’ll get a high-level overview of the practical aspects of implementing Kubernetes in a multi-cloud environment. You’ll also learn about some best practices you can implement to ensure a smooth and efficient multi-cloud Kubernetes experience. By the end of this guide, you’ll have a clear idea of what your organisation needs to deploy and manage Kubernetes across multi-cloud platforms effectively.

Factors to consider for deploying Kubernetes in a multi-cloud environment

Several critical factors must be considered when deploying Kubernetes in a multi-cloud environment. When choosing cloud vendors, it’s essential to weigh aspects like cost, performance, and localisation. Due to variances in business needs, there’s no universally optimal choice. Cloud vendors should be evaluated based on business-specific requirements and the anticipated workload characteristics.

Another factor to consider is automation since it aids in maintaining consistency, reducing errors, and enhancing productivity. Centralised dashboards also play a significant role by providing a unified view of the entire infrastructure, facilitating administration, and improving the observability of multi-cloud Kubernetes clusters.

It’s also a good idea to consider adding an API management tool such as Tyk to your Kubernetes multi-cloud strategy. Tyk can help your DevOps team keep the complexity of your multi-cloud deployment in check and manage the entire Kubernetes API lifecycle. As you’ll learn, an API gateway enhances networking, boosts security, and optimises performance in a multi-cloud Kubernetes environment.

Best practices to keep in mind when using multi-cloud Kubernetes

As you learn about the complex realm of multi-cloud Kubernetes, it’s essential to grasp certain best practices. This section aims to guide your team through application design, deployment, scaling, and observability strategies in Kubernetes multi-cloud environments.

Application design

When deploying multi-cloud Kubernetes, a robust application design is critical. Here are some best practices that your DevOps team should consider:


Design loosely coupled applications

Loosely coupled applications comprise independent microservices that communicate with each other through APIs and standard protocols. Loosely coupled applications allow each microservice to change and evolve without affecting the others.

In a multi-cloud environment, the microservices approach reduces dependencies, making it easier to manage and scale applications across different cloud providers. In this regard, using a platform like Tyk to handle the entire API lifecycle can be especially convenient.


Use cloud-agnostic APIs

Cloud-agnostic APIs allow applications to work seamlessly across different cloud platforms. Rather than tying your application to a specific cloud provider’s API, you should use a cloud-agnostic approach to ensure interoperability and portability.

This practice provides flexibility and avoids vendor lock-in, ensuring your application can leverage the best features and services of different cloud providers.


Deploying a multi-cluster Kubernetes environment demands meticulous attention to several vital aspects. These are paramount to ensure the effective operation of your clusters and help your organisation realise the full benefits of the multi-cloud paradigm.

The following is a list of best practices specific to the deployment of a Kubernetes multi cloud environment:


Employ infrastructure as code

Infrastructure as code (IaC) s a critical practice that involves managing and provisioning computing infrastructure through machine-readable definition files. It allows for consistent and reproducible environments, reducing the risk of human error. For instance, tools such as Terraform can be used to automate the creation and management of your Kubernetes clusters across different cloud providers, thus enforcing GitOps best practices.


Use centralised multi-cluster configuration and management tools

In a multi-cluster environment, managing each cluster’s configuration individually can be a daunting task. A centralised approach can streamline this process, ensuring consistency across different clusters. Tools such as Helm, Kustomize, and ArgoCD can be used to manage and sync cluster configurations.

In addition, implementing a multicluster management tool is vital for deploying distributed applications. Tools such as Rancher or Google Anthos can simplify the process of managing multiple clusters across different cloud platforms.


Implement cross-cluster networking management

Implementing cross-cluster networking management in a multi-cloud Kubernetes environment provides robust and flexible API solutions.

For instance, with Tyk’s Multi Data Centre Bridge MDCB, your team can manage distributed clusters of Tyk API Gateways from a centralised dashboard, ensuring synchronised and up-to-date gateways across multiple data centres and Kubernetes clusters.

Additionally, Tyk’s multicloud and hybrid API management offering allows DevOps teams to manage API gateways across different cloud providers, reducing infrastructure burdens and promoting local performance. On top of that, Tyk’s API management for multi-region deployments ensures high availability of gateways and automatic configuration of new instances in case of failures, providing robust data security even in the event of a network partition.

Overall, implementing a cross-cluster network management strategy boosts productivity, resiliency, and convenience for Kubernetes multi-cloud environments.


Automate CI/CD pipelines

Automated continuous integration, continuous delivery (CI/CD) pipelines can streamline the process of deploying new applications and updates. Tools such as Jenkins, GitLab and Spinnaker can help automate deployments across different Kubernetes clusters.

Moreover, automated CI/CD pipelines are crucial for maintaining rapid, reliable releases in a multi-cloud environment. This is mainly because your team needs to build, test, and deploy applications and services across multiple Kubernetes clusters, and doing it manually is time-consuming and error-prone.


