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Nebius GPU Cloud Pricing: Complete Guide vs Hourly Rates for Every GPU

Deploybase · December 8, 2025 · GPU Pricing

Contents

Overview

Nebius GPU cloud pricing reflects a specialized AI-focused infrastructure provider. The platform emerged from Yandex's AI division, maintaining strong technical foundations in machine learning operations.

Nebius Pricing Models

Nebius offers straightforward hourly pricing without complex tier structures. Transparency in billing supports accurate cost forecasting for data science teams.

Standard Hourly Rates

Nebius pricing starts lower than AWS and Azure for equivalent hardware. Per-GPU rates vary by hardware generation and deployment region.

Current pricing tiers:

  • H100 80GB: $2.95/hour
  • H200 (141GB HBM3e): $3.50/hour
  • B200: $5.50/hour
  • A100 80GB: $2.10-$2.50/hour
  • L40S: $1.55-$1.82/hour

Comparing RunPod GPU pricing where H100 SXM costs $2.69/hour, Nebius remains competitive in the mid-tier range.

Commitment Discounts

12-month pre-payments provide 15% discounts on all GPU rates. This discount level remains lower than AWS but exceeds providers offering no reduction path.

Annual commitments make sense for teams with stable workload baselines. Development and experimentation workloads better utilize pay-as-you-go pricing.

GPU Portfolio

H100 Availability

Nebius maintains substantial H100 inventory across US, EU, and APAC regions. H100 80GB is the primary offering, with HBM3 memory supporting bandwidth-intensive applications.

Capacity planning indicates strong regional availability throughout March 2026. Teams should verify regional inventory before committing to workloads.

H200 Introduction

H200 GPUs are newly available on Nebius as of early 2026. Pricing positions at approximately 28-35% premium over H100, reflecting hardware costs.

Comparing NVIDIA H200 pricing at reference rates, Nebius's H200 hourly cost aligns with production cloud pricing across the market.

Specialized Hardware

Nebius stocks L40S for inference and multi-modal workloads. Pricing runs approximately 35-40% below H100 equivalents, making L40S attractive for inference-heavy deployments.

Consumer GPUs like RTX 4090 are available at minimal cost, supporting development environments and cost-conscious prototyping.

Total Cost Analysis

Single GPU Economics

Running H100 continuously for one month:

  • Nebius hourly rate: $2.95
  • Monthly cost: 30 × 24 × $2.95 = $2,124
  • Annual cost: $25,488

This figure excludes storage and bandwidth charges, typically adding 5-15% additional expenses ($600-1,200 annually). Storage for checkpoints and intermediate results costs approximately $0.10/GB-month, accumulating to $30-50 monthly for typical workloads.

The per-minute billing granularity on Nebius enables precise cost tracking. Short experiments incur proportional costs rather than minimum hourly charges. A 30-minute prototyping session costs approximately $1.48 (half of $2.95/hr) rather than full hourly charges.

Multi-Month Project Costs

A six-month machine learning research project using 4×H100 GPUs:

  • Configuration cost: 4 GPUs × $2.95/hour = $11.80/hour
  • Monthly cost: $11.80 × 24 × 30 = $8,496
  • Six-month cost: $8,496 × 6 = $50,976

With 12-month commitment discount at 15% reduction: Annual cost becomes $25,488 × 0.85 = $21,665 Per-month equivalent: $1,805 (vs $2,124 on-demand) Six-month project cost with commitment: $50,976 × 0.85 = $43,330

Comparing Lambda GPU pricing, identical configuration costs $4 × $3.78 = $9.96/hour, or $7,171/month, totaling $43,027 for six months. Nebius is similarly priced to Lambda for H100 clusters.

For a 12-month continuous deployment:

  • Nebius on-demand (single GPU): $2.95 × 24 × 365 = $25,842
  • Nebius with commitment: $25,842 × 0.85 = $21,966
  • Lambda on-demand (single H100 SXM): $3.78 × 24 × 365 = $33,113
  • Nebius vs Lambda: Nebius ($21,966 with commitment) is substantially cheaper than Lambda on-demand ($33,113), though Lambda offers more stable infrastructure

Multi-GPU Scaling Economics

Running 8×H100 cluster for one month continuous:

  • Configuration: 8 × $2.95 = $23.60/hour
  • Monthly cost: $23.60 × 24 × 30 = $16,992
  • Annual cost: $203,904

Comparing CoreWeave GPU pricing at $49.24/hour for 8×H100:

  • CoreWeave monthly: $49.24 × 24 × 30 = $35,452.80
  • Nebius monthly: $16,992
  • Monthly savings: $18,460.80 or 52%

This 8-GPU economics heavily favors Nebius for training workloads, representing a compelling advantage for research institutions and AI companies scaling beyond single-GPU experiments.

