Kubernetes is now the de facto standard for container orchestration and modern management of Cloud Native applications. More and more companies are wondering how to simplify its adoption. If you're also considering managed cloud solutions to reduce risks and free up your team, you're in the right place.
In this guide to Kubernetes as a Service (KaaS), you'll find everything you need: from tangible benefits to best practices for security and management, up to DevOps and AI scenarios. Without neglecting the limitations and practical advice for truly effective adoption, also thanks to SparkFabrik's profound experience.
Kubernetes as a Service (KaaS) is a managed cloud solution that provides ready-to-use Kubernetes clusters, such as AWS EKS, Google GKE, or Azure AKS. The main feature of KaaS is the so-called managed Control Plane: everything needed to coordinate and "orchestrate" the cluster's operation is managed directly by the provider, so the IT team can focus solely on their applications.
A quick note before we continue: to better understand KaaS, it's helpful to have some knowledge of the basic components of Kubernetes. If you want to delve into the Control Plane, Worker Nodes, and other key elements of this technology, check out our guide to Kubernetes architecture (Italian).
Let's take a closer look at how this platform works. In a traditional cluster, teams have to handle the installation, configuration, and updates of Kubernetes' central elements. With the managed Control Plane of KaaS, however, all these responsibilities are in the hands of the provider: the API server, etcd, controller-manager, and scheduler are automatically replicated and updated by the service itself. The advantage? High availability, resilience, and security are guaranteed, with no extra effort for the internal team.
Another key aspect is the configuration of internal cluster services. KaaS allows you to easily set up different types of Kubernetes services, such as ClusterIP for internal communication, NodePort for external exposure, LoadBalancer to balance cloud traffic, and ExternalName to connect to external DNS. Furthermore, the Ingress controller (often NGINX or Traefik) simplifies the entry of HTTP/S traffic into the infrastructure, correctly routing requests and automatically managing TLS security, eliminating the need to manually configure a LoadBalancer for each service.
To summarize: compared to manual Kubernetes management, KaaS eliminates the complexity of daily cluster installation and maintenance, automates patches and updates, reduces risks and errors, and speeds up deployment (we're talking about going from weeks to a few hours). This way, company teams can avoid the typical mistakes made with Kubernetes. Most importantly, they can focus on their applications, with significant savings in time and resources.
Adopting a Kubernetes as a Service solution brings numerous concrete advantages: first, deployment and scaling become immediate and reliable operations thanks to the native integration of features like the Horizontal Pod Autoscaler (HPA) and the Cluster Autoscaler. These automatically adjust the number of pods or nodes based on real demand, preventing waste or overload and reducing provisioning times from hours to minutes.
Secondly, automated cluster management is completely delegated to the provider (including updates, security patches, backups, and recovery), with tools like Azure AKS or GKE offering integrated SLAs and restore tools, which relieves the DevOps team of these responsibilities.
Another important benefit is access to other advanced features, which would require complex manual setups in a DIY context. In addition to the more common HPA and Cluster Autoscaler, you can easily leverage:
The result of all this? Cost savings due to a more efficient use of resources: think of 24/7 services with variable loads that scale up during peak hours and then completely reduce costs based on real consumption. For example, companies that adopt KaaS in production report a 20-30% reduction in infrastructure costs, simply by enabling autoscaling and managed backups.
Of course, there's a flip side. Later, we'll discuss the potential disadvantages of KaaS. However, we'll also show how an expert technology partner like SparkFabrik can effectively mitigate these drawbacks.
To effectively adopt Kubernetes as a Service, you need to rely on specific best practices. First, it's crucial to plan regular updates and patches for the control plane and nodes, preferably by enabling the automatic updates offered by providers like GKE or AKS, to reduce the attack surface and immediately benefit from the latest patches.
In parallel, an effective access management strategy must be implemented. Best practices involve strictly applying the Principle of Least Privilege (PoLP), which assigns each user only the minimum necessary permissions. This can be applied in conjunction with Role-Based Access Control (RBAC), defining specific roles and bindings for users, service accounts, and applications, avoiding excessive permissions like cluster-admin and periodically reviewing policies.
