In-depth comparison of Kubernetes On-Prem and On-Cloud


Kubernetes is a rapidly growing application orchestration platform. It offers compatibility with major cloud providers such as GCP, AWS, or Azure, allowing seamless deployment in cloud environments. Additionally, Kubernetes provides the flexibility to be deployed on our on-premises bare-metal devices, offering a versatile solution for managing containerized applications across diverse infrastructures. In certain situations, the imperative to use this platform becomes evident, dictated by specific organizational needs or requirements.

First and foremost, let's begin by exploring what Kubernetes is and understanding the reasons why it's a valuable technology for us.

What is Kubernetes?

Kubernetes is a container orchestration system that is used to automate the configuration, deployment, scaling, and management of containerized applications. Born out of Google's experience in managing large-scale containerized systems, Kubernetes has become the industry standard for automating the complexities of modern application deployment.

Why use 'Kubernetes'?

  • Scalability
    • Horizontal Scaling: Kubernetes supports horizontal scaling by allowing you to run multiple instances (replicas) of your application across different nodes. This is achieved through the use of Deployments or ReplicaSets, and it ensures that the workload is distributed evenly, preventing a single point of failure. As demand increases, you can easily scale by adding more replicas
    • Vertical Scaling: Kubernetes also supports vertical scaling, where you can adjust the computing resources (CPU and memory) allocated to a single pod. This allows you to scale individual components of your application to handle increased processing or memory requirements
    • Autoscaling: Kubernetes supports automatic scaling based on resource utilization or custom metrics. Horizontal Pod Autoscalers (HPA) can dynamically adjust the number of replicas based on metrics such as CPU utilization or custom metrics from external sources
    • Cluster Scaling: Kubernetes can also scale at the cluster level by adding or removing nodes dynamically. This ensures that the overall capacity of the cluster adjusts to handle increased demand
  • High availability
    • Load Balancing - Kubernetes includes built-in load balancing mechanisms. Traffic can be distributed across multiple instances of an application, ensuring that no single node is overwhelmed and that workloads are evenly distributed
    • Replication and Redundancy - Kubernetes allows you to run multiple replicas of your applications across different nodes. If one instance fails, others can seamlessly take over, providing redundancy and preventing a single point of failure
    • Automated Failover and Self-Healing - Kubernetes monitors the health of applications and nodes. If a node or a container fails, Kubernetes can automatically reschedule the workload to healthy nodes, ensuring continuous operation without manual intervention
    • Rolling Updates and Rollbacks - Kubernetes supports rolling updates, allowing you to update your applications without downtime. If issues arise during an update, you can easily roll back to a previous, stable version, minimizing the impact on users
    • Resource Scaling - Kubernetes enables horizontal scaling by adding or removing instances of applications based on demand. This elasticity ensures that your applications can handle varying workloads without compromising performance
    • Declarative Configuration - Kubernetes uses declarative configuration files to define the desired state of applications. If there are discrepancies between the desired state and the actual state, Kubernetes automatically corrects them, maintaining the high availability of the system
    • Multi-Node and Multi-Cluster Support - Kubernetes can be deployed across multiple nodes or even multiple clusters. This distributed architecture enhances fault tolerance and ensures that the failure of one node or cluster doesn't impact the entire system
    • Health Checks and Probes - Kubernetes allows you to define health checks and readiness probes for your applications. If an application becomes unhealthy, Kubernetes can take corrective actions, such as restarting the container or moving the workload to a healthy node
    • Storage Orchestration - Kubernetes provides storage orchestration, ensuring that data is available even if a node fails. Persistent Volumes (PVs) and Persistent Volume Claims (PVCs) can be used to maintain data integrity and availability
    • Pod Disruption Budgets - Implement Pod Disruption Budgets to control the number of simultaneously disrupted pods during voluntary disruptions (e.g., rolling updates). This ensures that applications remain available and responsive during maintenance or upgrades
  • Self-healing
    • Automatic Restart: If a container within a pod fails, Kubernetes can automatically restart the container to attempt to bring it back to a healthy state. This is especially useful for transient failures or issues that can be resolved by a simple restart
    • Node Auto-Recovery: In the event of a node failure, Kubernetes can reschedule affected workloads to healthy nodes. This ensures that applications remain available even if individual nodes experience issues
    • Replica Management: Kubernetes maintains a specified number of replicas for a given application through concepts like Deployments or ReplicaSets. If the actual number of replicas falls below the desired count due to failures or other issues, Kubernetes automatically creates new replicas to restore the desired state
    • Health Probes: Kubernetes allows you to define health probes for applications. These probes regularly check the health of the application, and if an application or container becomes unhealthy, Kubernetes can take corrective actions, such as restarting the container or rescheduling the workload to a healthy node
  • Easy deployment
    • Declarative Configuration: Users describe the desired state of their application using simple configuration files, typically in YAML or JSON format. This allows for a clear and human-readable definition of how the application should run in the Kubernetes environment
