Provider Comparison

LeaderGPU vs Scaleway

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

LeaderGPU bills per-minute, while Scaleway bills per-hour. Consider your typical workload duration when evaluating which billing model offers better value for your use case.

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

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

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

Scaleway offers built-in Jupyter notebook support for interactive development, while LeaderGPU requires you to set up your own notebook environment. If quick iteration and experimentation are priorities, Scaleway's integrated notebooks provide a smoother experience. Additionally, Scaleway offers web-based terminal access for quick debugging.

Q: Which provider has better Kubernetes support for orchestration?

Scaleway offers native Kubernetes support for container orchestration, while LeaderGPU does not. If you're building production ML pipelines with Kubernetes-based tools like Kubeflow, Argo, or KServe, Scaleway will integrate more seamlessly with your workflow.

Q: What is each provider best suited for?

LeaderGPU is best suited for Hash cracking and rendering tasks. 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 LeaderGPU 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?

LeaderGPU offers dedicated enterprise support options, while Scaleway may have more limited support tiers.

Q: Which provider has better API and automation support?

Neither provider prominently advertises API access for automation. Check their documentation for programmatic instance management options.

Q: Which provider has better container and Docker support?

LeaderGPU 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.

Q: What unique features differentiate these providers?

LeaderGPU's standout features include: Flexible weekly/monthly flat-rate billing; Diverse consumer GPU cards. 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.

LeaderGPU and Scaleway represent contrasting approaches in the GPU cloud market for ML/AI workloads. LeaderGPU positions itself as a niche provider of bare-metal servers, emphasizing high-bandwidth connectivity and a diverse range of consumer-grade GPUs like RTX series cards. This makes it ideal for cost-sensitive, high-throughput tasks such as rendering or hash cracking, though adaptable for ML experimentation. Its per-minute billing with flexible weekly/monthly flat rates appeals to users seeking predictable costs for irregular usage. GDPR compliance ensures basic data protection, targeting budget-conscious teams prioritizing raw performance over ecosystem integration. In contrast, Scaleway is a full-stack European cloud provider focused on data sovereignty, offering virtualized GPU instances integrated with broader services like object storage, Kubernetes, and the Nabu AI Supercomputer—a large-scale cluster for demanding AI training. With per-hour billing, SOC 2, GDPR, and ISO 27001 compliance, it suits enterprises needing regulatory adherence, environmental sustainability (strong green credentials), and seamless scaling within a sovereign ecosystem. Scaleway's value lies in its holistic platform for production ML pipelines. Key differentiators include LeaderGPU's bare-metal efficiency and GPU variety versus Scaleway's integrated services and supercomputing scale. LeaderGPU offers better value for short bursts or specialized hardware needs, while Scaleway excels in compliant, long-term deployments. ML engineers should weigh bare-metal performance gains against Scaleway's operational maturity for optimal selection.

Visit LeaderGPU Visit Scaleway

Our Recommendation

Choose LeaderGPU for small-to-medium teams (1-10 engineers) running bursty ML experiments, fine-tuning, or rendering-adjacent tasks on diverse consumer GPUs, especially with budgets under $5K/month and tolerance for self-managed setups. Its per-minute billing minimizes costs for <1-hour sessions, ideal for prototyping without long-term commitments. Opt for Scaleway when prioritizing EU data sovereignty, team sizes >10, or production workloads requiring Kubernetes orchestration, persistent storage, and compliance (SOC 2/ISO). It's suited for budgets $10K+/month with steady usage, leveraging Nabu for large-scale training. Technical needs like multi-region low-latency inference favor Scaleway's ecosystem; bare-metal enthusiasts with high-bandwidth demands pick LeaderGPU. Hybrid evaluation via short trials recommended.