Network Policies become essential for isolating traffic between Pods, reducing the risk of lateral movement within a network in case of compromise. In a microservices architecture, an attacker could exploit the compromise of a single pod (e.g., the frontend) to reach other pods (like the backend or a database) that would otherwise be protected. Network Policies address this risk by setting precise rules on which pods can communicate with each other.
To ensure availability and scalability, it is essential to configure HPA/VPA to automatically adjust replicas and resources based on real metrics. In the same way, you must define Pod Disruption Budgets to maintain a minimum number of active Pods during maintenance or updates.
Monitoring and logging must transition to centralized structures with Prometheus/Grafana for metrics, EFK/ELK for logs, and Alerting configured to track errors, anomalous resources, and unauthorized attempts.
The security of container images is obviously fundamental to prevent vulnerabilities and malware from spreading in the cluster. This must be managed by adopting several strategies:
No less important is the protection of the etcd datastore, a critical Kubernetes component that stores the configuration and state of the entire cluster. Its compromise would have extremely serious consequences, which is why its protection is of utmost importance. It must be encrypted in transit and at rest (protecting encrypted data from interception or direct access), as well as isolated with TLS, which ensures secure communication. It must also be subjected to frequent backups (using native tools like etcdctl or solutions with snapshots in object storage), but having backups is not enough: it's essential to regularly test the restore procedures as well.
Finally, introducing Admission Controller (such as OPA/Gatekeeper, components that intercept and can reject requests directed at the cluster), setting resource requests/limits to prevent oversubscription and overloads, and ensuring readiness/liveness probes (periodic checks to monitor a pod's "health status") increases security and resilience.
Thanks to all these measures, a KaaS cluster becomes highly secure, performant, and ready to handle incidents, providing a solid foundation on which to build mission-critical applications.
If you want to delve deeper into Kubernetes security, check out our insights on Container Security or more generally on Cloud security (Italian).
As we've seen, Kubernetes as a Service (KaaS) revolutionizes the DevOps application lifecycle by offering a robust infrastructure abstraction that allows teams to focus on code, automation, and release quality. Native support for CI/CD is clearly evident: Kubernetes allows you to integrate pipelines with tools like GitLab CI/CD, Jenkins, or Azure DevOps, enabling automatic builds, tests, and deployments directly on clusters, with rollbacks and updates without downtime (blue/green, canary).
Containerization ensures consistency across environments and prevents classic "works-on-my-machine" problems, improving productivity and predictability and accelerating time-to-market. Thanks to automatic scalability (HPA, VPA, cluster autoscaler, KEDA), pipelines can dynamically adjust resources based on load, ensuring efficiency and optimized costs.
Kubernetes maintains the "desired state" by automatically replacing downed pods and supporting end-to-end resilience, freeing DevOps from the overhead of managing standard infrastructure. The infrastructure abstraction that characterizes KaaS means that DevOps teams no longer have to worry about VM provisioning or networking: they can operate in a declarative GitOps environment, where every change is traceable, versioned, and deployable via commit.
Furthermore, the operational impact is drastically reduced: DevOps teams can spin up on-demand runners for CI, test in isolated namespaces, and then destroy them afterward, eliminating costs related to ad-hoc activated servers. KaaS therefore allows for the creation of robust, resilient, and agile CI/CD and IaC pipelines, with a total focus on application value, not on the underlying infrastructure.
We've mentioned many concepts that deserve further exploration. To do so, you can check out the resources we've created on Infrastructure as Code, GitOps, and Continuous Integration and Delivery.
Finally, we must mention the context of Artificial Intelligence. Indeed, Kubernetes as a Service is proving to be a strategic platform for the deployment and scaling of AI/ML applications, thanks to infrastructure abstraction and built-in features like HPA, VPA, and Cluster Autoscaler.
Tools like the ones just mentioned allow for dynamically allocating resources based on load and GPU or CPU requirements, while the Cluster Autoscaler extends scalability to the node level, preventing waste and ensuring optimal performance for training and inference. In addition, services like Azure AKS offer native integration with GPUs and spot nodes to reduce training costs, while separate Node Pools allow for differentiated workloads (e.g., training vs. inference).
Added to this are auto-scaling solutions based on predictive AI, capable of anticipating traffic spikes and proactively allocating resources, reducing lag during spikes and cutting costs by up to 40-50%. These results are also confirmed by real cases, such as Alibaba CS with AHPA (Adaptive HPA), which increased CPU usage by 10% (reducing wasted or idle CPU capacity) and overall reduced costs by 20% through more efficient use of cloud resources.
