CoreWeave vs Nebius
A side-by-side comparison of CoreWeave and Nebius, two Inference tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
CoreWeave
InferenceThe AI hyperscaler — GPU cloud built for large-scale training and inference.
View CoreWeaveAt a glance
| Attribute | CoreWeave | Nebius |
|---|---|---|
| Category | Inference | Inference |
| Pricing | PAID | PAID |
| License | Proprietary | Proprietary |
| Deployment | Cloud | Cloud |
| Platforms | Web, API | Web, API |
| Model support | Model-agnostic | Model-agnostic |
| Vendor (differs) | CoreWeave | Nebius Group |
| Capabilities (differs) |
|
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The honest brief
CoreWeave
Operates at a scale smaller GPU clouds can't match — first to stand up new NVIDIA generations, with nine of the ten top AI model providers as customers.
- Frontier-scale GPU capacity
- First to new NVIDIA generations
- Managed Kubernetes + observability
- Contracted by top AI labs
- Enterprise-oriented; no free tier
- Less self-serve than smaller GPU clouds
- Heavy debt-financed expansion
Nebius
Covers the full AI stack from bare-metal GPU clusters up to managed per-token inference, where many GPU clouds stop at raw compute.
- Latest NVIDIA silicon, H100 through Blackwell
- Managed Slurm and Kubernetes built in
- Token Factory per-token inference layer
- Nasdaq-listed, with Microsoft and Meta deals
- AI-only cloud — few general-purpose services
- Younger ecosystem than the big general clouds
When to pick which
Both cover GPU compute, Model inference / serving, and Fine-tuning / training.
Pick Nebius if you need Multi-model access.
- Multi-model access (secondary capability)
Nebius leans on Model inference / serving as a headline capability; CoreWeave treats it as secondary.
- Model inference / serving (primary capability)