In modern times, one size doesn’t fit all, especially for demanding workloads like AI model training, rendering, and high-performance computing (HPC). While major cloud providers offer general-purpose infrastructure, newer players like CoreWeave are rethinking what a specialised cloud can look like. Focused on GPUs, low-latency networking, and large-scale parallel processing, CoreWeave is quickly establishing itself as a go-to platform for next-gen workloads.
In this CoreWeave review, we explore the platform’s features, performance, pricing and more so you can understand if CoreWeave is the right fit for your workloads.
What is CoreWeave?
CoreWeave is a US-based specialised cloud provider that focuses on GPU-accelerated workloads, including AI/ML, visual effects, real-time inference, and scientific computing. Founded in 2017, the company has grown rapidly thanks to its strategic emphasis on scalable infrastructure, low-cost GPU access, and purpose-built software orchestration.
Unlike leading cloud platforms like AWS, CoreWeave builds for specific industries that demand raw performance and granular control, such as :
- AI startups
- Model training labs
- VFX studios
- Scientific institutions
Key Features of CoreWeave
The key features of CoreWeave include :
1. Enterprise-Grade GPU Options
CoreWeave offers access to a wide array of NVIDIA GPUs, including :
- H100 (latest generation)
- A100 (40/80GB)
- A40, A30, A10
- RTX 6000, V100
You can select single-GPU or multi-GPU instances with support for NVLink, which enables faster inter-GPU communication, a must for training large language models (LLMs).
2. High-Speed Networking
CoreWeave stands out for its ultra-low-latency, high-bandwidth networking, designed for tightly coupled compute clusters. This enables :
- Faster multi-node training
- Distributed inference
- HPC-style workloads with MPI support
The network architecture includes InfiniBand and RoCE v2, providing serious bandwidth for performance-critical applications.
3. Dynamic Compute Scheduling
CoreWeave uses a Kubernetes-native orchestration engine, giving users access to fine-tuned scheduling, autoscaling, and cost-saving features. With custom resource definitions and workload affinity tools, you can :
- Automatically allocate GPUs across workloads
- Preempt non-critical jobs to save costs
- Burst into high availability during peak times
4. Object and Block Storage
In addition to compute, CoreWeave offers high-performance storage, including :
- NVMe-backed block volumes
- Shared file systems
- S3-compatible object storage
This is especially important for AI/ML training where datasets can scale into terabytes or petabytes.
CoreWeave User Experience
While CoreWeave is optimised for performance, it does require a certain level of technical fluency. You can typically interact with the platform through :
- Web UI for provisioning and monitoring
- Kubernetes CLI (kubectl) for workload management
- Terraform and APIs for infrastructure-as-code
CoreWeave Performance and Reliability
CoreWeave stands out for its remarkable performance and cost-efficiency, offering up to 35 times faster compute and as much as 80% lower pricing compared to general-purpose public clouds like AWS and GCP. This performance advantage is particularly evident in high-throughput, GPU-heavy workloads such as large-scale AI model training, inference pipelines, and scientific simulations.
The platform supports massive GPU clusters with ultra-low latency interconnects, making it ideal for distributed training and parallelised workloads. Technologies like NVLink, RoCE v2, and InfiniBand enable tight coupling between nodes, which is critical for performance-sensitive applications such as LLM training or fluid dynamics simulations.
Another key differentiator is provisioning speed. Unlike AWS EC2 Spot or GCP Preemptible VMs, which often come with unpredictable availability and slower startup times, CoreWeave allows users to spin up GPU instances rapidly, often in under a minute. This is especially valuable for teams running batch jobs or experiments that need quick iterations.
In terms of reliability, CoreWeave provides enterprise-grade SLAs and actively monitors node health. Faulty nodes are automatically removed from production, ensuring high availability. Combined with flexible orchestration through Kubernetes, CoreWeave delivers a stable, performant, and cost-effective solution for organisations that require serious GPU power.
CoreWeave Pricing
Pricing is one of CoreWeave’s main differentiators. By cutting out unnecessary services and focusing purely on compute, it can offer :
- Up to 80% cheaper rates than AWS or GCP
- Tiered pricing based on reservation (on-demand vs committed)
- Custom quotes for large-scale deployments
| GPU Model | Price Per Hour |
| NVIDIA H100 PCIe | $4.25 |
| A100 80GB PCIe | $2.21 |
| A100 80GB NVLINK | $2.21 |
| A100 40GB PCIe | $2.06 |
| A100 40GB NVLINK | $2.06 |
| RTX A6000 | $1.28 |
| A40 | $1.28 |
| Tesla V100 NVLINK | $0.80 |
| RTX A5000 | $0.77 |
| RTX A4000 | $0.61 |
| Quadro RTX 5000 | $0.57 |
| Quadro RTX 4000 | $0.24 |
| NVIDIA HGX H100 | $4.76 |
In addition, you can :
- Preempt workloads to lower costs further
- Combine pricing with storage and bandwidth packages
- Choose committed use contracts for even better deals
CoreWeave Use Cases
CoreWeave is ideal for :
- LLM training and fine-tuning
- Multi-node inference pipelines
- VFX rendering pipelines (e.g., Blender, Houdini)
- Scientific simulations (e.g., genomics, fluid dynamics)
- MLOps teams are building custom training workflows
CoreWeave Community and Support
CoreWeave provides a robust support system to ensure users can troubleshoot and resolve issues efficiently. Their infrastructure is continuously monitored through active and passive tests, along with telemetry collection. If a node experiences any problems, it’s automatically taken out of production by its node lifecycle process, minimising downtime without user intervention.
For direct support, CoreWeave offers two primary contact methods :
- Cloud Console Help Portal : The preferred way to get support is through the Help button located in the bottom corner of the CoreWeave Cloud Console. After logging in, users can submit a request by filling out a short form that includes their organisation, namespace and contact information.
- Email Support : If the console isn’t accessible, users can contact the support team at support@coreweave.com.
Is CoreWeave Worth It?
If you’re building and scaling AI models or HPC applications and you need raw GPU power, flexible orchestration, and affordable pricing, CoreWeave is one of the best platforms on the market today. It’s not a plug-and-play solution for beginners, but for technical teams, it offers a whole new level of control and performance. The ability to reserve clusters, preempt jobs and scale on Kubernetes gives it a serious edge in enterprise and research contexts.
FAQs
1. What types of workloads is CoreWeave best suited for?
CoreWeave is ideal for GPU-intensive tasks like AI model training, inference, VFX rendering, and scientific computing.
2. Does CoreWeave support multi-GPU setups with NVLink?
Yes, CoreWeave offers multi-GPU instances with NVLink support for high-bandwidth interconnects, essential for LLM training.
3. Can I use CoreWeave through Terraform or CLI tools?
Yes, CoreWeave supports Terraform, APIs, and Kubernetes CLI (kubectl) for advanced infrastructure management.
4. How is CoreWeave different from AWS or GCP?
CoreWeave focuses solely on GPU workloads and performance-critical applications, offering lower pricing and better customisation.
5. How does CoreWeave pricing work?
CoreWeave offers transparent hourly pricing for each GPU model, with options for preemptible instances and committed-use discounts.
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