Provider Comparison

AWS vs Nebius

Q: Which provider offers better spot instance pricing?

Both AWS and Nebius offer spot/preemptible instances, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and distributed training with checkpointing. The actual savings depend on current demand and GPU availability, so we recommend comparing real-time spot prices for your specific GPU requirements on both platforms.

Q: What is the minimum billing increment for each provider?

AWS bills per-second, while Nebius bills per-second. Both providers use the same billing granularity, so this factor won't differentiate your decision.

Q: Which provider has better compliance certifications for enterprise use?

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Nebius holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Both providers have similar compliance postures. Check with each provider directly for the most current certification status and specific compliance documentation.

Q: Which provider offers better development tools like Jupyter notebooks?

Both AWS and Nebius offer built-in Jupyter notebook support, making it easy to start experimenting without additional setup. This is particularly valuable for data scientists and researchers who prefer interactive development environments. Additionally, both providers offer web-based terminal access for quick debugging.

Q: Which provider has better Kubernetes support for orchestration?

Both AWS and Nebius support Kubernetes for container orchestration, enabling you to deploy scalable ML pipelines, manage distributed training jobs, and integrate with MLOps tools like Kubeflow. This is essential for teams running production workloads at scale.

Q: What is each provider best suited for?

AWS is best suited for Large-scale enterprises requiring deep integration with other cloud services; Organizations needing globally redundant availability zones. Nebius excels at Enterprises needing EU/US compliance and managed K8s. Understanding these specializations helps you choose the provider that aligns with your primary use case, though both can handle a variety of GPU computing needs.

Q: Which provider offers reserved instances for long-term savings?

Both AWS and Nebius offer reserved instance pricing for committed usage, typically providing 20-40% discounts compared to on-demand rates. Reserved instances are ideal for predictable, steady-state workloads like always-on inference services. For variable workloads, on-demand or spot instances may offer better flexibility.

Q: Which provider offers better enterprise support?

Both AWS and Nebius offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Nebius offers SLA guarantees.

Q: Which provider has better API and automation support?

AWS provides a comprehensive API for programmatic control, while Nebius may require more manual management. If automation is a priority, AWS's API support will streamline your infrastructure-as-code workflows.

Q: Which provider has better container and Docker support?

AWS offers native container support for running Docker images, while Nebius may require additional configuration. Container support is valuable for reproducible ML pipelines and easy deployment of pre-built environments.

AWS and Nebius represent contrasting approaches in GPU cloud infrastructure for ML/AI workloads. AWS, the market leader, offers a vast ecosystem with seamless integration across services like SageMaker, EC2 P5 instances featuring NVIDIA H100 GPUs, and proprietary Trainium/Inferentia chips for cost-optimized training/inference. It's ideal for enterprises needing global redundancy, hybrid cloud setups, and managed ML pipelines, but faces criticism for high costs, complex pricing (including data egress fees), and occasional GPU supply constraints. Nebius, an AI-centric provider spun from Yandex, emphasizes simplicity and performance with managed Kubernetes clusters on high-density NVIDIA GPU bare-metal servers (A100/H100), primarily in EU (Finland) and US data centers. As a public company, it provides transparency and focuses on compliant workloads (SOC 2, HIPAA, GDPR). It's best for teams prioritizing EU data sovereignty, Kubernetes-native deployments, and competitive pricing without ecosystem lock-in. Key differentiators: AWS excels in scale and integrations but at premium pricing; Nebius offers startup agility, lower latency in Europe, and easier ops for K8s users. Overall, AWS suits complex, enterprise-scale operations; Nebius delivers value for focused AI teams seeking cost-efficiency and compliance without bloat. Both support per-second billing and spot instances, enabling flexible ML experimentation to production.

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Our Recommendation

Choose AWS for large enterprises (>50 engineers) with existing AWS investments, needing global AZ redundancy, SageMaker for end-to-end ML, or Trainium for massive training savings (up to 50% vs GPUs). It's optimal for budgets >$100K/month tolerating complexity, hybrid workloads, or strict SLAs. Opt for Nebius if your team (10-50 engineers) runs Kubernetes-heavy AI pipelines, requires EU/US compliance for sensitive data, or prioritizes cost savings (often 20-40% lower on GPUs). Ideal for startups/scaling AI firms with budgets <$50K/month, focusing on raw GPU perf without extras. For pure experimentation, either works; for production inference at scale, AWS edges due to ecosystem. Evaluate via PoCs considering data transfer needs and region latency.