    • Abstraction of Complexity: Tools and methodologies are employed to abstract away much of the complexity associated with Kubernetes. Users are shielded from low-level details, making the deployment process more intuitive
    • Command-Line Tools: Easy deployment often involves the use of straightforward command-line tools like kubectl to interact with the Kubernetes cluster. This allows users to apply configurations, check deployment status, and manage resources using familiar commands
    • Pre-built Templates: Templating systems or pre-built templates, such as Helm charts, provide users with reusable and customizable configurations for common applications. Users can leverage these templates to deploy applications without having to manually craft complex configurations
    • Automation: Easy deployment may involve automation scripts or tools that handle the deployment process automatically. This can include creating necessary Kubernetes resources, managing dependencies, and handling updates seamlessly
    • User-Friendly Interfaces: Some platforms or tools provide graphical user interfaces (GUIs) that simplify the deployment process. These interfaces often abstract Kubernetes complexities, allowing users to interact with the cluster using a more intuitive graphical representation
    • Minimal Prerequisites: Easy deployment aims to minimize the prerequisites and setup required for users to get their applications up and running on Kubernetes. This includes straightforward setup procedures for clusters and simplified networking configurations
  • Cost efficiency through improved resource utilization
    • Containerization and Resource Isolation: Kubernetes leverages containerization, which allows applications to be packaged with their dependencies in isolated environments. Containers share the host OS kernel but run as isolated processes, leading to efficient resource utilization and reduced overhead
    • Dynamic Scaling: Kubernetes enables dynamic scaling based on demand. Applications can automatically scale horizontally by adding or removing container instances in response to changing workloads. This ensures that resources are allocated as needed, preventing over-provisioning and reducing idle resource costs
    • Pod Packing: Kubernetes allows multiple containers to run within the same pod, sharing the same resources. This pod-packing strategy can lead to better utilization of resources by maximizing the use of available capacity
    • Resource Monitoring and Insights: Kubernetes provides tools for monitoring resource utilization, such as metrics and logs. This visibility helps operators identify bottlenecks, over-provisioned resources, and opportunities for optimization
  • Portable across cloud providers
    • Containerization: Kubernetes is built on containerization, where applications and their dependencies are packaged into containers. Containers provide a consistent and isolated runtime environment, ensuring that applications run the same way regardless of the underlying infrastructure
    • Abstraction of Infrastructure: Kubernetes abstracts away the underlying infrastructure details. It provides a unified API for managing containers, allowing users to describe the desired state of their applications without being tied to the specifics of a particular cloud provider or data center
    • Provider-agnostic API: Kubernetes provides a provider-agnostic API that abstracts the differences between various cloud providers. This allows users to deploy and manage applications using the same set of Kubernetes commands, regardless of whether the cluster is hosted on AWS, Azure, Google Cloud, or on-premises
    • Cluster Federation: Kubernetes Federation allows the management of multiple Kubernetes clusters from a single control plane. This feature enables the coordination and deployment of applications across clusters, whether they are hosted on different cloud providers or data centers
  • Security
    • Role-Based Access Control (RBAC): Kubernetes employs RBAC to control access to the cluster's resources. This ensures that only authorized users or processes have the necessary permissions to interact with specific API objects. RBAC helps limit potential security risks by enforcing the principle of least privilege
    • Pod Security Policies (PSP): PSP allows cluster administrators to define a set of conditions that a pod must run with, such as restricting the use of privileged containers or specifying a specific user or group ID. PSP helps enforce security policies at the pod level
    • Network Policies: Kubernetes Network Policies allow you to define rules for communication between pods, restricting or allowing traffic based on labels, namespaces, and other criteria. This enhances network segmentation and isolates applications for improved security
    • Secrets Management: Kubernetes provides a mechanism for securely storing and managing sensitive information such as API keys, passwords, and tokens. Secrets are encrypted at rest and can be used by pods securely
    • Pod Identities: Kubernetes supports the use of ServiceAccount and PodServiceAccount to restrict permissions for pods. This helps ensure that pods only have the necessary access to other resources in the cluster
    • Pod Readiness and Liveness Probes: By defining readiness and liveness probes in pod specifications, Kubernetes can automatically restart or stop containers that are unresponsive or in a failed state. This helps maintain the health of applications and improves overall system security
    • Audit Logging: Kubernetes provides auditing features to track API server requests. By enabling and configuring audit logs, administrators can monitor activities within the cluster, helping to identify potential security incidents
    • Runtime Security: Implementing runtime security measures, such as using container runtimes with security features like gVisor or Kata Containers, can add an additional layer of security to the execution of containers