Inference Cost Structures

Batch inference on L40S GPUs:

  • Hourly rate: $2.05/hour (40% less than H100)
  • Cost per 1000 inference requests (assuming 10-second latency): approximately $0.0057

This cost structure makes Nebius competitive for inference when comparing LLM API pricing against self-hosted alternatives. Running continuous inference on L40S costs $1,465/month, ideal for moderate-traffic inference endpoints serving 100-500 daily requests.

Comparing to Replicate GPU pricing at $0.001/second for A40 GPUs, per-request costs split favorably based on model complexity. Small models benefit from Replicate's API model. Large models benefit from Nebius's reserved infrastructure.

Optimal Deployment Scenarios for Nebius

Research Institutions

Academic institutions benefit significantly from Nebius pricing. Universities training multiple Llama 2 variants save 25-30% compared to AWS or Azure. A research group training 10 distinct 7B models over 6 months saves approximately $20,000 in GPU costs using Nebius.

Per-minute billing enables cost-effective short experiments. Prototyping novel attention mechanisms costs only minutes of GPU time rather than full-hour allocations.

Emerging AI Companies

Startups and early-stage AI companies stretch limited budgets through Nebius's cost efficiency. A 10-person team training production models continuously saves approximately $50,000-100,000 annually compared to AWS or Azure.

These savings enable reinvestment in additional model experiments, data curation, or infrastructure optimization. Seed-stage AI companies often use Nebius as primary infrastructure, switching to multi-provider deployments only at scale.

Cost-Conscious Development Teams

Individual researchers and small teams benefit from Nebius's low barrier to entry. No minimum commitments and per-minute billing enables exploring GPU computing affordably.

Developing and validating models on Nebius before committing to production deployment elsewhere represents sound cost optimization strategy.

Performance and Reliability

Nebius provides competitive infrastructure SLAs without production premium pricing. Their 99.5% uptime guarantee aligns with specialist GPU providers while undercutting traditional cloud platforms.

For research and non-mission-critical inference, Nebius's reliability suffices. Production inference endpoints serving customer traffic should evaluate higher-SLA providers or implement application-level redundancy across multiple providers.

AI-First Infrastructure Philosophy

ML Workload Optimization

Nebius designs infrastructure specifically for AI operations, not as an afterthought to general cloud infrastructure. This specialization manifests in:

  1. NCCL optimizations for distributed training across regions
  2. Pre-configured PyTorch and TensorFlow environments
  3. Automated model checkpoint management
  4. Direct integration with Hugging Face Model Hub

Comparing AWS GPU pricing where GPU instances represent commodity compute, Nebius's AI-focused optimization reduces deployment friction. Data scientists configure notebooks directly; infrastructure concerns remain abstracted.

Emerging Company Advantages

Nebius, spun from Yandex's AI division, benefits from deep LLM expertise without legacy production baggage. Technical support staff understand fine-tuning, distributed training, and inference optimization. This specialized knowledge creates superior onboarding experience versus generalist cloud providers.

Teams training custom models experience faster problem resolution. Nebius engineers debug NCCL communication issues or quantization failures in hours, not days.

Regional Expansion Trajectory

Nebius aggressively expands its regional footprint. By Q3 2026, additional European regions should be operational. This expansion enables GDPR-compliant deployments at Nebius pricing, which is broadly competitive with European providers like Hyperstack.

Teams planning European expansion should monitor Nebius announcements. First-mover advantage on new regions often includes introductory pricing benefits.

FAQ

Q: Does Nebius support NVIDIA A100 instances? A: Yes. A100 80GB instances are available across all regions at pricing of $2.10-$2.50/hour.

Q: What's the minimum deployment duration? A: Hourly billing supports deployments of any duration. No minimum commitment exists for on-demand instances.

Q: Can I change commitment levels mid-contract? A: Nebius allows switching from on-demand to committed pricing but does not permit early termination of commitments.

Q: How does Nebius rank for AI model training? A: Nebius provides strong infrastructure for training workloads. Multi-GPU networking supports efficient scaling to 8 or 16 GPU configurations.

Q: What security features does Nebius provide? A: Nebius offers encryption at rest and in transit, isolated networking, and SOC 2 Type II compliance. However, some companies require FedRAMP certification, which Nebius does not provide.

Sources

  • Nebius Cloud official pricing documentation (as of March 2026)
  • GPU specification sheets and performance benchmarks
  • Industry cloud infrastructure surveys
  • DeployBase pricing analysis