Live Pricing

Compare real-time GPU offers from AWS and Nebius

28 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	4 vCPU 16GB RAM	Virginia	$0.53/GPU/hr
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	8 vCPU 32GB RAM	Virginia	$0.75/GPU/hr
AWS	4×NVIDIA Tesla T4 16GB VRAM	16GB	48 vCPU 192GB RAM	Virginia	$0.98/GPU/hr $3.91/hr total (4×)
AWS	NVIDIA RTX A6000 48GB VRAM	48GB	4 vCPU 16GB RAM	Virginia	$1.01/GPU/hr
AWS	NVIDIA Tesla T4 16GB VRAM	16GB	16 vCPU 64GB RAM	Virginia	$1.20/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

4 vCPU

16GB RAM

$0.53/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

8 vCPU

32GB RAM

$0.75/GPU/hr

AWS

Virginia

NVIDIA Tesla T44x

16GB VRAM

48 vCPU

192GB RAM

$0.98/GPU/hr

$3.91/hr total (4×)

AWS

Virginia

NVIDIA RTX A6000

48GB VRAM

4 vCPU

16GB RAM

$1.01/GPU/hr

AWS

Virginia

NVIDIA Tesla T4

16GB VRAM

16 vCPU

64GB RAM

$1.20/GPU/hr

View all 28 offers

AWS(Est. 2006)

The dominant force in global cloud computing with deep integration of GPUs into its ecosystem for machine learning and other services.

Best For

Large-scale enterprises requiring deep integration with other cloud servicesOrganizations needing globally redundant availability zones

Unique Features

Proprietary silicon like Trainium and Inferentia chips
Fully managed ML development environment with SageMaker

Limitations

High cost relative to specialized clouds
Complexity of pricing including egress fees

Nebius(Est. 2023)

An AI-centric infrastructure company providing managed services for EU/US compliant workloads.

Best For

Enterprises needing EU/US compliance and managed K8s

Unique Features

Public company with transparency
Startup-like focus on AI

Feature Comparison

Access Methods

Feature	AWS	Nebius
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	AWS	Nebius
Billing Increment	per-second	per-second
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	AWS	Nebius
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	Nebius
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Both providers use per-second billing for on-demand instances, minimizing waste for variable ML jobs, and offer spot instances for 50-90% discounts on preemptible capacity—crucial for non-urgent training. AWS adds Savings Plans (1-3 year commitments, up to 72% off), Reserved Instances, and complex add-ons like data transfer out ($0.09/GB beyond free tier) and EBS volumes. Nebius keeps it simpler: straightforward on-demand/spot without long-term locks or egress penalties within its regions, but lacks AWS's volume discounts for hyperscale users. Implications: AWS favors predictable long runs with commitments; Nebius suits bursty, short-term workloads avoiding pricing opacity. Track via AWS Cost Explorer vs Nebius dashboard for real-time forecasts.

Value Assessment

Nebius offers superior value for small experiments/fine-tuning (e.g., A100 spots at ~$1.5/hr vs AWS ~$2.5/hr) and batch inference due to lower base rates and no egress fees, ideal for <100 GPU-hour jobs. AWS shines for large LLM training (P5/H100 clusters with Trainium hybrids cut costs 40%) and production inference via scalable SageMaker endpoints with auto-scaling. For intermittent use, Nebius's simplicity yields 20-30% savings; AWS for sustained >1K GPU-hours/month via reservations. Budget-conscious teams save more on Nebius; enterprises leveraging credits/integrations favor AWS. Always benchmark total cost including storage/networking.

Use Case Comparison

LLM Training

AWS recommended

AWS

AWS excels with P5.48xlarge (8x H100) clusters scaling to thousands of GPUs via Trainium for cost-efficient pre-training. SageMaker handles distributed training (SMDDP), checkpointing, and fault tolerance. Global AZs ensure high availability, but supply waits and higher spot prices (~$30/hr H100) apply. Integrates with FSx Lustre for petabyte storage.

Nebius

Nebius supports dense H100/A100 K8s clusters with Slurm/Kubernetes orchestration for multi-node training. EU-focused low-latency networking aids sync-heavy jobs, but lacks proprietary chips and global scale. Competitive spot pricing (~$25/hr H100) suits mid-scale runs; managed storage integrates seamlessly.

Batch Inference

Either works

AWS

SageMaker Batch Transform leverages Inferentia for 40% faster/cheaper inference on large datasets. Spot fleets auto-scale, with EFS/S3 integration minimizing costs. Handles variable payloads well, but data egress adds ~10% overhead for external results.

Nebius

Kubernetes jobs on GPU pods excel for custom batch scripts, with built-in object storage and no egress fees. H100/A100 deliver high throughput; spot preemption managed via K8s. Simpler for teams avoiding SageMaker lock-in.

Real-time Inference

Nebius recommended

AWS

SageMaker Endpoints with auto-scaling, Inferentia/Trn1 for low-latency (<100ms), and global edge via CloudFront. Multi-model support and A/B testing built-in. Robust monitoring via CloudWatch, but startup latency ~minutes and costs higher (~$4/hr A10G).

