Architecting Kubernetes clusters- how many should you have

Architecting Kubernetes clusters- how many should you have

There are different ways to design Kubernetes clusters depending on the needs and objectives of users. Some common cluster architectures include:

Single cluster architecture: This is the prevalent approach, where all applications are deployed on a single cluster. It is easy to set up and manage. There may be limitations, in terms of scalability, availability, security, and resource utilization.

Multi-cluster architecture: This involves an advanced setup where applications are distributed across multiple clusters. This architecture enhances scalability, availability, security, and isolation. Introduces overheads, complexity, and potential inconsistencies.

Hybrid cluster architecture: This offers flexibility by deploying applications on a combination of types of clusters, like on-premises cloud-based or edge clusters. This approach optimizes cost effectiveness, performance, and compliance. It comes with trade-offs in terms of challenges and dependencies.

The primary goal of this article is to offer some guidance on selecting the cluster architecture for your specific needs. However, it’s important to note that there is no one solution for cluster architectures as the ideal choice depends on various factors and requirements. Within this article, I will explore the advantages and disadvantages of each cluster architecture and present examples of use cases where they’re particularly applicable. Additionally, I will provide recommendations on how to assess and compare cluster architectures based on performance, availability, cost, or compliance considerations. By reading this article, you will be able to find the optimal balance between simplicity and flexibility when architecting your Kubernetes clusters.

Factors To Consider Before Choosing Cluster Architecture

Before choosing a Kubernetes cluster application, it is important that you evaluate the factors involved that will significantly influence the performance, scalability, and resilience of your cluster, because cluster architecture, whether for storage, high-performance computing, or other purposes, serves as the backbone of your infrastructure, and making good decisions at this stage makes it possible to achieve your intended outcome. Some of the critical factors in these architectural decisions include:

The Primary Purpose of the Cluster: The intended use of the cluster, such as data storage, high-performance computing, load balancing, or any other application, would significantly impact the design and configuration of the architecture.
Customer Requirements: This directly addresses the unique needs and expectations of the end-users or customers. Considering customer requirements as a factor before selecting cluster architecture ensures that the resulting solution is not only technically sound but also aligns with the business goals and customer expectations.
Workload constraints or requirements: Some workloads may require real-time data access, while others might involve batch processing. The cluster architecture must align with these access patterns. Workloads that are mission-critical may require redundancy and fault tolerance measures to ensure continuity in the event of hardware failures. The architecture must be designed to meet these requirements.
Scale of the workloads running on Kubernetes: The scale of workloads impacts resource allocation. Large-scale workloads require more CPU, memory, and storage resources. Cluster architecture must be designed to efficiently allocate and manage these resources to ensure optimal performance.

Single-cluster architecture


A single cluster configuration is the widely used architecture, where all applications are deployed on a single cluster. A cluster refers to a collection of nodes (either virtual machines) that execute Kubernetes components and host the applications. A cluster can consist of one or more nodes, and a node can be part of one or more clusters.

A single cluster architecture possesses the characteristics;

Simplicity: Setting up and managing a single cluster architecture is straightforward, as it requires configuration and coordination compared to multiple clusters. It also simplifies networking, storage, and security across clusters by reducing complexity and overhead.

Efficiency: Opting for a single cluster architecture can enhance resource utilization and application performance since it enables them to share cluster resources while facilitating communication. Furthermore maintaining clusters incurs costs and waste.

Consistency: By adopting a single cluster architecture, applications benefit from consistency and reliability as it mitigates synchronization issues, replication challenges, and divergences across different clusters. Additionally, deploying and updating applications becomes more streamlined.

However there are also limitations and disadvantages associated with a single cluster architecture:

  • Scalability: A single-cluster architecture can limit the scalability and availability of the applications, as it depends on the capacity and resilience of the cluster. If the cluster becomes overloaded, unstable, or unavailable, it can affect all the applications running on it. It can also be challenging to scale up or down the cluster dynamically based on the demand and load of the applications.

  • Security: A single-cluster architecture can pose some security risks and challenges for the applications, as it exposes them to a larger attack surface and a higher blast radius. If one application is compromised or malicious, it can potentially affect or access other applications on the same cluster. It can also be difficult to enforce different security policies and controls for different applications on the same cluster.

  • Resource constraints: A single-cluster architecture can impose some resource constraints and trade-offs for the applications, as they have to share and compete for the same cluster resources. This can lead to resource contention, starvation, or interference among the applications. It can also limit the flexibility and customization of the applications, as they have to conform to the same cluster specifications and configurations.

A single-cluster architecture is suitable for some use cases, such as:

  • Development or testing environments: A single-cluster architecture can be used for development or testing purposes, where simplicity, efficiency, and consistency are more important than scalability, availability, and security. It can also facilitate faster feedback loops and easier debugging for developers and testers.

  • Small or medium-sized applications: A single-cluster architecture can be used for small or medium-sized applications where scalability, availability, and security are not critical or demanding. It can also optimize resource utilization and performance for these applications.

  • Homogeneous workloads: A single-cluster architecture can be used for homogeneous workloads, where all the applications have similar characteristics, requirements, and behaviors. It can also ensure consistency and reliability for these applications.

Multi-cluster architecture


In a more complex architecture known as multi-cluster applications are spread out across several clusters. These clusters consist of groups of nodes, which can be virtual machines running Kubernetes components and hosting the applications.

A multi-cluster architecture has the following characteristics:

Scalability: By implementing a multi-cluster architecture, applications can achieve improved scalability and availability. This approach allows them to span across clusters and regions, enabling load balancing, failover mechanisms, and disaster recovery capabilities. Additionally, it supports the scaling of clusters based on application demand and workload.

