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- Google Kubernetes Engine
- Azure Kubernetes Service
- Amazon EKS
|Small Businesses (1-50 employees)||0%|
|Mid-Size Companies (51-500 employees)||50%|
|Enterprises (more than 500 employees)||50%|
|Supported Languages||A managed Kubernetes offering to deliver powerful tools, an intuitive user experience and built-in security for rapid delivery of applications that you can bind to cloud services related to IBM Watson®, IoT, DevOps and data analytics. As a certified K8s provider, IBM Cloud Kubernetes Service provides intelligent scheduling, self-healing, horizontal scaling, service discovery and load balancing, automated rollouts and rollbacks, and secret and configuration management. The Kubernetes service also has advanced capabilities around simplified cluster management, container security and isolation policies, the ability to design your own cluster, and integrated operational tools for consistency in deployment. Visit our Docs pages for pricing and support information.|
It is not appropriate for really monolithic applications that require a lot of data in memory that is shared with multiple threads (e.g. AI applications).
- DEV / QA / SIT deployments: You can use IBMCKS for DEV / QA or SIT environments. Since IBM CKS is a managed service, you can let the deveolping team to be in charge of the infraestructure.
- Multicloud deployments: Working with ISTIO mesh works perfect on IBM CKS.
- Network intensive deployments: IBM's network is quite powerfull.
- Docker containers
- Production Workloads: Is never a good idea to use K8s for production if you don't have a team that fully understands how to operate K8s. K8s is one of the hardest techonologies to use due it's complexity.
- Workloads that requieres KERNEL tunning: You can't modify Kernel parameters on Managed K8s like CKS. If you are running a workload like REDIS that needs certain KERNEL parameters to be set, I highly advice you to use K8s Open Source.
- High performance workloads (GPU): IBM CKS does not offers GPUs on their nodes.