Nebius

Managed K8s deployments with NVIDIA Triton enable sub-50ms latency on H100s. Autoscaling via HPA; EU proximity reduces user latency. Easier custom serving stacks, lower costs (~$3/hr), but less mature global CDN.

Fine-tuning & Experimentation

Nebius recommended

AWS

SageMaker Studio notebooks with spot GPUs (A10G/P4d) for rapid prototyping. JumpStart models accelerate starts; per-second billing fits short runs. Ecosystem (Glue, EMR) aids data prep, but navigation complexity slows solo users.

Nebius

K8s-native JupyterHub on spots for interactive tuning. Pre-built AI stacks (NVIDIA NGC) simplify; transparent pricing/no lock-in favors iteration. EU compliance for regulated experiments; quick spin-up for <1hr jobs.

Technical Comparison

Infrastructure

AWS relies on virtualized EC2 (Nitro) with some bare-metal options, offering EFA networking (400Gbps+), EBS/GP3 storage (125MB/s), and managed EKS for K8s. Global 30+ regions ensure redundancy. Nebius focuses on bare-metal GPU servers in dedicated clusters (Finland/US), Kubernetes-managed with 400Gbps RoCE, high-performance NVMe storage, and Slurm support. No virtualization overhead; simpler for K8s but limited regions (3-4). Both provide elastic IP and VPC-like isolation.

Performance

AWS P5 instances benchmark top for NVLink-multi-GPU training (2.3TB/s aggregate); Trainium matches H100 on BF16. Availability strong but queues during peaks. Nebius H100 clusters show comparable interconnect perf (800GbE), low-jitter for fine-tuning; faster EU ramps claim 20% better single-node vs virtualized rivals. Scaling to 100s GPUs solid on both via NCCL; Nebius edges density/latency in Europe, AWS in cross-region. Benchmarks vary—test via MLPerf submissions.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both AWS and Nebius offer spot/preemptible instances, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and distributed training with checkpointing. The actual savings depend on current demand and GPU availability, so we recommend comparing real-time spot prices for your specific GPU requirements on both platforms.

What is the minimum billing increment for each provider?▾

AWS bills per-second, while Nebius bills per-second. Both providers use the same billing granularity, so this factor won't differentiate your decision.

Which provider has better compliance certifications for enterprise use?▾

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Nebius holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Both providers have similar compliance postures. Check with each provider directly for the most current certification status and specific compliance documentation.

Which provider offers better development tools like Jupyter notebooks?▾

Both AWS and Nebius offer built-in Jupyter notebook support, making it easy to start experimenting without additional setup. This is particularly valuable for data scientists and researchers who prefer interactive development environments. Additionally, both providers offer web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Both AWS and Nebius support Kubernetes for container orchestration, enabling you to deploy scalable ML pipelines, manage distributed training jobs, and integrate with MLOps tools like Kubeflow. This is essential for teams running production workloads at scale.

What is each provider best suited for?▾

AWS is best suited for Large-scale enterprises requiring deep integration with other cloud services; Organizations needing globally redundant availability zones. Nebius excels at Enterprises needing EU/US compliance and managed K8s. Understanding these specializations helps you choose the provider that aligns with your primary use case, though both can handle a variety of GPU computing needs.

Which provider offers reserved instances for long-term savings?▾

Both AWS and Nebius offer reserved instance pricing for committed usage, typically providing 20-40% discounts compared to on-demand rates. Reserved instances are ideal for predictable, steady-state workloads like always-on inference services. For variable workloads, on-demand or spot instances may offer better flexibility.

Which provider offers better enterprise support?▾

Both AWS and Nebius offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Nebius offers SLA guarantees.

Which provider has better API and automation support?▾

AWS provides a comprehensive API for programmatic control, while Nebius may require more manual management. If automation is a priority, AWS's API support will streamline your infrastructure-as-code workflows.

Which provider has better container and Docker support?▾

AWS offers native container support for running Docker images, while Nebius may require additional configuration. Container support is valuable for reproducible ML pipelines and easy deployment of pre-built environments.

What unique features differentiate these providers?▾

AWS's standout features include: Proprietary silicon like Trainium and Inferentia chips; Fully managed ML development environment with SageMaker. Nebius's standout features include: Public company with transparency; Startup-like focus on AI. These differentiators may be decisive factors depending on your specific technical requirements and workflow preferences.

How do I get started with each provider?▾

To get started with AWS, visit their website at https://aws.amazon.com?utm_source=gpuperhour&utm_medium=referral to create an account and explore available GPU options. For Nebius, visit https://nebius.com?utm_source=gpuperhour&utm_medium=referral to sign up. Both providers typically offer some form of free credits or trial period for new users. We recommend starting with a small experiment to evaluate the platform's ease of use, instance launch times, and overall fit for your workflow before committing to larger workloads.