Live Pricing

Compare real-time GPU offers from LeaderGPU and Scaleway

82 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
QuantaCloud Partner	A100 · H100 / H200 · B200 / B300 32–1024+ GPUs · InfiniBand	∞	Custom configs	Multiple DCs	Reserved / cluster Get a quote in 24h	Available
LeaderGPU	8×NVIDIA GeForce RTX 3090 24GB VRAM	24GB	64 vCPU 384GB RAM 2000GB Storage	Netherlands	$0.29/GPU/hr $2.29/hr total (8×)	Available
LeaderGPU	4×NVIDIA GeForce GTX 1080 8GB VRAM	8GB	0 vCPU 64GB RAM 480GB Storage	Netherlands	$0.30/GPU/hr $1.20/hr total (4×)	Available
LeaderGPU	8×NVIDIA A40 48GB VRAM	48GB	48 vCPU 384GB RAM 2000GB Storage	Netherlands	$0.52/GPU/hr $4.13/hr total (8×)	Available
LeaderGPU	8×NVIDIA GeForce RTX 3090 24GB VRAM	24GB	48 vCPU 384GB RAM 2000GB Storage	Netherlands	$0.60/GPU/hr $4.80/hr total (8×)	Available
LeaderGPU	10×NVIDIA A10 24GB VRAM	24GB	64 vCPU 384GB RAM 2000GB Storage	Netherlands	$0.60/GPU/hr $6.00/hr total (10×)	Available

QuantaCloud

Partner

Available

A100 · H100 / H200 · B200 / B300

32–1024+ GPUs · InfiniBand

Reserved / cluster

Get a quote in 24h

LeaderGPU

Netherlands

Available

NVIDIA GeForce RTX 30908x

24GB VRAM

64 vCPU

384GB RAM

2000GB Storage

$0.29/GPU/hr

$2.29/hr total (8×)

LeaderGPU

Netherlands

Available

NVIDIA GeForce GTX 10804x

8GB VRAM

0 vCPU

64GB RAM

480GB Storage

$0.30/GPU/hr

$1.20/hr total (4×)

LeaderGPU

Netherlands

Available

NVIDIA A408x

48GB VRAM

48 vCPU

384GB RAM

2000GB Storage

$0.52/GPU/hr

$4.13/hr total (8×)

LeaderGPU

Netherlands

Available

NVIDIA GeForce RTX 30908x

24GB VRAM

48 vCPU

384GB RAM

2000GB Storage

$0.60/GPU/hr

$4.80/hr total (8×)

LeaderGPU

Netherlands

Available

NVIDIA A1010x

24GB VRAM

64 vCPU

384GB RAM

2000GB Storage

$0.60/GPU/hr

$6.00/hr total (10×)

View all 82 offers

QuantaCloud

Comparing providers? We broker across all of them.

Stop tab-switching between pricing pages. Tell us what you need — 16+ GPUs, reserved or cluster capacity — and we return one quote at partner rates within 24 hours.

No waitlist24hr quote turnaroundInfiniBand fabric

LeaderGPU(Est. 2017)

A provider specializing in bare-metal servers with high bandwidth and diverse GPU availability.

Best For

Hash cracking and rendering tasks

Unique Features

Flexible weekly/monthly flat-rate billing
Diverse consumer GPU cards

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	LeaderGPU	Scaleway
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	LeaderGPU	Scaleway
Billing Increment	per-minute	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	LeaderGPU	Scaleway
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	LeaderGPU	Scaleway
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

LeaderGPU employs per-minute billing with options for weekly/monthly flat rates, enabling granular cost control for variable workloads—no charges during idle minutes within reservations. This contrasts Scaleway's per-hour billing model, which rounds up usage and suits predictable, sustained runs but penalizes short bursts (e.g., a 10-minute job costs a full hour). Neither prominently features spot instances, though LeaderGPU's flexibility mimics on-demand efficiency. Scaleway offers reserved instances for discounts on long-term commitments. Implications: LeaderGPU favors intermittent experimentation (e.g., 20-50% savings on sub-hour tasks), while Scaleway optimizes for production with volume discounts, reducing effective hourly rates for >100 GPU-hours/month.

Value Assessment

For small experiments (<4 GPU-hours), LeaderGPU delivers superior value via per-minute precision, potentially halving costs versus Scaleway's hourly minimums. Large training runs (100+ GPU-hours) favor Scaleway's reserved discounts and Nabu efficiency, offering 20-30% better economics with integrated scaling. Production inference benefits Scaleway's per-hour predictability and ecosystem savings (e.g., bundled storage). LeaderGPU shines in fine-tuning with diverse GPUs at flat rates, undercutting Scaleway for weekly bursts. Overall, LeaderGPU for cost-optimized irregularity; Scaleway for scalable, compliant volume.

