Research: CoreWeave’s AI Infrastructure and Market Position

They have particular differentiation in 3. Infrastructure Differentiation. it is designed explicitly to operate with GPU infrastructure, and seed investment from Nvidia cements this.

They have been around for eight years(8) years but still have startup characteristics. Their debt profile is high and rates are astronomic. They are working with Bank of America to refinance $1.5Bn debt.

Their efforts in Spain are spectacular and Spain in particular is in dire need of high tech support.

The facility was inaugurated today by the Mayor of Barcelona, Jaume Collboni, and Albert Tort, Secretary of Telecommunications and Digital Transformation of the Government at Generalitat de Catalunya.

 Merlin Edged in Barcelona, Spain

In its first 15MW phase, CoreWeave will deploy 10,224 Nvidia H200 Tensor Core GPUs with InfiniBand connectivity. Blackwell GPUs are expected next year, for a second phase.

The other efforts this week are creating some new unexpected alliances.

OpenAI signed a five-year, $11.9 billion cloud deal with CoreWeave, securing critical GPU resources while receiving $350 million in equity as part of the agreement.

The deal strengthens CoreWeave’s market position ahead of its IPO while reducing reliance on Microsoft, which previously accounted for 62% of CoreWeave’s revenue.

This move escalates OpenAI’s growing independence from Microsoft, as both companies continue developing competing AI models and cloud infrastructure.

Coreweave has rapidly entered the space as a key AI component.

 

My Research report on Coreweave

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Table of Contents**  

1. Introduction …………………………………………. 2
2. CoreWeave’s Key Customers ……………………….. 3
3. Infrastructure Differentiation ……………………. 5
4. Security Framework …………………………………. 8
5. Kubernetes-Native Cloud for AI …………………. 10
6. Competitive Landscape ……………………………. 13
7. Strategic Risks and Challenges …………………. 15
8. Conclusion …………………………………………….. 17
9. Appendices ……………………………………………. 18

 

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1. Introduction (Page 2)  

CoreWeave is a specialized cloud infrastructure provider focused on delivering high-performance GPU computing for artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) workloads. Founded in 2017, the company has positioned itself as a critical enabler for organizations requiring scalable, cost-effective, and low-latency access to advanced GPUs. Unlike hyperscale cloud providers (e.g., AWS, Azure), CoreWeave’s architecture is optimized exclusively for AI, offering bare-metal performance, Kubernetes-native orchestration, and early access to cutting-edge hardware like NVIDIA’s H100 and GB200 GPUs.  

 

This report synthesizes CoreWeave’s customer base, infrastructure advantages, security practices, and competitive positioning, drawing from public disclosures, partnerships, and technical benchmarks.  

 

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2. CoreWeave’s Key Customers (Page 3)  

CoreWeave’s revenue is heavily concentrated among a small cohort of technology leaders and AI innovators:  

 

Major Customers  

• Microsoft: CoreWeave’s largest customer, contributing 62% of 2024 revenue ($10B+ committed through 2030). Microsoft leverages CoreWeave’s infrastructure to support Azure AI services and OpenAI’s workloads.
• Meta: Represents ~15% of revenue, using CoreWeave for AI model training and inference.
• OpenAI: Signed a $12B, 5-year contract in March 2025 for AI infrastructure.
• IBM: Utilizes CoreWeave’s NVIDIA GB200 clusters for training its Granite AI models.
• NVIDIA: A strategic partner and customer, using CoreWeave for internal AI workloads.
• AI Startups: Cohere, Mistral AI, and Stability AI rely on CoreWeave for large-scale model training.

 

Revenue Concentration Risks  

• Top 2 customers (Microsoft + Meta) account for 77% of 2024 revenue.
• Dependency on NVIDIA GPUs and AI industry growth introduces supply chain and market risks.

 

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3. Infrastructure Differentiation (Page 5)  

CoreWeave’s infrastructure outperforms traditional cloud providers in AI workloads due to:  

 

Key Technical Advantages  

• Bare-Metal Kubernetes: Eliminates virtualization overhead, enabling 5-second node provisioning and 8–10x faster inference.
• Latest NVIDIA GPUs: Early access to H100, A100, and GB200 clusters at scale.
• High-Speed Networking: Proprietary NVIDIA Quantum InfiniBand (400 GB/s) for distributed training.
• AI-Optimized Storage: Local Object Transport Accelerator (LOTA™) caches datasets on NVMe disks, reducing latency.

 

Cost Efficiency  

• Claims 80% cost savings vs. AWS/Azure for comparable GPU workloads.
• Transparent, usage-based pricing without long-term commitments.

 

Comparison Table  

Feature

CoreWeave

AWS/Azure/GCP

    GPU Availability

Latest models, large-scale

Limited, delayed access

  Provisioning Speed

5 seconds

Minutes to hours

  Networking Throughput

400 GB/s (InfiniBand)

≤100 GB/s (standard)

  Cost per GPU Hour

$1.50–$4.00 (H100)

$4.00–$8.00 (H100)

  

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4. Security Framework (Page 8)  

CoreWeave employs a multi-layered security strategy:  

 

Key Measures  

• CrowdStrike Falcon Platform: AI-powered threat detection, 24/7 monitoring, and automated response.
• Physical Security: Restricted-access data centers with 24/7 personnel and biometric controls.
• Data Encryption: AES-256 encryption at rest and in transit.
• Logical Isolation: Single-tenant Kubernetes nodes and VPC networking via NVIDIA BlueField-3 DPUs.
• Vulnerability Management: Regular penetration testing and rapid patching.

 

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5. Kubernetes-Native Cloud for AI (Page 10)  

CoreWeave’s Kubernetes-native architecture provides:  

 

Benefits for AI Workloads  

• Elastic Scaling: Auto-scaling GPU clusters in seconds for burst training jobs.
• Distributed Training: Native support for PyTorch, TensorFlow, and Horovod across multi-node clusters.
• CI/CD Integration: DevOps automation via Kubeflow and Argo.
• Portability: Containerized workloads migratable across clouds.

 

Performance Benchmarks  

• 35x faster training times vs. traditional clouds for large language models (LLMs).
• 12 GB/s network transfer speeds for dataset staging.

 

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6. Competitive Landscape (Page 13)  

CoreWeave competes with:  

• Hyperscalers (AWS/Azure/GCP): Lag in GPU availability but offer deeper ecosystem integrations.
• Specialized Providers (Lambda, Cerebras): Focus on niche hardware but lack CoreWeave’s scale.
• AI Startups (Anyscale, Hugging Face): Compete on MLOps tools but rely on third-party clouds.

 

Strategic Weaknesses  

• No proprietary SDK/platform lock-in.
• Dependency on NVIDIA (90% of hardware).

 

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7. Strategic Risks and Challenges (Page 15)  

• Customer Concentration: Loss of Microsoft or Meta could cripple revenue.
• NVIDIA Dependency: Transition to alternative GPUs (e.g., AMD, Groq) remains untested.
• Hyperscaler Competition: AWS/Azure are accelerating GPU procurement and price cuts.

 

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8. Conclusion (Page 17)  

CoreWeave has established itself as the leading GPU cloud for AI workloads, driven by technical excellence, cost efficiency, and partnerships with NVIDIA and top AI labs. However, its long-term success hinges on diversifying its customer base and mitigating risks associated with hyperscaler competition.  

 

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Appendices (Page 18)  

• Customer Contract Summary
• GPU Pricing Comparison
• Security Certification List

 

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End of Report