CoreWeave operates a cloud platform built for artificial intelligence (AI) and high-performance computing (HPC). Customers rent GPU computing power along with storage, networking and software to train, fine-tune and run AI models. It earns most of its revenue through customer commitments, while also offering on-demand access.
What CoreWeave sells
CoreWeave is a cloud service provider: it operates or secures the infrastructure and sells customers access to it rather than selling them GPUs to install and manage themselves. Its platform combines several layers:
- Compute: GPU servers for demanding AI and HPC workloads, supported by CPUs.
- Networking: high-speed connections between servers, which help coordinate work distributed across GPU clusters.
- Storage: object and file storage designed for AI workloads and their data needs.
- Software and operations: tools for provisioning, scheduling, orchestration and observability, as well as managed and application software services.
CoreWeave calls its proprietary orchestration and operations software Mission Control. For large-scale research and training workloads, it also offers Slurm on Kubernetes (SUNK). These software layers help customers deploy and manage workloads across infrastructure; the offer is therefore broader than renting GPU capacity alone. CoreWeave’s FY2025 Form 10-K describes the platform and services.
How a GPU cloud workload works
A customer uses cloud resources instead of buying and operating the full hardware stack on site. Depending on the task, the customer provisions GPU compute, connects it to storage and networking, and uses platform software to run and monitor the workload.
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Training and fine-tuning
Training uses compute to build a model from data; fine-tuning further adapts a model. Large jobs may be distributed across many GPUs, making the connections between servers, data throughput and job orchestration important alongside the GPUs themselves.
Inference and agent workloads
Inference is running a trained model to produce outputs. CoreWeave identifies inference, agentic AI, agent development and specialized workloads among the uses for its platform. Its filing says its facilities vary in size and location: smaller sites are suited to inference closer to users, while larger sites support high-density training.
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Why use a specialized GPU cloud?
CoreWeave positions its platform as purpose-built for the combination of high-density compute, advanced networking, optimized storage and software required by distributed AI workloads. That is the company’s positioning, not proof that general-purpose cloud services cannot support AI. The practical question for a customer is whether a provider’s available infrastructure and services fit a particular workload.
Useful comparison points include GPU type and scale, network performance and data throughput, software compatibility and operational support, facility location and latency, reliability, contract flexibility and total cost. CoreWeave’s filing does not provide a complete apples-to-apples price comparison with other cloud providers, and current GPU availability, service prices and contract terms vary and are not specified here.
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How CoreWeave makes money
CoreWeave charges for cloud computing services, including compute enabled by software and infrastructure optimized for AI and HPC. Customers can access capacity through committed contracts or on demand. The company describes committed contracts as take-or-pay arrangements that typically involve customer prepayment before service access. Those contracts accounted for over 98% of revenue in 2025, compared with 96% in 2024 and 88% in 2023, according to its FY2025 Form 10-K.
The reported financial results show rapid growth alongside ongoing losses:
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| Fiscal year | Revenue | Net loss |
|---|---|---|
| 2023 | $229 million | $594 million |
| 2024 | $1.9 billion | $863 million |
| 2025 | $5.1 billion | $1.2 billion |
These figures are CoreWeave’s reported results for the years ended December 31, from its FY2025 Form 10-K. Revenue growth has not yet meant net profitability. The model requires substantial investment in data-center capacity, servers, networking and related infrastructure before or alongside delivering services.
What its backlog means
CoreWeave announced $66.8 billion in revenue backlog as of December 31, 2025. The company defines this measure as remaining performance obligations plus other amounts it estimates will be recognized in future periods under committed contracts. It is subject to requirements for delivery and service availability, so it is neither revenue already earned nor guaranteed cash. See the FY2025 results announcement for the reported figure and definition.
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What risks shape the business
Committed contracts can give CoreWeave visibility into customer demand, but they do not remove the operating and financial risks of building a large infrastructure business. The company’s FY2025 filing identifies several material exposures:
- Capital and financing: expanding capacity requires significant investment and continued access to financing.
- Power: data centers need sufficient electricity, and power costs affect operations.
- Suppliers and partners: the company depends on limited suppliers for important components and on data-center partners to perform.
- Customer concentration: reliance on a relatively concentrated customer base creates exposure if major customers change their plans or usage.
- Demand uncertainty: future growth depends in part on continued adoption of AI.
- Technology cycles: fast-changing hardware can make infrastructure investment and deployment more complex.
These risks, along with the company’s capital needs and customer concentration, are described in its FY2025 Form 10-K.
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