Use cloud-agnostic resource definitions

To achieve true multi-cloud portability, it’s strongly recommended that you use cloud-agnostic resource definitions. This ensures that your applications can run on any cloud provider without modification.

Kubernetes itself provides a cloud-agnostic platform, but this should also be considered when selecting other tools and services.


Prepare for multi-cloud disaster recovery

In a multi-cloud environment, disaster recovery becomes more complex due to the different cloud platforms involved. However, it also provides an opportunity to leverage the strengths of other cloud providers to create a robust disaster recovery plan. This involves regular data backups, replication across different geographic locations, and a well-defined process to restore services quickly during an outage.

Governance and security

Implementing effective governance and security measures is also crucial when operating with multi-cloud Kubernetes. A comprehensive strategy includes robust access control, data encryption, and consistent policy enforcement across all cloud environments. 

Employing a Kubernetes gateway like Tyk can significantly facilitate these processes. Tyk offers advanced functionalities for secure inter-cloud communication, ensuring each request is authenticated before it reaches the Kubernetes service. It validates API keys, OAuth tokens, or JWTs and enforces rate-limiting and quota policies. In addition, Tyk can control who has access to what, which reduces the risk of unauthorised access. 

Last but not least, governance and security practices must be designed with the unique complexities of multi-cloud deployments in mind, leveraging tools like Tyk that facilitate centralised API lifecycle management and maintain a secure and regulated environment. 


Scaling is another crucial aspect of deploying multi-cloud Kubernetes since it ensures a flexible, cost-effective operation that can handle varying traffic levels. With that in mind, review some of the best practices regarding scaling.


Utilise load balancing

One best practice for scaling is to use load-balancing mechanisms and technologies, which help distribute traffic evenly across your application’s instances and clusters. This approach ensures that no single instance is overwhelmed, thereby maintaining your application’s overall performance and responsiveness.


Utilise spot instances or preemptible VMs for cost-effective scaling

Another strategic approach is to use spot instances or preemptible VMs. These offer cost-effective scaling solutions by allowing you to leverage excess cloud capacity at a fraction of the regular price.

However, these instances may not always be available. You must have a fallback plan.


Explore the built-in autoscaling capabilities offered by cloud providers 

Exploring the built-in autoscaling capabilities that cloud providers offer is another best practice. This instance talks specifically about the implementation that each provider makes of Kubernetes’s Cluster Autoscaler, Horizontal Pod Autoscaler (HPA), and Vertical Pod Autoscaler (VPA). These features can automatically adjust the number of instances and pods based on real-time usage metrics, ensuring that you have enough resources to meet demand while avoiding unnecessary costs.


Perform load testing and capacity planning exercises regularly

Lastly, regularly performing distributed load testing and capacity planning exercises is essential. These practices help identify potential bottlenecks and capacity issues before they impact your application, allowing you to manage your resources and ensure optimal performance proactively.


Observability is crucial in any Kubernetes cluster, but it’s particularly relevant in a multi-cloud Kubernetes deployment. This is because observability provides vital information that helps keep costs under control, troubleshoot failures, and maintain optimal performance.


Implement centralised logging and distributed tracing

Implementing centralised logging and distributed tracing is a standard best practice. In Kubernetes, each container writes its logs to standard output. A centralised logging solution for analysis can then aggregate these logs.

For this reason, it’s a good idea to implement a distributed tracing technology, such as OpenTelemetry, as it lets your team understand apps and service interactions within complex microservice systems.


Set up a proactive monitoring and alerting system

Setting up a proactive monitoring and alerting system is another core observability practice. Kubernetes monitoring allows cluster administrators and users to identify issues such as insufficient resources, failures, and problematic pods.

Metrics stored in time-series data stores are vital for understanding system health and creating dynamic dashboards that show historical or real-time data.

In this sense, tools such as Prometheus and Grafana have the advantage of being open source and vendor-agnostic. Additionally, both tools provide valuable insights into the health and performance of your clusters and applications.

Consider implementing a service mesh to enhance observability

Service meshes provide a method for controlling how different parts of an application share data with one another. This can improve visibility and help manage the complexity inherent in a distributed architecture.

All in all, adhering to these observability best practices can ensure that your multi-cloud Kubernetes deployment remains reliable, responsive, and cost-effective.


In this article, you learned that deploying Kubernetes in a multi-cloud multi-cloud environment requires a well-thought-out strategy and the right tools.

By implementing the right approach and best practices, your company can leverage the benefits of Kubernetes and multi-cloud, improving your applications’ reliability, flexibility, and efficiency.

Talking about the right approach, Tyk is a proven API management platform that can be a valuable tool in your multi-cloud Kubernetes deployment. It can manage and monitor your APIs across multiple clouds, providing security, analytics, and developer-friendly tools. Tyk’s versatility and robust features can enhance your multi-cloud strategy and improve the overall efficiency of your Kubernetes deployments.