Provider Comparison

AWS vs Scaleway

Q: Which provider offers spot instances for cost savings?

AWS offers spot/preemptible instances, which can significantly reduce costs (typically 50-80% off on-demand prices) for interruptible workloads like batch processing and training with checkpoints. Scaleway does not currently offer spot instances, so all usage is billed at on-demand rates. If cost optimization through spot instances is important for your workflow, AWS would be the better choice.

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

AWS bills per-second, while Scaleway bills per-hour. Per-second billing from AWS offers better cost efficiency for short experiments and iterative development, as you only pay for exactly what you use.

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

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Scaleway holds SOC 2, GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

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

Both AWS and Scaleway 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 Scaleway 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. Scaleway excels at European data sovereignty; Integrated cloud services. 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 Scaleway 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?

AWS offers dedicated enterprise support options, while Scaleway may have more limited support tiers. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Scaleway has no published SLA.

Q: Which provider has better API and automation support?

AWS provides a comprehensive API for programmatic control, while Scaleway 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 Scaleway may require additional configuration. Container support is valuable for reproducible ML pipelines and easy deployment of pre-built environments.

AWS and Scaleway represent contrasting approaches in GPU cloud provisioning for ML/AI workloads. AWS, the market leader, offers unparalleled global scale with instances like P5 (H100 GPUs) deeply integrated into SageMaker for end-to-end ML pipelines, Trainium/Inferentia for cost-optimized training/inference, and features like spot instances for savings up to 90%. It's ideal for enterprises needing multi-AZ redundancy, compliance (including HIPAA), and ecosystem tools like EKS for Kubernetes orchestration. However, pricing complexity, high on-demand rates, and egress fees can inflate costs. Scaleway, a European provider, prioritizes data sovereignty with GDPR/HDS compliance and eco-friendly operations (100% renewable energy). Its Nabu AI Supercomputer provides dense GPU clusters (e.g., 4x H100 per node), alongside Instances like ROME (A100s). Billing is straightforward per-hour, appealing for predictable EU workloads, but lacks AWS's global footprint and managed ML services. Scaleway suits sovereignty-focused teams with integrated Object/Block storage and Kubernetes via Kapsule. AWS excels in large-scale, production-grade deployments requiring seamless scaling and integrations; Scaleway offers competitive pricing and lower latency for European users, with strong environmental credentials. Choice hinges on geography, scale, budget, and integration needs—AWS for global enterprises, Scaleway for EU-centric, cost-sensitive operations.

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Our Recommendation

Choose AWS for large teams (50+ engineers) running enterprise-scale LLM training or production inference needing global redundancy, SageMaker for managed workflows, or Trainium for cost savings on massive jobs. It's suited to budgets over $10K/month where spot instances offset premiums, and HIPAA compliance is required. Opt for Scaleway with small-to-medium teams (under 20) prioritizing EU data residency, per-hour billing simplicity, and eco-impact—ideal for fine-tuning or batch jobs under $5K/month. Scaleway fits latency-sensitive European inference without AWS's egress costs. For hybrid needs, start with Scaleway for prototyping, migrate to AWS for scale; avoid Scaleway if global HA or advanced ML ops are critical.

Live Pricing

Compare real-time GPU offers from AWS and Scaleway

53 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
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
Scaleway	4×NVIDIA L4 24GB VRAM	24GB	32 vCPU 192GB RAM	Paris	$0.92/GPU/hr $3.67/hr total (4×)	Available
Scaleway	4×NVIDIA L4 24GB VRAM	24GB	32 vCPU 192GB RAM	Paris	$0.92/GPU/hr $3.67/hr total (4×)	Sold Out
Scaleway	2×NVIDIA L4 24GB VRAM	24GB	16 vCPU 96GB RAM	Paris	$0.92/GPU/hr $1.83/hr total (2×)	Available

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

Scaleway

Paris

Available

NVIDIA L44x

24GB VRAM

32 vCPU

192GB RAM

10000 Mbps ↑

10000 Mbps ↓

$0.92/GPU/hr

$3.67/hr total (4×)

Scaleway

Paris

Sold Out

NVIDIA L44x

24GB VRAM

32 vCPU

192GB RAM

10000 Mbps ↑

10000 Mbps ↓

$0.92/GPU/hr

$3.67/hr total (4×)

Scaleway

Paris

Available

NVIDIA L42x

24GB VRAM

16 vCPU

96GB RAM

5000 Mbps ↑

5000 Mbps ↓

$0.92/GPU/hr

$1.83/hr total (2×)

View all 53 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

Scaleway(Est. 1999)

A major European cloud provider emphasizing data sovereignty and integrated services.

Best For

European data sovereigntyIntegrated cloud services

Unique Features

Nabu AI Supercomputer
Strong environmental credentials

Feature Comparison

Access Methods

Feature	AWS	Scaleway
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	AWS	Scaleway
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	Scaleway
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

AWS employs per-second billing for EC2 GPU instances (e.g., p5.48xlarge H100 at ~$32.77/hour on-demand), with spot instances offering 50-90% discounts for interruptible workloads and Savings Plans/Reserved Instances for 30-70% off committed use. Egress fees ($0.09/GB inter-region) add complexity. Scaleway uses per-hour billing (e.g., ROME-4A100 at €3.60/hour, Nabu H100 clusters from €5.99/hour/node), no spot but volume discounts for reservations; simpler but less flexible for short bursts. AWS favors variable, bursty patterns (e.g., experiments); Scaleway suits steady, predictable runs, potentially 20-40% cheaper for EU on-demand without egress.

