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Nebius vs CoreWeave: GPU Cloud Pricing & Performance Compared

Deploybase · December 19, 2025 · GPU Cloud

Contents

Nebius and CoreWeave Overview

Nebius (formerly Yandex Cloud) provides GPU infrastructure across multiple regions with emphasis on developer experience and API consistency. The platform serves both inference and training workloads with flexible pricing models. Nebius targets mid-market teams requiring reliable, well-documented services.

CoreWeave operates purpose-built GPU cloud infrastructure optimized for dense compute. The company bundles GPUs in multi-GPU configurations emphasizing throughput and cost efficiency for large-scale workloads. CoreWeave targets enterprises and research teams running production AI systems.

Both platforms compete on cost and performance, but architectural approaches differ significantly. Nebius provides traditional cloud infrastructure with pay-as-developers-go flexibility. CoreWeave bundles GPUs for workload-specific optimization.

As of March 2026, CoreWeave expanded European operations while Nebius consolidated regional presence. Both platforms target the LLM and AI training market intensively.

Pricing Comparison

Entry-level GPU (A10 equivalent):

  • Nebius A10: $0.70/hour
  • CoreWeave: No individual GPU pricing
  • Advantage: Nebius for experimentation

Mid-range single GPU (A100):

  • Nebius A100 80GB: $2.10-$2.50/hour
  • CoreWeave: Sold as 8xA100 bundles only at $21.60/hour ($2.70 per GPU)
  • Per-GPU cost: CoreWeave $2.70 vs Nebius $2.10-$2.50
  • Advantage: Nebius slightly cheaper for single GPU deployments

High-end single GPU (H100):

  • Nebius H100 80GB: $2.95/hour
  • CoreWeave: Sold as 8xH100 bundles at $49.24/hour ($6.16 per GPU)
  • Per-GPU cost: CoreWeave $6.16 vs Nebius $2.95
  • Advantage: Nebius 2.1x cheaper for single GPU

Multi-GPU bundles (8xH100):

  • Nebius (8x H100): 8 * $2.95 = $23.60/hour
  • CoreWeave 8xH100: $49.24/hour
  • Advantage: Nebius 2.1x cheaper even at scale

Very large clusters (8xH200):

  • Nebius (8x H200): 8 * $3.00 = $24/hour (estimated)
  • CoreWeave 8xH200: $50.44/hour
  • Advantage: Nebius 2x cheaper

CoreWeave's bundled pricing appears consistently higher. However, bundle pricing includes pre-configured networking, storage, and orchestration that Nebius charges separately.

Performance Metrics

Single-GPU training throughput (ResNet-50 on A100):

  • Nebius A100: 780-820 images/second
  • CoreWeave A100 (from 8xA100 bundle): 780-820 images/second
  • Variance: Within measurement error
  • Verdict: Equivalent performance

Multi-GPU distributed training (8xA100, Llama 70B):

  • Nebius (8x separate A100s): 4,200-4,600 tokens/second
  • CoreWeave 8xA100 bundle: 4,400-4,800 tokens/second
  • Performance advantage: CoreWeave +5-8%
  • Reason: Co-located GPUs with optimized networking

H100 inference throughput (Llama 7B):

  • Nebius H100: 52-58 tokens/second
  • CoreWeave H100 (from bundle): 52-58 tokens/second
  • Variance: Within typical measurement error
  • Verdict: Equivalent for single-GPU workloads

Network latency between GPUs:

  • Nebius inter-GPU latency: 20-40 microseconds
  • CoreWeave inter-GPU latency: 2-5 microseconds
  • Impact: CoreWeave 5-8x lower latency for collective operations
  • Significance: Meaningful for 64+ GPU training, marginal for 8 GPUs

CoreWeave's performance advantage emerges in distributed setups with 16+ GPUs. Nebius's separate GPU instances introduce network overhead that compounds across large clusters. For single or few-GPU workloads, performance remains equivalent.

Feature Comparison

Networking and inter-GPU communication: CoreWeave provides high-bandwidth interconnects (400Gb/s between GPUs) enabling efficient all-reduce operations. Nebius relies on standard cloud networking with lower throughput (up to 100Gb/s in premium configurations). This matters substantially for 8+ GPU training but not for small-scale deployments.

Persistent storage: Nebius offers integrated block storage with instances. CoreWeave requires external storage (AWS S3 compatible or custom configurations). Nebius simplifies state management; CoreWeave requires more careful orchestration.

Container orchestration: CoreWeave supports Kubernetes natively with GPU scheduling plugins. Nebius requires manual cluster management or integration with external orchestration tools. CoreWeave's native support reduces deployment complexity.

Spot and preemptible capacity: Nebius provides spot instances at 30-50% discounts with fault-tolerance guarantees. CoreWeave's equivalent offerings launched in 2025 but lack mature safeguards. Nebius offers better risk guarantees.

Regional availability: Nebius operates data centers across 5+ regions globally. CoreWeave expanded to 6 regions by 2026. Both provide reasonable geographic distribution, though Nebius maintains slight coverage advantage.

Support and SLAs: Nebius offers tiered support with 99.7% uptime SLA for paid tiers. CoreWeave provides dedicated support to production customers with custom SLAs. Both exceed minimum standards, though Nebius provides more consistent support tiers.

Use Case Recommendations

Choose Nebius if:

  • Running single or dual GPU experiments
  • Cost minimization is primary objective
  • Developing in Python with standard frameworks
  • Requiring flexible pay-as-developers-go pricing
  • Needing distributed storage simplicity

Choose CoreWeave if:

  • Deploying 8+ GPU production training
  • Network latency between GPUs matters
  • Running containerized Kubernetes workloads
  • Requiring dedicated GPU-to-GPU communication
  • Focusing on throughput maximization

For 2-4 GPU deployments: Nebius wins on cost and simplicity. Individual GPU rental ($1.30-2.50/hour) beats bundled pricing.

For 8-16 GPU production training: CoreWeave's networking advantage justifies higher cost. Low inter-GPU latency compounds across training runs.

For LLM inference: Both platforms serve equivalently. Choice depends on secondary factors like location and UI preferences.

FAQ

Which platform offers better value for my startup?

If your team has limited ML infrastructure experience, I'd choose Nebius. The simpler pricing model and integrated storage reduce operational overhead. If training speed on large models directly impacts product roadmap, CoreWeave's distributed capabilities justify the cost premium.

Can I move between Nebius and CoreWeave easily?

Both platforms use standard Docker containers and NVIDIA GPUs, making migration straightforward. Model weights transfer between systems without modification. I'd recommend testing on your target platform first before committing to volume discounts or contracts.

Does Nebius or CoreWeave offer better spot pricing?

Nebius provides more mature spot instances at 30-50% discounts. CoreWeave's spot offerings are newer but reaching feature parity. For fault-tolerant batch jobs, I'd lean toward Nebius. For stateful training where interruption is costly, I'd avoid spot instances entirely regardless of provider.

What about GPU availability?

Both platforms occasionally experience inventory shortages during AI boom periods. CoreWeave sometimes runs out of H100 capacity in popular regions. Nebius maintains larger inventory pools. For time-critical projects, I'd verify availability in your target region before planning.

Should I commit to annual contracts?

Nebius contracts provide 15-25% discounts. CoreWeave contracts reach 30-40% but require larger commitments. I'd only commit for 1-year periods if workload characteristics are stable and predictable.

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