Managed Kubernetes

CoreWeave Kubernetes Service (CKS) is a managed Kubernetes platform designed specifically for AI and machine learning workloads.

Reviewed by 7wData

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CoreWeave Kubernetes Service (CKS) is a managed Kubernetes platform designed specifically for AI and machine learning workloads. It targets organizations that need to train large models or run inference at scale—teams that have outgrown single-cluster setups and want cloud-like flexibility without the overhead of virtualized infrastructure. Unlike general-purpose managed Kubernetes services from hyperscalers, CKS is built from the ground up for GPU-intensive computation, making it a fit for AI startups, research labs, and enterprises that demand supercomputer-level performance.

CKS runs Kubernetes directly on bare-metal servers, eliminating the hypervisor layer entirely. Each node uses NVIDIA BlueField DPUs to offload networking, security, and resource management, freeing GPUs for computation. The platform supports scaling across clusters with 100k+ GPUs and integrates with AI orchestration tools like Slurm, KubeFlow, and KServe. CoreWeave's backbone uses NVIDIA Quantum-2 InfiniBand with non-blocking fat-tree topology and SHARP for gradient synchronization. CKS delivers 20% higher GPU cluster performance compared to alternatives, according to CoreWeave. The service also provides granular observability tools for real-time insights and automatically identifies and removes problematic nodes.

CoreWeave competes directly with Amazon EKS, Azure AKS, and Google GKE, but differentiates itself through a bare-metal architecture that avoids virtualization tax and a network purpose-built for distributed AI training. While hyperscalers offer broader geographic coverage and more integrated services, CoreWeave has secured GPU supply that even Microsoft and Google struggled to obtain during shortages. The company's fleet exceeds 250,000 GPUs across 32+ data centers, and it was the first to deploy NVIDIA GB200 NVL72 and GB300 NVL72 commercially. Major customers include OpenAI ($22.4B total contract value) and Meta ($14.2B deal).

The primary trade-off is that CKS is optimized almost exclusively for AI/ML workloads—it lacks the broad ecosystem of general-purpose cloud services (databases, serverless functions, object storage) that hyperscalers offer. Pricing is not publicly listed in a simple tier structure; instead, users must contact sales for custom quotes, which introduces friction for small teams. The platform's reliance on NVIDIA hardware creates vendor lock-in risk. Additionally, CoreWeave's rapid growth (revenue from $16M in 2022 to $1.92B in 2024) means operational maturity may lag behind established competitors.

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How it works

  1. Bare-metal Kubernetes

    Runs Kubernetes directly on bare-metal servers, removing the hypervisor layer to reduce latency and improve GPU utilization.

  2. NVIDIA BlueField DPU offloading

    Each node uses BlueField DPUs to offload networking and security tasks, freeing GPUs for computation.

  3. 100k+ GPU cluster scaling

    Supports scaling across clusters with 100k+ GPUs, enabling supercomputer-level distributed training.

  4. AI workload orchestration integration

    Integrates with Slurm, KubeFlow, and KServe for managing AI training and inference pipelines.

  5. InfiniBand fabric with SHARP

    Uses NVIDIA Quantum-2 InfiniBand with non-blocking fat-tree topology and SHARP for fast gradient synchronization.

  6. Granular observability

    Provides real-time insights and automatically identifies and removes problematic nodes to maintain cluster health.

  7. Virtual Private Cloud connectivity

    Offers secure connections via VPC, with custom network policies and privileged access controls.

Strengths and trade-offs

Strengths

  • Delivers 20% higher GPU cluster performance compared to alternatives by eliminating the hypervisor layer.
  • Supports scaling across clusters with 100k+ GPUs, enabling training of models that would be impractical on other managed Kubernetes services.
  • Secured GPU supply during shortages that left even Microsoft and Google scrambling for allocation, ensuring availability for large workloads.
  • First to deploy NVIDIA GB200 NVL72 and GB300 NVL72 commercially, providing access to the latest hardware before competitors.

Trade-offs

  • Pricing is not publicly listed; users must contact sales for custom quotes, creating friction for small teams or exploratory projects.
  • Optimized almost exclusively for AI/ML workloads, lacking the broad ecosystem of general-purpose cloud services (databases, serverless, object storage).
  • Heavy reliance on NVIDIA hardware creates potential vendor lock-in, limiting flexibility to use alternative GPU architectures.
  • Rapid growth from $16M revenue in 2022 to $1.92B in 2024 means operational maturity and support processes may lag behind established competitors.

Pricing context

Not publicly listed; custom quotes require contacting sales. On-demand GPU pricing is available per hour (e.g., NVIDIA HGX H100 at $49.24/hr, NVIDIA L40S at $18.00/hr), but CKS-specific pricing is not disclosed.

Getting started with Managed Kubernetes

  1. Sign up for CoreWeave

    Visit the CoreWeave website and fill out the contact form to request access to the Managed Kubernetes service. A sales representative will reach out to discuss your requirements and provide a custom quote for your AI workloads.

