What is CoreWeave?

CoreWeave is a specialized cloud infrastructure provider founded in 2017 in New Jersey by Michael Intrator, Brian Venturo, Brannin McBee, and Peter Salanki. Originally started by three commodities traders, CoreWeave has grown into a leading GPU cloud platform built specifically for AI and machine learning workloads. Based in Livingston, New Jersey, with approximately 1,871 employees as of January 2026, CoreWeave offers on-demand access to NVIDIA H100 and A100 GPUs with significantly lower pricing than traditional hyperscalers. The platform provides Kubernetes-native orchestration, fast networking, and flexible scaling, making it popular with AI labs, research institutions, and startups that need large GPU clusters without long-term commitments. CoreWeave's infrastructure is designed from the ground up for GPU-accelerated workloads, offering up to 60% discounts over on-demand prices for committed usage, with transparent pricing that doesn't charge for data egress, IOPS, or core networking services.

Key features

Core capabilities this platform advertises.

Large-scale GPU clusters (H100, A100)
InfiniBand networking for distributed training
Kubernetes-native orchestration
On-demand and reserved capacity
Bare-metal performance

Strengths and tradeoffs

What this tool does well, and the limitations to keep in mind.

Pros

Significantly lower GPU pricing compared to AWS, Azure, and GCP hyperscalers
Purpose-built for AI workloads with optimized networking and Kubernetes orchestration
Transparent pricing with no hidden fees for data egress or core services
Up to 60% discounts available for committed usage, excellent for predictable workloads

Cons

No free tier or trial period, requiring upfront payment commitment

Plans & pricing

What's included in each plan, and how the tiers compare.

On-Demand GPU

Starting at $4.76

Per hour (H100 PCIe)

Pay-as-you-go pricing
Immediate availability
No commitments
Free data egress
Free VPC setup

Committed Usage

Common use cases

AI companies and startups that need large-scale GPU clusters for training and inference

Large language model training
Distributed training across GPU clusters
High-performance inference at scale
AI startup compute infrastructure
Batch processing and fine-tuning

Using CoreWeave with Respan

CoreWeave and Respan together provide powerful GPU infrastructure with comprehensive AI observability. Run your models on CoreWeave's cost-effective GPUs while monitoring performance and costs with Respan.

Track costs across CoreWeave GPU instances to optimize your compute spending
Monitor model inference performance on CoreWeave infrastructure with Respan's detailed metrics
Compare GPU utilization and LLM costs across CoreWeave and other cloud providers
Debug model performance issues by correlating Respan traces with CoreWeave resource metrics