Isolation: The adoption of a cluster architecture enhances the isolation and security of applications by segregating them into distinct clusters and namespaces. This segregation helps mitigate the impact of points of failure or malicious attacks on any particular cluster. Furthermore, it enables the enforcement of security policies and controls for applications across different clusters.

Diversity: A multi-cluster architecture embraces diversity by facilitating the deployment of applications on different types of clusters, such as on-premises cloud-based or edge clusters. It also accommodates specifications and configurations for each application across clusters.

However, a multi-cluster architecture also has some challenges and drawbacks, such as:

Complexity: Implementing and managing clusters in a cluster architecture can pose challenges and increase the level of complexity. It requires coordination and synchronization between the clusters, particularly when it comes to communication, storage, and security across clusters.

Overhead: The use of a cluster architecture may result in additional overhead and resource consumption due to the maintenance of multiple clusters. This can also lead to costs associated with running these clusters.

Inconsistency: The presence of clusters within a cluster architecture may introduce inconsistencies and potential reliability issue for applications. These inconsistencies could manifest as latency, duplication, or divergence across the clusters. It can also complicate the deployment and update processes of the applications.

A multi-cluster architecture is suitable for some use cases, such as:

Production or disaster recovery environments: A multi-cluster architecture can serve the purpose of both production and disaster recovery scenarios, ensuring scalability, availability, and security are given importance. Additionally, it offers redundancy and resilience to applications in case of failures or unforeseen events.

Large or complex applications; When dealing with complex applications that pose challenges in terms of scalability, availability, and security, a cluster architecture comes into play. It allows for the division of applications into simpler components that can run independently on clusters. This approach also supports modularization and facilitates management.

*Heterogeneous workloads**: A multi-cluster architecture is suitable for workloads characterized by diverse requirements, behaviors, and characteristics. It enables customization and optimization of applications to cater to cluster types while ensuring efficiency across the board.

Hybrid-cluster architecture

A hybrid cluster architecture refers to an adaptable structure that combines types of clusters, including on-premises, cloud, or edge clusters, to deploy applications.

The main characteristics of a hybrid cluster architecture are as follows;

Flexibility: This type of architecture offers users the freedom to choose the cluster type for their specific needs and objectives. It also facilitates integration and interoperability between clusters and platforms, like Kubernetes with OpenShift or AWS with Azure.

Cost effectiveness: A hybrid cluster architecture maximizes cost effectiveness and efficiency for applications by leveraging the advantages provided by cluster types, such as performance, availability, security, and scalability. Furthermore, it reduces reliance on a cluster type or provider to avoid vendor lock-in.

Maintaining Compliance: Using a hybrid cluster architecture offers the advantage of ensuring compliance and governance for applications. This approach allows applications to meet business requirements and standards across various types of clusters, encompassing factors such as data sovereignty, privacy, and security.

However, a hybrid-cluster architecture also has some trade-offs and drawbacks, such as:

Considerations: When using a hybrid cluster architecture, there are considerations and compromises that need to be made for applications. It’s important to find a balance between performance, availability, security, and scalability while managing different types of clusters.

Obstacles: Setting up and managing an environment with a hybrid cluster architecture can present some obstacles and challenges. These include dealing with networking, storage, security, and monitoring across cluster types. Additionally, it may require skills and expertise to operate and maintain these clusters effectively.

Interdependencies: Choosing a hybrid cluster architecture means that applications become dependent on the availability and compatibility of cluster types. This introduces risks if there are network issues or provider outages that could lead to failures or disruptions.

A hybrid cluster structure is well suited for situations, such, as;

Hybrid or multi-cloud scenarios: A hybrid cluster setup can be utilized in cases where applications need to operate on both on-premises and cloud clusters or across cloud service providers. It also facilitates migration to the cloud or handling workload surges.

Global deployments: A hybrid cluster architecture is beneficial for deployments that span across geographical locations or zones. This arrangement can enhance application performance. Reduce latency.

  • Regulated or sensitive data: A hybrid-cluster architecture can be used for regulated or sensitive data, where the applications need to comply with different data protection laws or policies for different types of clusters. It can also enhance security and privacy for the applications.


Finally, it’s crucial to highlight that there is no one-size-fits all solution when it comes to cluster architectures. The optimal choice depends on factors and requirements like performance, availability, cost, and compliance.

In this article, I’ve discussed with you how to design Kubernetes clusters and the number of clusters you should create and manage. I’ve also explored with you the advantages and disadvantages of cluster architectures: cluster, multi-cluster and hybrid cluster. Additionally, I’ve provided some examples of use cases where each architecture’s suitable. The main aim of this article was to offer guidance on selecting the cluster architecture for your needs.

Moreover, I also offered some recommendations on evaluating and comparing cluster architectures based on these criteria. For instance, you can assess performance and availability by considering metrics such as throughput, latency, uptime, reliability, scalability, security measures, utilized resources, or cost effectiveness. Furthermore, you can also evaluate compliance and governance by referencing standards, like GDPR, HIPAA, PCI and DSS ISO 27001.

Ultimately, the best way to find the optimal balance between simplicity and flexibility for your Kubernetes clusters is to experiment and test different cluster architectures and see how they work for your applications. You can use tools such as Kubefed, Anthos, or Rancher to create and manage multiple or hybrid clusters. You can also use tools such as Prometheus, Grafana, or Datadog to monitor and troubleshoot your clusters. By doing so, you will be able to architect your Kubernetes clusters with confidence and ease.

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