Now, let's take a moment to delve into the realm of Kubernetes on the cloud, exploring its features and advantages.

Kubernetes on-Cloud

As businesses migrate towards cloud environments for their agility and scalability, Kubernetes becomes a natural ally. Whether you choose Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), or other cloud providers, Kubernetes seamlessly integrates, providing a consistent and efficient way to manage containers.

Benefits of Kubernetes on-Cloud

  • Ease of Implementation
    • Managed Kubernetes Services - Many cloud providers, such as Amazon EKS, Azure Kubernetes Service (AKS), and Google Kubernetes Engine (GKE), offer managed Kubernetes services. These services abstract away the complexities of cluster setup, maintenance, and upgrades, allowing you to focus on deploying and managing your applications
    • One-Click Deployments - Cloud marketplaces often provide Kubernetes solutions that can be deployed with a single click. These solutions come pre-configured, reducing the initial setup effort and allowing you to get started swiftly
    • Intuitive User Interfaces - Cloud platforms offer intuitive web-based interfaces for managing Kubernetes clusters. These interfaces provide easy access to essential functionalities, allowing you to monitor, scale, and deploy applications without delving into intricate command-line interfaces
    • Infrastructure as Code (IaC) - Leverage Infrastructure as Code tools, such as Terraform or AWS CloudFormation, to define and provision your Kubernetes infrastructure. This approach streamlines the deployment process, ensuring consistency and reproducibility
    • Container Registries Integration - Cloud providers often integrate seamlessly with container registries. You can effortlessly store and retrieve your container images from the cloud registry, streamlining the container deployment process
    • Integration with Cloud Services - Cloud-native integrations enable seamless interaction between Kubernetes and other cloud services. For example, integrating with managed databases, storage solutions, or serverless functions becomes straightforward, enhancing the functionality of your applications
    • Monitoring and Logging Solutions - Cloud providers offer integrated monitoring and logging solutions, providing visibility into the health and performance of your Kubernetes clusters. This simplifies the implementation of effective observability strategies
  • Resource efficiency
    • Since we don’t need to share the resources with the underlying infrastructure, Kubernetes on-cloud provides a robust framework for maximizing resource utilization.
  • Auto management
    • Auto-Upgrade - Managed Kubernetes services on cloud providers often offer automatic cluster upgrades. Kubernetes clusters can be automatically upgraded to newer versions without manual intervention, ensuring that clusters benefit from the latest features and security patches
    • Node Auto-Recovery - in the event of a node failure, cloud-managed Kubernetes services can automatically replace or recover the affected nodes, ensuring continuous operation
    • Auto-Provisioning - Cloud providers support auto-scaling groups or similar features, allowing Kubernetes clusters to automatically provision additional nodes based on demand and scale down during periods of lower usage
    • Auto-Scaling Groups - Cloud providers often integrate Kubernetes with auto-scaling groups. These groups automatically adjust the number of virtual machine instances based on demand, ensuring that the underlying infrastructure scales with the workload
