Cloud infrastructure platform providing GPU-accelerated compute services specifically designed for AI and machine learning workloads.
Cloud infrastructure platform providing GPU-accelerated compute services specifically designed for AI and machine learning workloads.
CoreWeave is an Infrastructure cloud platform that delivers GPU-accelerated compute at scale for AI, ML, and HPC workloads, with on-demand pricing starting at around $2.06/hr for NVIDIA A100 GPUs. It serves AI startups, research labs, and enterprises that need dedicated high-performance GPU clusters without building their own data centers.
Founded in 2017 and headquartered in Livingston, New Jersey, CoreWeave has grown into one of the largest specialized GPU cloud providers in the United States. The company went public on the Nasdaq in March 2025 at a valuation of approximately $23 billion, after raising over $12 billion in debt and equity financing. CoreWeave operates 32+ data centers across the US and Europe, housing tens of thousands of NVIDIA GPUs including the latest H100, H200, and GB200 accelerators. The platform is built on Kubernetes-native infrastructure, giving engineering teams familiar orchestration tools while providing bare-metal-level GPU performance. Unlike hyperscalers such as AWS, Azure, and GCP, CoreWeave focuses exclusively on GPU compute, which allows it to offer purpose-built networking (InfiniBand), storage (high-throughput NVMe), and scheduling optimized specifically for AI training and inference workloads.
CoreWeave's product lineup includes GPU Instances for on-demand and reserved compute, Kubernetes-native orchestration via their managed control plane, high-performance block and object storage, and InfiniBand networking for multi-node training. The platform supports the full NVIDIA GPU lineup from RTX A4000/A5000 for inference and rendering to A100 and H100/H200 for large-scale model training. Major customers include Microsoft (which signed a multi-billion dollar agreement), as well as AI labs and enterprises running foundation model training. CoreWeave also provides Virtual Workstations for creative professionals doing rendering and VFX, though its core market remains AI/ML infrastructure.
Compared to the 35+ other Infrastructure tools in our directory, CoreWeave stands out for its singular focus on GPU compute and its Kubernetes-native approach. While hyperscalers offer GPUs as one service among hundreds, CoreWeave's entire stack is optimized for GPU workloads, resulting in faster provisioning times (often minutes vs. weeks for reserved capacity on AWS or GCP), lower latency networking between GPU nodes, and more predictable performance. The trade-off is a narrower service portfolio — teams needing databases, serverless functions, or CDN services will still need a traditional cloud provider alongside CoreWeave. For organizations whose primary bottleneck is GPU availability and performance, CoreWeave offers a compelling specialized alternative to the general-purpose hyperscalers.
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CoreWeave maintains one of the largest commercial fleets of NVIDIA GPUs outside the hyperscalers, including H100, H200, and next-generation GB200 accelerators. This scale allows the company to provision clusters of thousands of GPUs for single training runs, with dedicated allocation that avoids the noisy-neighbor performance issues common in shared cloud environments.
Unlike hyperscalers that bolt GPU support onto existing VM-based architectures, CoreWeave built its platform on Kubernetes from the ground up. This means native support for container orchestration, GPU-aware scheduling, auto-scaling based on GPU utilization metrics, and seamless integration with MLOps tools like Kubeflow, Ray, and Argo Workflows.
CoreWeave provides NVIDIA InfiniBand interconnects (up to 400 Gb/s) between GPU nodes, which is critical for distributed training workloads. This high-bandwidth, low-latency networking enables near-linear scaling efficiency when training across hundreds of GPUs, significantly reducing training time and cost compared to Ethernet-based alternatives.
The platform offers NVMe-based block storage and scalable object storage optimized for AI workloads. Block storage delivers low-latency random I/O for checkpoint writing during training, while object storage provides cost-effective capacity for large datasets and model artifacts. Storage is co-located with GPU compute to minimize data transfer bottlenecks.
CoreWeave supports multiple consumption models including on-demand instances billed per-second, reserved capacity with 1-3 year commitments at discounted rates, and spot-like preemptible instances for fault-tolerant workloads. Virtual Servers provide a VM-like experience for teams not yet on Kubernetes, while bare-metal options deliver maximum performance for the most demanding workloads.
From ~$2.06/hr (A100 80GB) to ~$4.76/hr (H100 SXM)
20-30% discount off on-demand rates
35-50% discount off on-demand rates
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CoreWeave completed its IPO on the Nasdaq in March 2025 at a ~$23 billion valuation. The company has been expanding its data center footprint to 32+ locations and adding NVIDIA GB200 and Blackwell-architecture GPUs to its fleet. Microsoft expanded its multi-billion-dollar compute agreement with CoreWeave, and the company has continued to scale capacity to meet surging demand from AI model training customers.
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