Kubernetes Engine (OKE)

Simplify operations of enterprise-grade Kubernetes at scale. Easily deploy and manage resource-intensive workloads such as AI with automatic scaling, patching, and upgrades.

Why Choose OKE?

  • Price-Performance

    OKE is the lowest cost Kubernetes service amongst all hyperscalers, especially for serverless.

  • Autoscaling

    OKE automatically adjusts compute resources based on demand, which can reduce your costs.

  • Efficiency

    GPUs can be scarce, but OKE job scheduling makes it easy to maximize resource utilization.

  • Portability

    OKE is consistent across clouds and on-premises, enabling portability and avoiding vendor lock-in.

  • Simplicity

    OKE reduces the time and cost needed to manage the complexities of Kubernetes infrastructure.

  • Reliability

    Automatic upgrades and security patching boost reliability for the control plane and worker nodes.

OKE use cases

OKE powers OCI AI services

Kubernetes is the go-to platform to deploy AI workloads. OKE powers Oracle Cloud Infrastructure (OCI) AI services.

AI model building

– The initial build stage of an AI project involves defining the problem and preparing data to create models.

– Kubernetes clusters can significantly improve efficiency by granting shared access to expensive and often limited GPU resources while providing secure and centrally managed environments.

Kubeflow, a Kubernetes-related open source project, provides a comprehensive framework designed to streamline the building, training, and deployment of models.

OKE for AI model building

OKE is built on top of OCI, offering a complete stack of high performance infrastructure designed for AI/ML workloads such as:

– The full range of NVIDIA GPUs including H100, A100, A10, etc.

– Ultrafast RDMA networks

Using OKE self-managed nodes, you can run AI/ML building workloads on your Kubernetes clusters.

OKE powers OCI AI services

Kubernetes is the go-to platform to deploy AI workloads. OKE powers OCI AI services.

AI model training

– In model training, data scientists select an algorithm and initiate training jobs using prepared data. This stage requires sophisticated scheduling systems to handle the jobs efficiently.

– Kubernetes projects such as Volcano and Kueue help handle such requirements and make efficient use of compute resources.

– Large-scale distributed training requires low-latency internode communications in the cluster. This is where a specialized ultrafast network with remote direct memory access (RDMA) is needed. It enables data to be moved directly to or from an application’s memory, bypassing the CPU to reduce latency.

OKE for AI model training

OKE is built on top of OCI, offering a complete stack of high performance infrastructure designed for AI/ML workloads such as:

– The full range of NVIDIA GPUs including H100, A100, A10, etc.

– Low-latency, ultra-high performance RDMA networks

Using OKE self-managed nodes, you can run AI/ML training on your Kubernetes clusters.

OKE powers OCI AI services

Kubernetes is the go-to platform to deploy AI workloads. OKE powers OCI AI services.

AI model inferencing (serving)

– AI model inferencing is where Kubernetes really shines. Kubernetes can automatically scale the number of inference pods up or down based on demand, ensuring efficient use of resources.

– Kubernetes provides sophisticated resource management, including the ability to specify CPU and memory limits for containers.

OKE for AI model inference

OKE is designed with resilience at its core, leveraging Kubernetes’ built-in pod autoscaling to scale worker nodes based on usage. Worker nodes can be distributed across multiple fault and/or availability domains for high availability.

OKE virtual nodes provide a serverless Kubernetes experience. They only need to scale at the pod level, without ever scaling worker nodes. This allows for quicker scaling and more economical management since service fees are based solely on the pods in use.

Virtual nodes are well-suited for inference workloads and can use Arm processors, which are becoming a much more attractive option for AI inference—especially when GPUs are in short supply.

Existing applications can benefit by migrating to OCI and OKE

OKE offers lower total cost of ownership and improved time to market.

OKE simplifies operations at scale in the following ways:

  • Lift and shift; there’s no need to rearchitect
  • Reduce operations burden with automation
  • Save time on infrastructure management
  • Increase resource utilization and efficiency
  • Improve agility, flexibility, uptime, and resilience
  • Reduce compliance risk and enhance security

Microservices offer many advantages over monolithic applications

Future-proof your applications with an OKE-centric microservices architecture.

  • Architecture modernization
  • Faster pace of innovation
  • Deployment automation
  • Parallel development
  • Easier scalability
  • Higher reliability
  • More flexibility
  • Greater agility

“Many OCI AI services run on OCI Kubernetes Engine (OKE), Oracle’s managed Kubernetes service. In fact, our engineering team experienced a 10X performance improvement with OCI Vision just by switching from an earlier platform to OKE. It’s that good.”

Jun Qian

VP of OCI AI Services, Oracle Cloud Infrastructure

Customers innovating with cloud native services on OCI

Explore more customer stories

Get started with Kubernetes Engine

  • Deploy a simple containerized app using OKE managed nodes

    Deploy simple microservices packaged as Docker containers and communicate via a common API.


  • Deploy a Kubernetes cluster with virtual nodes

    Discover best practices for deploying a serverless virtual node pool using the provided Terraform automation and reference architecture.


  • Discover patterns to optimize your Kubernetes resources

    Find out how Tryg Insurance reduced their costs by 50% via dynamic rightsizing.


January 9, 2024

Network Troubleshooting for OCI Kubernetes Engine Clusters

Greg Verstraeten, Senior Principal Product Manager

Organizations adopting Kubernetes face many challenges in getting networking configured correctly. Troubleshooting a Kubernetes cluster’s network configuration involves analyzing the cluster components’ requirements, carefully walking through multiple route tables, and scrutinizing network security rules along the multihop network paths. If done manually, it can be error-prone. OCI Kubernetes Engine (OKE) provides a Quick Create workflow, a Quick Start experience that automates the creation of a virtual network, a Kubernetes cluster, and nodes.

Read the complete post

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Resource Manager

Terraform-based cloud infrastructure automation

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