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CoreWeave operates a cloud platform for artificial intelligence (AI) and high-performance computing (HPC). Customers rent access to GPU computing, storage, networking and software to train or run AI models and other demanding workloads. Rather than selling GPUs alone, CoreWeave combines data-center infrastructure with tools for provisioning, scheduling and operating large-scale compute.
What CoreWeave sells
CoreWeave is a cloud service provider: it operates or secures computing infrastructure and sells customers access to that capacity. Its platform brings together several layers:
- Compute: GPU clusters for workloads that benefit from parallel processing, alongside CPUs for other computing tasks.
- Networking: high-speed connections between GPU servers, important when a workload is distributed across many machines.
- Storage: object and file storage designed for AI workloads and their data needs.
- Software and operations: tools to provision resources, schedule jobs, orchestrate workloads and monitor operations.
CoreWeave describes Mission Control as its proprietary orchestration and operations software. Its Slurm on Kubernetes (SUNK) offering supports large-scale research and training workloads. The company also describes managed and application software services, including developer tools. This integrated approach is intended to help customers operate workloads across infrastructure rather than assemble and manage each layer separately. CoreWeave’s FY2025 Form 10-K sets out the platform and its services.
How a GPU cloud supports AI workloads
A GPU cloud gives customers remote access to GPU computing capacity without requiring them to own and operate the underlying servers. In broad terms, training uses compute to build or refine a model; inference runs a trained model to produce outputs. CoreWeave identifies both, along with agentic AI, agent development and specialized workloads, as uses for its cloud.
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Large training jobs can require many GPUs to work together while moving substantial volumes of data. That makes GPU availability only one part of workload fit: customers may also care about the connections between servers, storage throughput, software compatibility and how the system is provisioned and monitored.
CoreWeave says its data-center facilities vary in size and location. It describes smaller sites as suited to inference closer to users, and larger sites as able to support high-density training. The practical implication is that location and facility design can matter as much as raw compute capacity, depending on the workload.
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How CoreWeave makes money
CoreWeave earns revenue by providing cloud computing services, including compute enabled by its software and infrastructure optimized for AI and HPC. It sells access through committed contracts and on-demand, pay-as-you-go service. The company says its committed contracts are take-or-pay and typically involve customer prepayment before service access. These are contractual arrangements, not simply charges for each GPU-hour used.
Committed contracts made up over 98% of revenue in 2025, compared with 96% in 2024 and 88% in 2023, according to the company’s FY2025 Form 10-K. The figures show how strongly the business relies on contracted commitments, while on-demand access remains available.
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CoreWeave reported the following revenue and net losses for the fiscal years ended December 31:
| 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, reported by CoreWeave, show rapid revenue growth alongside net losses in every year listed; growth had not yet translated into net profitability. The company’s Form 10-K details its financial results and business risks.
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What the backlog figure 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 delivery and service-availability requirements, so it should not be read as revenue already earned or cash guaranteed to arrive. CoreWeave’s FY2025 results announcement provides the figure and its context.
How CoreWeave differs from a general-purpose cloud
CoreWeave positions its platform as purpose-built for the combination of high-density computing, advanced networking, optimized storage and software needed by distributed AI workloads. That is the company’s positioning; it does not establish that general-purpose cloud providers cannot support AI.
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For a customer comparing providers, the useful question is whether a service fits the particular workload. Relevant factors include:
- which GPU types are available and at what scale;
- networking between servers and the throughput available for data movement;
- storage performance and compatibility with the customer’s software;
- provisioning, scheduling, orchestration and operational support;
- facility location and the latency requirements of the workload;
- reliability, contract flexibility and total cost.
CoreWeave’s company filing does not provide a full apples-to-apples price comparison with other cloud providers. Current GPU availability, service prices and individual contract terms can vary and are not established by the financial disclosures cited here.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What could constrain the business
Building or securing the capacity required for AI cloud services takes substantial investment. CoreWeave’s filings identify several risks that affect its ability to turn customer demand into profitable service:
- Capital and financing: the company needs significant capital expenditure and financing to build or secure capacity and acquire equipment.
- Power: access to sufficient electricity and its cost can affect facility availability and operating economics.
- Suppliers and partners: limited suppliers for important components and data-center partner performance can constrain deployment.
- Customer concentration: reliance on a limited number of major customers makes their demand and ability to meet commitments consequential.
- Demand and hardware cycles: continued AI adoption is uncertain, while rapid changes in hardware can affect investment needs and the value of existing capacity.
Committed contracts can improve revenue visibility, but they do not remove the execution, financing, customer-concentration or demand risks described in the company’s FY2025 Form 10-K.
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