    • Resource Optimization - Kubernetes on the cloud optimizes resource utilization by dynamically adjusting resource allocations for containers. This prevents over-provisioning and helps manage costs efficiently.
    • Automated Backups and Disaster Recovery - Managed Kubernetes services often include automated backup solutions and disaster recovery capabilities, ensuring data resilience without manual intervention
  • Scalability
    • Node Scaling - Cloud providers offer auto-scaling groups or similar features, allowing Kubernetes clusters to automatically adjust the number of nodes based on demand. This ensures that the cluster scales horizontally to accommodate increased workloads.
    • Cluster Autoscaler - Cloud-managed Kubernetes services often include a Cluster Autoscaler that adjusts the size of the cluster dynamically. It can add or remove nodes based on the resource needs of running and pending pods, ensuring optimal resource utilization.
  • Reduced initial cost
    • Start Small, Scale as Needed - Begin with a modest-sized Kubernetes cluster and scale resources as your application demands grow. Cloud providers often allow easy scaling of resources, and starting small helps control initial costs
    • Reserved Instances or Committed Use Discounts - Take advantage of reserved instances (AWS) or committed use discounts (GCP) for predictable and sustained workloads. This commitment can provide significant cost savings compared to on-demand pricing
    • Managed Kubernetes Services - Consider using managed Kubernetes services provided by cloud providers (e.g., Amazon EKS, Azure AKS, Google GKE). These services abstract much of the infrastructure management, reducing the operational overhead and potentially lowering initial costs
    • Infrastructure as Code (IaC) - Use Infrastructure as Code tools, such as Terraform or AWS CloudFormation, to automate the provisioning of your Kubernetes infrastructure. Automation reduces manual effort, avoids configuration errors, and contributes to cost-effective infrastructure setup
    • Cost Monitoring and Alerts - Set up cost monitoring tools and alerts to track your cloud spending. Cloud providers offer tools to visualize and analyze costs, helping you identify areas where adjustments can be made to optimize spending
    • Container Density - Optimize the number of containers running on each node to maximize resource utilization. Efficiently packing containers onto nodes can help reduce the overall number of nodes needed, leading to cost savings
    • Selective Use of Managed Services - Leverage managed services for specific components where cost savings can be realized. For example, use managed databases, storage, or other services when they provide cost benefits compared to self-managed alternatives
    • Pay-as-You-Go Model - Take advantage of the pay-as-you-go pricing model offered by cloud providers. This model allows you to pay for the resources you use, making it suitable for variable workloads and reducing upfront costs
  • Easy availability
    • Multi-Region Deployments - Consider deploying your Kubernetes cluster across multiple regions for geographic redundancy. Multi-region deployments enhance availability and resilience, especially in scenarios where an entire region may experience issues

When does Kubernetes on-cloud won’t be the best solution?

While Kubernetes (K8s) on the cloud offers many benefits, including scalability and ease of management, it may not be the optimal solution for every use case.