  2. Connect your GPU cluster

    After receiving your account credentials, log into the CoreWeave dashboard and configure your Virtual Private Cloud (VPC) settings. Set up network policies and privileged access controls to securely connect your existing infrastructure to the managed Kubernetes cluster.

  3. Configure AI workload orchestration

    Integrate your AI orchestration tools—such as Slurm, KubeFlow, or KServe—with the CoreWeave Kubernetes Service. Define your training or inference pipelines, specifying the GPU resources and scaling parameters needed for your models.

  4. Deploy your first model

    Use kubectl or the CoreWeave dashboard to deploy a sample AI model onto the bare-metal Kubernetes cluster. Monitor the deployment logs to verify that the GPUs are utilized efficiently and that the InfiniBand fabric is handling gradient synchronization correctly.

  5. Schedule regular performance checks

    Set up automated observability alerts using CoreWeave's granular monitoring tools to track GPU utilization, node health, and network throughput. Schedule weekly reviews to identify and remove problematic nodes, ensuring consistent cluster performance for ongoing training runs.

Frequently Asked Questions

What is managed Kubernetes for AI workloads?

Managed Kubernetes for AI workloads is a cloud service that automates deploying, scaling, and managing containerized AI applications. CoreWeave Kubernetes Service (CKS) is one example, built specifically for GPU-intensive tasks like training large models and running inference at scale.

How does bare-metal Kubernetes improve GPU performance?

Bare-metal Kubernetes runs directly on servers without a hypervisor, reducing latency and improving GPU utilization. CoreWeave uses NVIDIA BlueField DPUs to offload networking and security tasks, freeing GPUs for computation. This architecture delivers 20% higher GPU cluster performance compared to virtualized alternatives.

Can managed Kubernetes scale to 100k GPUs for AI training?

Yes, CoreWeave Kubernetes Service supports scaling across clusters with over 100,000 GPUs. This enables training massive AI models that would be impractical on other managed Kubernetes services. The platform uses NVIDIA Quantum-2 InfiniBand with non-blocking fat-tree topology for fast gradient synchronization.

How does CoreWeave Kubernetes compare to Amazon EKS or Google GKE?

CoreWeave Kubernetes Service differs by using bare-metal servers and NVIDIA DPUs to avoid virtualization tax, offering higher GPU performance. However, it lacks the broad ecosystem of general-purpose services like databases and serverless functions that hyperscalers provide. It is optimized almost exclusively for AI and ML workloads.

What is the pricing for CoreWeave Kubernetes Service?

Pricing for CoreWeave Kubernetes Service is not publicly listed in a simple tier structure. Users must contact sales for custom quotes, which can create friction for small teams. On-demand GPU pricing is available per hour, such as NVIDIA HGX H100 at $49.24/hr, but CKS-specific pricing is not disclosed.

What are the risks of using a managed Kubernetes service focused on NVIDIA hardware?

The primary risk is vendor lock-in, as CoreWeave relies heavily on NVIDIA hardware, limiting flexibility to use alternative GPU architectures. Additionally, the platform's rapid growth from $16M revenue in 2022 to $1.92B in 2024 means operational maturity and support may lag behind established competitors.

Alternatives

How Managed Kubernetes compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

Managed Kubernetes

Pricing
Not publicly listed; custom quotes require contacting sales. On-demand GPU pricing is available per hour (e.g., NVIDIA HGX H100 at $49.24/hr, NVIDIA L40S at $18.00/hr), but CKS-specific pricing is not disclosed.
Target
CoreWeave Kubernetes Service (CKS) is a managed Kubernetes platform designed specifically for AI and machine learning workloads.
Strength
Delivers 20% higher GPU cluster performance compared to alternatives by eliminating the hypervisor layer.
Watch for
Pricing is not publicly listed; users must contact sales for custom quotes, creating friction for small teams or exploratory projects.

Amazon EKS

Pricing
$0.10 per cluster per hour plus EC2 instance costs.
Target
Enterprises needing deep AWS integration and scale.
Deployment
AWS cloud
Strength
Tight integration with AWS services like IAM and VPC.
Watch for
Complex setup and higher operational overhead than GKE or AKS.

Azure AKS

Pricing
Free control plane; pay only for worker nodes and associated resources.
Target
Organizations already using Microsoft ecosystem and Azure.
Deployment
Azure cloud
Strength
Seamless integration with Active Directory and Visual Studio.
Watch for
Pricing can escalate with add-ons like Azure Policy and monitoring.

Google GKE

Pricing
$0.10 per cluster per hour for standard; Autopilot from $0.0011 per vCPU per hour.
Target
Teams wanting advanced Kubernetes features and Autopilot mode.
Deployment
Google Cloud
Strength
Autopilot mode reduces node management overhead significantly.
Watch for
Higher egress costs and less multi-cloud flexibility than EKS.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. www.coreweave.com
  2. introl.com
  3. www.coreweave.com
  4. www.coreweave.com