Value Assessment

For small experiments/fine-tuning (<1 hour), Scaleway's per-hour minimum and lower base rates (€1-6/hour for A100 equiv.) yield better value, avoiding AWS's ramp-up costs. Large training runs (days-long) favor AWS spot/Trainium (up to 50% cheaper than GPU on-demand). Production batch inference benefits AWS Savings Plans for steady loads; real-time inference suits Scaleway's dense Nabu clusters for EU latency at fixed hourly rates. Overall, Scaleway wins on cost-per-flop for mid-scale EU jobs (20-50% savings); AWS for optimized, interruptible scale with ecosystem efficiencies.

Use Case Comparison

LLM Training

AWS recommended

AWS

AWS shines with P4d/P5 instances (8x H100), Trainium clusters for 40-50% cost savings on FP16 training, SageMaker for distributed runs via SMDDP/TorchElastic, and spot for fault-tolerant jobs. Global AZs ensure 99.99% SLA; integrates with FSx Lustre for 100PB storage. Ideal for 100B+ param models but pricier on-demand.

Scaleway

Scaleway's Nabu Supercomputer (H100 pods up to 256 GPUs) supports large-scale training with NVLink/RoCE networking; per-hour billing suits long jobs. EU sovereignty aids regulated data, but lacks managed orchestration like SageMaker; scaling limited to Paris/DC5 regions.

Batch Inference

Either works

AWS

AWS Inferentia (Inf2 instances) delivers 40-60% better price/perf than GPUs for batch; SageMaker Batch Transform handles autoscaling. Spot instances optimize costs for non-urgent queues; EBS/S3 integration streamlines data pipelines.

Scaleway

Scaleway GPUs (A100/H100) via Nabu excel for dense batch with fast local NVMe; per-hour fixed costs aid budgeting. Kubernetes Kapsule eases job scheduling, but no specialized inference silicon; strong for EU data pipelines.

Real-time Inference

AWS recommended

AWS

AWS EC2 G5/Inf2 with low-latency ENIs, Lambda/SageMaker Endpoints for serverless scaling, and Global Accelerator for <100ms worldwide. Trainium2 upcoming for dynamic batching; robust autoscaling via ASGs.

Scaleway

Scaleway's low-latency EU networking (1-5ms intra-region) and Nabu H100s suit real-time; Elastic Metal bare-metal minimizes jitter. Kapsule Kubernetes aids deployments, but regional focus limits global reach.

Fine-tuning & Experimentation

Scaleway recommended

AWS

AWS SageMaker Studio notebooks, spot A10G instances (~$1/hour), and JumpStart models speed iteration. Per-second billing perfect for short runs; but setup overhead for non-experts.

Scaleway

Scaleway's affordable A4000/A100 instances (€0.50-3/hour) and per-hour billing minimize waste for trials. Nabu for quick multi-GPU tests; simple console suits small teams, with EU snapshot storage.

Technical Comparison

Infrastructure

AWS relies on virtualized EC2 Nitro with EBS/EFS storage, Elastic Fabric Adapter (400Gbps) for multi-GPU, and EKS for managed Kubernetes; supports Trainium/Inferentia ASICs. Global 30+ regions. Scaleway mixes virtual Instances and bare-metal Elastic Metal, with Block/Object storage (up to 100TB NVMe); Kapsule Kubernetes native. Nabu uses InfiniBand for clusters; focused on 3 EU regions (Paris, Amsterdam) emphasizing sovereignty/low-latency intra-EU.

Performance

AWS P5 achieves 2x H100 throughput via Trainium2 (upcoming), strong multi-node scaling (10k+ GPUs via Slurm); benchmarks show 95% scaling efficiency. Scaleway Nabu H100 pods hit 90% efficiency on RoCE-400Gbps, competitive single-node perf but fewer public multi-GPU benchmarks. AWS edges availability/diversity (A100/H100/V100); Scaleway reliable for EU, with eco-optimized cooling aiding sustained loads. Both support CUDA 12.x.

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

AWS offers spot/preemptible instances, which can significantly reduce costs (typically 50-80% off on-demand prices) for interruptible workloads like batch processing and training with checkpoints. Scaleway does not currently offer spot instances, so all usage is billed at on-demand rates. If cost optimization through spot instances is important for your workflow, AWS would be the better choice.

What is the minimum billing increment for each provider?▾

AWS bills per-second, while Scaleway bills per-hour. Per-second billing from AWS offers better cost efficiency for short experiments and iterative development, as you only pay for exactly what you use.

Which provider has better compliance certifications for enterprise use?▾

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Scaleway holds SOC 2, GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

Both AWS and Scaleway 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 Scaleway 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. Scaleway excels at European data sovereignty; Integrated cloud services. 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 Scaleway 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?▾

AWS offers dedicated enterprise support options, while Scaleway may have more limited support tiers. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Scaleway has no published SLA.

Which provider has better API and automation support?▾

AWS provides a comprehensive API for programmatic control, while Scaleway 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 Scaleway 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. Scaleway's standout features include: Nabu AI Supercomputer; Strong environmental credentials. 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 Scaleway, visit https://www.scaleway.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.