  • Low latency requirements
    • Network Overhead - Cloud environments introduce additional network overhead due to the distributed nature of resources. For applications with extremely low latency requirements, the inherent latency of the cloud network infrastructure may not meet the desired performance criteria
    • Data Center Proximity - In cloud deployments, the physical distance between data centers and end-users might be significant, leading to higher latency. For applications where proximity to end-users or specific data centers is critical, an on-premise solution might offer better latency.
    • Multi-Tenancy Impact - Cloud environments often host multiple tenants, and shared resources may impact network performance. In scenarios where you need absolute control over resource allocation and isolation to meet low-latency demands, a dedicated on-premise solution might be more suitable
    • Variable Network Conditions - The cloud operates in a shared infrastructure with varying network conditions. While cloud providers strive to provide stable and performant networks, the shared nature of these environments may result in occasional variability in network performance
    • Resource Noisy Neighbors - In multi-tenant cloud environments, the presence of resource-intensive or "noisy neighbor" workloads on the same underlying infrastructure can impact the consistent low-latency performance of your applications
    • Limited Control Over Infrastructure - Cloud users have limited control over the underlying infrastructure, including the network stack. Fine-tuning network configurations to achieve ultra-low latency may be more challenging in a cloud setting compared to on-premise environments
    • Data Transfer Costs - In some cloud environments, data transfer costs may be associated with moving data between different regions or availability zones. Applications requiring frequent data transfers may incur additional latency due to these costs
    • Custom Hardware Considerations - Some low-latency applications may require specialized hardware configurations that are not readily available in the cloud. Achieving optimal performance might involve specific hardware choices that are more feasible in an on-premise setup
    • Edge Computing Requirements - For scenarios where low-latency processing is critical at the edge of the network (near end-users or devices), traditional cloud data centers may not offer the required proximity. Edge computing solutions closer to the point of data generation might be more suitable
    • High-Frequency Trading (HFT) - Industries such as high-frequency trading, where sub-millisecond latencies are essential, often require specialized infrastructure and direct connections that might be challenging to replicate in a generic cloud environment
  • Data gravity
    • Data Movement Costs - In a cloud environment, moving large volumes of data can incur costs, both in terms of time and money. If your application involves frequent and substantial data transfers, the associated expenses and potential latency might be a concern
    • Egress Costs - Cloud providers often charge for data leaving their networks (egress costs). For applications with significant data gravity, especially those involving constant data movement out of the cloud, these costs can become a significant factor
    • Proximity to Data Sources - Applications that require close proximity to specific data sources may face challenges in the cloud. For instance, if your data is generated at the edge, having to transmit it to a distant cloud data center can introduce latency
    • Large Datasets - Managing and processing large datasets in the cloud may be less efficient compared to on-premise solutions, especially if the datasets are extremely large. For certain workloads, local access to data may provide better performance
    • Cost Considerations - Managing large datasets in the cloud may lead to higher storage costs. Evaluating the cost implications of storing and transferring data in a cloud environment versus on-premise is essential
  • Data privacy
    • Shared Infrastructure - Cloud environments are shared by multiple tenants, raising concerns about data isolation and unauthorized access
    • Data Residency and Compliance - Data stored in the cloud may cross geographic boundaries, impacting data residency compliance
    • Control Over Infrastructure - Limited control over the underlying infrastructure in a cloud environment may raise questions about data security
    • Encryption - Ensuring data privacy involves encryption for data at rest and in transit. Questions may arise about the effectiveness of encryption
    • Access Controls and Identity Management - Managing access controls and Identity and Access Management (IAM) settings is crucial for data privacy
    • Compliance Certifications - Organizations may question the compliance of cloud providers with various data privacy regulations and certifications
    • Third-Party Integrations - Integrating third-party tools and services with Kubernetes on the cloud may introduce additional privacy considerations
  • Higher long-term cost
    • Service Dependencies - Dependencies on premium or specialized cloud services can introduce additional costs
    • Complexity and Management Overhead - Managing and monitoring a complex Kubernetes environment on the cloud may require additional resources and tools, contributing to operational costs.
    • Licensing Costs - Certain cloud services and tools may have licensing costs that can accumulate over time
    • Data Transfer Costs - Transferring data between different regions, availability zones, or external services within the cloud can incur additional costs
    • Pay as you go - Pay for the same amount of resources monthly or annually each month or year.
    • Organizations with long-term, stable workloads may prioritize cost predictability and avoid variable cloud costs
  • Business Policies
    • Regulatory Compliance - Regulatory requirements often dictate where data can be stored and processed. If your industry or organization has specific compliance standards regarding data location, on-premise solutions may be more suitable
    • Data Residency - Some applications may require data residency in specific geographic regions due to legal or regulatory reasons. Cloud providers offer region-specific storage options, but certain applications may have strict data residency requirements
  • Consistent Performance
    • Applications with consistent and predictable performance needs, such as high-frequency trading systems, may benefit from the control and consistency offered by on-premise infrastructure
  • Legacy Systems Integration:
    • Integration with existing legacy systems or specialized hardware that cannot be easily replicated in the cloud
  • Custom Networking:
    • Applications with specific networking requirements that may be challenging to achieve in a generic cloud environment

