Kubernetes has become the standard for application delivery. According to the Cloud Native Computing Foundation’s (CNCF) annual survey for 2024, 93% of companies either use Kubernetes in production, are piloting it, or are actively evaluating it. Adopting Kubernetes helps companies accelerate digital transformation and adopt cloud native technologies.
However, adopting Kubernetes poses several challenges for infrastructure teams. A complete Kubernetes platform must cover computing, storage, networking, identity and access management, high availability, and scalability. This requires considering the necessary tooling and platforms for a stable, scalable Kubernetes deployment. Organizations also face stricter compliance and data protection requirements. These requirements are the core challenge of Kubernetes adoption.
This article will break down the benefits and decision criteria for adopting self-hosted and managed Kubernetes. The goal is to help you make an informed technical decision about which solution best fits your organization’s needs.
Kubernetes is an open-source platform whose core design principle is resilience, providing automated deployment, scaling, and management of containerized applications. Through declarative directives, K8s enables users to easily define clusters and manage the state and configuration of deployed applications.
This mechanism provides Kubernetes with several critical capabilities, including:
A complete Kubernetes platform delivers a stable development environment, enhancing productivity and reducing time-to-market for services. Kubernetes has become the standard for deploying cloud-native applications and serves as a key engine driving enterprise digital transformation.
For organizations looking to adopt Kubernetes for AI or considering future AI deployments, the choice of deployment model will be crucial in 2026, as AI deployments will increasingly leverage container management platforms. According to a 2025 Gartner report on container management, "By 2028, 95% of new AI deployments will use Kubernetes, up from less than 30% today."
Critical factors need to be evaluated when selecting the right deployment model.
Kubernetes has become the de facto standard for orchestrating containers in modern IT infrastructure, primarily functioning to automate the deployment, scaling, and management of containerized applications.
It enables organizations to transition smoothly to cloud-native architectures by providing a standardized Infrastructure as a Service (IaaS) or Platform as a Service (PaaS) model that interfaces consistently with containers, networking, and storage.
By abstracting infrastructure complexities, Kubernetes allows development teams to focus entirely on writing code and developing innovative services, including AI applications, regardless of whether they are deployed on public clouds, private clouds (like OpenStack), or bare metal.
This seamless integration with DevOps and CI/CD pipelines has led to significant business benefits, with many organizations reporting a reduction in time-to-market by up to 40%. This impact underscores Kubernetes' vital role and positive transformative potential in contemporary IT infrastructure.
Open-source software offers three fundamental advantages: it accelerates innovation, enhances security, and ensures freedom of choice.
Innovation is accelerated through open collaboration, allowing development teams to easily exchange knowledge and achieve superior results more rapidly.
Security and safety are enhanced because a community openly scrutinizes the code. This transparency enables both users and developers to continuously evaluate and review the software, leading to quicker identification and patching of vulnerabilities.
Lastly, open-source software provides organizations with complete freedom of choice. They can adopt and integrate tools precisely when and how they need them, thus strategically avoiding vendor lock-in.
The optimal Kubernetes deployment model depends on your organization's business goals, technical expertise, and infrastructure strategy. The following detailed comparison will help you decide whether a self-managed (DIY) or managed (KaaS) Kubernetes solution is the right fit for you.
Self-Managed vs. Managed Kubernetes Quick Comparison
|
Criteria |
Self-Managed |
Managed |
|
Infrastructure |
Full responsibility: Your team is responsible for planning, deploying, and operating infrastructure for high availability (HA) and backup. |
Provider responsibility: The provider is responsible for the infrastructure's high availability, operation, and backup. |
|
Control and Customizability |
Maximum flexibility: You have full control over the entire stack, including the OS, CNI, and CSI drivers, so you can match them to specific use cases. |
Reduce flexibility: The provider delivers an opinionated setup that restricts access to underlying components for customization. |
|
Seucity and Compliance |
Full responsibility: To meet compliance requirements, your team must apply all relevant patches and baseline configurations. |
Shared responsibility: The provider is responsible for infrastructure security, while your team is responsible for data, identity and access management (IAM), and compliance requirements. |
|
In-house Expertise |
Mandatory: A deep technical knowledge is required to handle security, operations, and troubleshooting architecture. |
Optional: For teams without in-house Kubernetes expertise, this model allows for developers to focus on application delivery. |
|
Cloud Strategy |
Favoured for on-premise, hybrid cloud, or air-gapped deployments and vendor-independent deployments. |
Favoured for single cloud deployment or multi-cloud deployment combined with a management platform. |
|
Operational Burden |
High: Requiring a lot of internal resources for both setup and ongoing maintenance. |
Low: Reduced operational complexity, allows for more focus on application delivery. |
|
Total cost of ownership |
Higher overall TCO: Costs can be three times higher due to the need for specialized engineers. |
Lower overall TCO: OpEx costs are generated through cluster management fees, without the need for specialized engineers. |
|
Time to Market |
Longer: Complexity and manual configuration result in a longer time to market. |
Faster: Automated cluster deployment reduces the initial workload, thereby reducing time to market. |
Choose an unmanaged (DIY) Kubernetes cluster if your organization requires maximum control, customization, and vendor independence.