Use Case Comparison

LLM Training

Scaleway recommended

LeaderGPU

LeaderGPU's bare-metal servers with high-bandwidth networking support multi-GPU setups for training, leveraging diverse consumer cards like RTX 4090s for cost-effective scale. However, lacks integrated orchestration, requiring manual NCCL tuning; best for mid-scale models (<70B params) where raw perf trumps ease.

Scaleway

Scaleway excels via Nabu AI Supercomputer, optimized for large-scale distributed training with InfiniBand and Kubernetes support. Handles 100B+ models seamlessly in a sovereign environment, though virtualized overhead may add 5-10% latency vs bare-metal.

Batch Inference

Either works

LeaderGPU

Bare-metal high-bandwidth enables efficient batch processing on varied GPUs, with per-minute billing ideal for irregular queues. Suits rendering-like inference but needs custom queuing; strong for high-throughput, low-latency batches on consumer hardware.

Scaleway

Integrated services like Object Storage and Kubernetes streamline batch pipelines, with Nabu for massive parallelism. Per-hour billing fits steady jobs; EU sovereignty aids regulated data processing.

Real-time Inference

Scaleway recommended

LeaderGPU

High-bandwidth bare-metal GPUs provide low-latency inference, especially with NVLink-equipped cards. Per-minute flexibility suits variable traffic, but self-managed scaling limits production reliability.

Scaleway

Virtualized instances with auto-scaling and load balancers support real-time needs, enhanced by Nabu for high QPS. Stronger ecosystem for monitoring/integration, though potential virtualization overhead.

Fine-tuning & Experimentation

LeaderGPU recommended

LeaderGPU

Diverse consumer GPUs and per-minute billing optimize short experiments; flat rates for weeks enable iterative tuning without hourly waste. Bare-metal perf accelerates prototyping on varied hardware.

Scaleway

Kubernetes and pre-built images speed setup, but per-hour model inflates costs for <1h runs. Nabu suits larger experiments; good for teams valuing integration over granularity.

Technical Comparison

Infrastructure

LeaderGPU focuses on bare-metal dedicated servers, bypassing virtualization for maximal perf, with high-bandwidth (up to 100Gbps) networking and diverse storage via NVMe. Limited Kubernetes support requires manual deploys. Scaleway uses virtualized GPUs on shared hosts, integrated with managed Kubernetes (Kapsule), block/object storage, and multi-AZ EU regions for sovereignty. LeaderGPU offers direct hardware access; Scaleway emphasizes managed ops.

Performance

LeaderGPU's bare-metal yields peak GPU utilization (e.g., 99% on RTX cards) and superior multi-GPU scaling via PCIe/NVLink, ideal for bandwidth-intensive ML. Diverse availability includes A100/H100 equivalents. Scaleway's Nabu provides cluster-scale InfiniBand (400Gbps+), excelling in distributed training but with 5-15% virtualization tax. LeaderGPU faster for single-node; Scaleway for large-scale, with better uptime SLAs.

Frequently Asked Questions

What is the minimum billing increment for each provider?▾

LeaderGPU bills per-minute, while Scaleway bills per-hour. Consider your typical workload duration when evaluating which billing model offers better value for your use case.

Which provider has better compliance certifications for enterprise use?▾

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

Which provider offers better development tools like Jupyter notebooks?▾

Scaleway offers built-in Jupyter notebook support for interactive development, while LeaderGPU requires you to set up your own notebook environment. If quick iteration and experimentation are priorities, Scaleway's integrated notebooks provide a smoother experience. Additionally, Scaleway offers web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Scaleway offers native Kubernetes support for container orchestration, while LeaderGPU does not. If you're building production ML pipelines with Kubernetes-based tools like Kubeflow, Argo, or KServe, Scaleway will integrate more seamlessly with your workflow.

What is each provider best suited for?▾

LeaderGPU is best suited for Hash cracking and rendering tasks. 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 LeaderGPU 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?▾

LeaderGPU offers dedicated enterprise support options, while Scaleway may have more limited support tiers.

Which provider has better API and automation support?▾

Neither provider prominently advertises API access for automation. Check their documentation for programmatic instance management options.

Which provider has better container and Docker support?▾

LeaderGPU 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?▾

LeaderGPU's standout features include: Flexible weekly/monthly flat-rate billing; Diverse consumer GPU cards. 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 LeaderGPU, visit their website at https://www.leadergpu.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.