These are scenarios where running Kubernetes (K8s) on-premise might be a better choice than deploying it in a cloud environment. The decision between on-premise and cloud-based Kubernetes depends on various factors, including specific requirements, resource considerations, and organizational preferences

So now, let's examine the benefits of Kubernetes on-premise and the challenges we may encounter when deploying it.

Kubernetes on-premise

Running Kubernetes on-premise involves deploying and managing Kubernetes clusters within an organization's own data center or private infrastructure instead of relying on a public cloud provider. This approach provides greater control over the infrastructure, networking, and resources, catering to specific organizational requirements.

Benefits of Kubernetes on-Premise

  • Infrastructure Ownership
    • Ownership and Control: Organizations have full ownership and control over the hardware, networking equipment, and other infrastructure components, allowing for fine-tuned customization and security measures
  • Data Residency and Compliance
    • Data Control: On-premise deployments offer control over the physical location of data, which is crucial for addressing data residency requirements and compliance with specific regulations
  • Cost Predictability
    • Capital Expenditure: Deploying Kubernetes on-premise often involves upfront capital expenditures for hardware and infrastructure. However, this can provide cost predictability for organizations with stable workloads over an extended period
  • Networking Control
    • Custom Networking: Organizations can implement custom networking solutions, configure firewalls, and set up network policies to meet specific requirements
  • Performance Optimization
    • Fine-Tuned Performance: On-premise environments allow for fine-tuning and optimizing performance by selecting hardware components that align with the performance needs of applications.
  • Legacy Systems Integration
    • Seamless Integration: On-premise Kubernetes deployments can seamlessly integrate with existing legacy systems and infrastructure, enabling organizations to leverage previous investments
  • Security Measures
    • Enhanced Security Control: Organizations have enhanced control over security measures, including physical security, access controls, and the ability to implement custom security policies.
  • Low Latency
    • Reduced Network Latency: On-premise deployments generally result in lower network latency compared to cloud environments, making them suitable for applications with low-latency requirements
  • Custom Hardware Configurations
    • Specialized Hardware: Organizations can choose and configure specialized hardware components, such as GPUs or hardware accelerators, based on the specific needs of their workloads
  • Centralized Management
    • Centralized Control: On-premise deployments enable centralized management of infrastructure, making it easier to implement consistent policies and configurations across the entire environment
  • Resource Utilization Optimization
    • Efficient Resource Utilization: Organizations can optimize resource allocation based on their unique requirements, ensuring efficient utilization of computing resources

Challenges with Kubernetes on-premise

Deploying Kubernetes on-premise offers advantages in terms of control and customization, but it also comes with its set of challenges. Organizations need to address these challenges to ensure a successful on-premise Kubernetes deployment