Retaining full control over the infrastructure allows you to tailor the deployment to precisely match your specific needs. This makes it ideal for effectively leveraging specialized or existing infrastructure, such as OpenStack-powered private clouds or HPC clusters. Ultimately, a DIY Kubernetes approach, backed by a strong internal team, provides true infrastructure freedom and prevents vendor lock-in.
Choose DIY Kubernetes if your organization requires:
You need full control over every layer of the Kubernetes stack, including the operating system (OS), kernel, networking (CNI), and storage (CSI) plugins.
Seamlessly connect Kubernetes to existing on-premises environments, private clouds (e.g., OpenStack), bare metal servers, and specialized hardware such as HPC clusters.
Self-host Kubernetes for maximum portability across different cloud and infrastructure providers to ensure long-term flexibility.
Existing large, skilled internal team that is capable of handling the complexities of high-availability control planes, security hardening, monitoring, and version upgrades.
Choose a managed Kubernetes service (KaaS) if your primary goals are reducing operational complexity and accelerating time-to-market.
These services offer the necessary agility and infrastructure abstraction that are ideal for smaller teams or those lacking specialized expertise. Since the cloud provider handles maintenance, security patching, and scaling, your developers can focus entirely on application delivery and leveraging the scale and flexibility of a multi-cloud or hybrid architecture.
Choose a managed Kubernetes service if your organization requires:
You want to offload the heavy lifting of managing the control plane, infrastructure, compliance, and security patching to a managed service provider.
Your primary goal is to quickly deploy applications and accelerate development velocity without spending weeks setting up infrastructure.
Reduce the need for highly specialized DevOps staff and leverage cluster autoscaling for better resource utilization to lower the total cost of ownership (TCO).
Implementing a multi-cloud or hybrid cloud strategy with cloud-agnostic Kubernetes deployment is necessary.
Kubernetes is a declarative, automated system that helps organizations deploy and scale containerized applications more quickly and consistently, while improving resilience. This choice will become even more critical in 2026 and beyond as AI workloads increasingly depend on Kubernetes. Gartner projects that 95% of new AI deployments will use Kubernetes by 2028.
The ideal solution lies between these two extremes.
CubeCOS strikes a balance by combining the control and flexibility of self-managed Kubernetes with the automation, security, and operational efficiency of managed services. CubeCOS enables teams to maintain ownership of their infrastructure and data while offloading routine management tasks. This pragmatic approach supports compliance, scalability, and faster innovation.
CubeCOS provides a Kubernetes deployment model that combines flexible control with automated operations. Organizations retain infrastructure and data sovereignty while the platform handles routine management, security, and availability. This approach strikes a balance between vendor independence, compliance, scalability, and operational efficiency, making it a solution that falls between self-managed Kubernetes and KaaS.
There are three key aspects companies should evaluate: control requirements, internal technical capabilities, and operational and compliance needs. In scenarios where extensive customization and vendor independence are paramount, self-managed Kubernetes emerges as an optimal solution. However, if the goal is to reduce the operational burden, accelerate deployment, and enhance security, KaaS offers greater advantages.
Self-managed Kubernetes provides the highest level of architectural control, offering complete freedom in choosing the operating system (OS), container network interface (CNI), container storage interface (CSI), hardware environment, and deployment topology. It also integrates more easily with existing infrastructure, such as OpenStack private clouds or HPC clusters, and it completely avoids vendor lock-in. It is ideal for organizations with strong SRE/DevOps teams.
KaaS is ideal for organizations looking to accelerate product launches, reduce operational complexity, and lacking a dedicated Kubernetes team. With KaaS, the provider manages the control plane, high availability, security patches, and compliance, allowing the company to focus on application and service delivery.
As AI workloads become more widespread, more and more enterprises deploying AI services will rely on Kubernetes. Gartner predicts that, by 2028, 95% of new AI deployments will use Kubernetes. Therefore, it is essential for organizations to clearly plan their cloud-native and AI infrastructures in 2026 to avoid future technical debt and vendor lock-in risks.
Kubernetes is at the core of cloud-native architecture. It simplifies complex infrastructure, offers a consistent IaaS/PaaS model, and integrates closely with DevOps and CI/CD pipelines. These features enable companies to reduce time-to-market by roughly 40% while running consistent workloads across multi-cloud, private cloud, and bare-metal environments.