Furthermore, KaaS supports complete AI/ML ecosystems through platforms like Kubeflow, which offer interactive notebooks (writing and running code to experiment with models), training pipelines (automating model training and data preparation), operators for frameworks (which allow popular ML frameworks like TensorFlow and PyTorch to be run directly on K8s), and serving via KServe (to make the model usable in production via APIs). In short, these are complete platforms with a suite of specialized tools that provide everything data scientists and ML engineers need to orchestrate the entire model lifecycle on Kubernetes, from the initial experiment to production deployment, without having to worry about the underlying infrastructure.
On the operational front, there are also intelligent plugins and operators (e.g., KubeAI, kgateway, Kubectl-AI) enhanced by AI that simplify the operational management of Kubernetes, for example by generating YAML manifests or providing automated cluster introspection.
Other solutions like kubernetes-sigs/lws (LeaderWorkerSet API) are designed to simplify the deployment and scaling of complex AI models, especially multi-host and multi-node ones, by allowing a group of pods to be treated as a single unit of replication.
To concretely and easily serve LLMs on Kubernetes, there's also vLLM, a tool that allows LLM models to be run in a distributed and scalable way, supporting deployment on both CPUs and GPUs, optimizing resource usage across multiple nodes, and ensuring high availability and resilience in inference. Our Cloud Native Engineers delve into these aspects in the talk "Deploy, scale, serve: gestire motori di Inference AI su Kubernetes" (in Italian).
In summary, KaaS for AI/ML offers a scalable, predictable, secure, and economically efficient foundation for managing complex workflows, with the added benefit of integrated predictive features to optimize costs and performance—a competitive advantage that more and more teams are adopting.
After dwelling on the benefits, it's time to talk about the limitations of the KaaS solution. In fact, although Kubernetes as a Service (KaaS) offers numerous advantages in terms of simplification and automation, it's essential to approach this technology with a full awareness of the potential challenges you may face.
So let's look at the main issues and how SparkFabrik's consultative approach and specialized services can help mitigate them.
With KaaS, the security of the Kubernetes infrastructure is inevitably shared between the provider and the customer. Fully understanding where the provider's responsibilities end and the user's begin is fundamental, but not always immediate.
The SparkFabrik Approach: To prevent this risk from becoming a threat, we work alongside our clients to clearly define the perimeters of responsibility. Through personalized Kubernetes consulting and the structured path of the Cloud Native Journey, we guide teams in implementing the security best practices that are the company's responsibility (and not the provider's). This includes adopting the Principle of Least Privilege (PoLP) with RBAC, configuring strict network policies, secure container image management, continuous monitoring, and much more.
Relying on a single KaaS provider can, unfortunately, create a strong technological dependency, making future migrations to other providers or to on-premise solutions complex and costly.
The SparkFabrik Approach: At SparkFabrik, we favor solutions based on open standards and Open Source technologies precisely to ensure maximum portability and flexibility. Our goal is to transfer knowledge and tools to make the team autonomous, avoiding the creation of technological or contractual dependencies (no lock-in). Therefore, our consulting is aimed at designing architectures that, while leveraging the benefits of KaaS, minimize the risk of lock-in, for example through the use of standard Kubernetes configurations and the adoption of DevOps practices that facilitate hybrid or multi-cloud management.
KaaS, by offering a "Managed Control Plane," abstracts much of the infrastructural complexity. By its nature, this leads to less direct control over the underlying hardware and some advanced network configurations compared to a self-managed Kubernetes deployment.
The SparkFabrik Approach: Every organization has different control needs, and we know this well. That's why we support you in selecting the KaaS provider and the service level that best aligns with your specific needs for governance and customization. We work to optimize the available configurations on the chosen platform, maximizing your control within the service's limits. If more granular control is an essential requirement, don't worry. We have all the experience necessary to guide you in the implementation and management of dedicated Kubernetes clusters or hybrid solutions, balancing the advantages of abstraction with the need for flexibility and direct control.
To conclude, we can say that addressing these challenges requires expertise, experience, and method. Exactly what we can offer you to transform the potential pitfalls of KaaS into opportunities for growth and optimization.