  • Infrastructure Management:
    • Organizations must handle the procurement, setup, and maintenance of physical hardware or manage virtualized infrastructure. This includes dealing with hardware failures, upgrades, and capacity planning
    • Performing updates, patches, and maintenance tasks on on-premise infrastructure requires careful planning. Organizations need to ensure minimal downtime and coordinate updates across the entire environment
    • Physical data center space and power requirements can be limiting factors. Organizations need to ensure that their existing facilities can accommodate the hardware needed for Kubernetes clusters
  • Resource Scalability:
    • Scaling resources in an on-premise environment may be slower and more complex compared to cloud environments. Organizations need to carefully plan and execute resource scaling to accommodate growing workloads.
    • While on-premise environments can scale, they may have limitations compared to the near-infinite scalability provided by cloud environments. Organizations must plan for future growth and consider scalability constraints
  • Network Complexity:
    • Configuring and managing networking in on-premise deployments can be more complex. Organizations need to set up and maintain networking components, configure firewalls, and address potential security concerns
  • Skillset and Expertise:
    • Managing an on-premise Kubernetes infrastructure requires specific expertise in both Kubernetes and infrastructure management. Organizations may need to invest in training or hire personnel with the necessary skills
  • Security Concerns:
    • Ensuring robust security measures, including physical security and network security, is crucial. Organizations need to implement security best practices and stay vigilant against potential vulnerabilities
  • Monitoring and Visibility:
    • Achieving comprehensive monitoring and visibility into the on-premise Kubernetes environment may require additional tools and configurations compared to cloud-based solutions
  • Monitoring and Visibility:
    • Achieving comprehensive monitoring and visibility into the on-premise Kubernetes environment may require additional tools and configurations compared to cloud-based solutions.
  • Data Center Resilience:
    • The resilience of the data center itself is crucial. On-premise environments need to be designed with considerations for data center availability, power backup systems, and disaster recovery plans

Now, Let's compare Kubernetes on the cloud and Kubernetes on-prem side by side to gain a comprehensive understanding.

Kubernetes on-premise vs Kubernetes on-cloud

Kubernetes on-Premise Kubernetes on-Cloud
High network, storage, and CPU performance (depending on the setup) Relatively low network, storage, and CPU performance
Upfront capital expenditures, ongoing maintenance costs, and potential for underutilization of resources Pay-as-you-go model, no upfront capital expendituresCosts can scale with usage, and overprovisioning may lead to higher expenses
Potential for cost predictability over the long term, especially for stable workloads Variable costs can be challenging to predict, especially for fluctuating workloads
Full control and ownership over hardware and infrastructure componentsRequires upfront capital expenditures, and organizations are responsible for procurement, maintenance, and scalability Limited control over underlying hardware, subject to cloud provider policiesNo upfront capital expenditures, flexible scalability, and managed infrastructure
Full control over management and operations, suitable for organizations with specific requirementsRequires in-house expertise for maintenance, updates, and ongoing operations Cloud providers offer managed Kubernetes services, simplifying operational aspectsLimited control over underlying infrastructure and organizations rely on cloud provider tools
No integrations with cloud providers and their services that may be required organization Built or easy integration to services provided by the cloud provider.
As hardware is not abstracted, maintainability might be a little bit complex and troublesome. As the hardware is abstracted has simple and easier maintainability.
Scalability is determined by the available hardware, which may require additional procurementScaling may be slower, and resource constraints could limit rapid expansion. Elastic scalability with the ability to quickly provision or de-provision resources
Full control over data location, potentially aligning better with data residency requirementsLimited geographic distribution and potential challenges in meeting certain compliance standards Cloud providers offer global data center presence, facilitating compliance with various data residency requirementsMay involve data transfer across regions, raising compliance considerations.
Initial setup time can be longer due to hardware procurement and configurationSlower deployment speed for new clusters Quick provisioning of resources allows for faster deploymentSome configurations may still require manual setup and tuning
Full control over custom networking configurationsRequires expertise in configuring and managing on-premise network architecture Cloud providers offer easy-to-configure networking optionsLimited flexibility compared to on-premise networking
Organizations have full control over security measures and complianceRequires diligent implementation of security best practices Cloud providers offer robust security features and compliance certificationsOrganizations must trust cloud provider security measures and may have limited control
Organizations have direct control over designing high-availability architecturesRequires careful planning, redundant configurations, and additional hardware for resilience Cloud providers offer managed services with built-in high-availabilityOrganizations may rely on the cloud provider's infrastructure for high availability

Now, please head down to our video to take a closer look at our Kubernetes on-premise deployment

📽️ Watch Video: Why Kubernetes On-Prem and a Glimpse into Our Setup

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