Provider Comparison

CoreWeave vs Scaleway

CoreWeave and Scaleway represent distinct approaches in the GPU cloud market for AI and ML workloads. CoreWeave positions itself as a premier, specialized provider optimized for massive-scale AI training and VFX rendering, leveraging a Kubernetes-native architecture and access to expansive InfiniBand clusters. It targets sophisticated engineering teams handling large language model (LLM) training or bursty rendering needs, offering per-second billing and spot instances for cost efficiency. However, its inventory constraints can hinder accessibility for new or smaller users. Compliance includes SOC 2, HIPAA, GDPR, and ISO 27001, enabling broad enterprise adoption. In contrast, Scaleway, a major European provider, emphasizes data sovereignty and integrated cloud services, making it ideal for EU-regulated workloads. Its Nabu AI Supercomputer provides high-performance GPU resources with strong environmental credentials, appealing to teams prioritizing compliance and sustainability. Billing is per-hour, with SOC 2, GDPR, and ISO 27001 certifications. Scaleway suits users needing seamless integration with other cloud services like object storage or databases. Key differentiators include CoreWeave's hyperscale networking and AI-specific optimizations versus Scaleway's regional focus and eco-friendly operations. CoreWeave excels in raw performance for exascale AI, while Scaleway offers reliable, sovereign infrastructure for mid-scale European projects. Value propositions hinge on scale: CoreWeave for cutting-edge training at any cost, Scaleway for balanced, compliant deployments with integrated ecosystems.

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Our Recommendation

Choose CoreWeave for large-scale LLM training or VFX rendering where Kubernetes-native orchestration, InfiniBand interconnects, and spot pricing enable massive parallelism for teams of 10+ engineers with budgets exceeding $100K/month. It's ideal for US/global teams unhindered by inventory waits, prioritizing performance over sovereignty. Opt for Scaleway when EU data residency is mandatory (e.g., GDPR-heavy industries), for mid-sized teams (5-20 members) running integrated workflows with Nabu clusters, or when environmental impact matters. It's better for budgets favoring predictable per-hour costs and smaller-to-medium experiments under $50K/month. For hybrid needs, evaluate Scaleway first if sovereignty trumps scale; otherwise, CoreWeave for technical superiority in AI hyperscaling.

Live Pricing

Compare real-time GPU offers from CoreWeave and Scaleway

41 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
Scaleway	8×NVIDIA L4 24GB VRAM	24GB	64 vCPU 384GB RAM	Paris	$0.90/GPU/hr $7.20/hr total (8×)	Sold Out
Scaleway	2×NVIDIA L4 24GB VRAM	24GB	16 vCPU 96GB RAM	Paris	$0.90/GPU/hr $1.80/hr total (2×)	Available
Scaleway	2×NVIDIA L4 24GB VRAM	24GB	16 vCPU 96GB RAM	Paris	$0.90/GPU/hr $1.80/hr total (2×)	Sold Out
Scaleway	2×NVIDIA L4 24GB VRAM	24GB	16 vCPU 96GB RAM	Warsaw	$0.90/GPU/hr $1.80/hr total (2×)	Available
Scaleway	8×NVIDIA L4 24GB VRAM	24GB	64 vCPU 384GB RAM	Warsaw	$0.90/GPU/hr $7.20/hr total (8×)	Available

QuantaCloud

Partner

Available

A100 · H100 / H200 · B200 / B300

32–1024+ GPUs · InfiniBand

Reserved / cluster

Get a quote in 24h

Scaleway

Paris

Sold Out

NVIDIA L48x

24GB VRAM

64 vCPU

384GB RAM

20000 Mbps ↑

20000 Mbps ↓

$0.90/GPU/hr

$7.20/hr total (8×)

Scaleway

Paris

Available

NVIDIA L42x

24GB VRAM

16 vCPU

96GB RAM

5000 Mbps ↑

5000 Mbps ↓

$0.90/GPU/hr

$1.80/hr total (2×)

Scaleway

Paris

Sold Out

NVIDIA L42x

24GB VRAM

16 vCPU

96GB RAM

5000 Mbps ↑

5000 Mbps ↓

$0.90/GPU/hr

$1.80/hr total (2×)

Scaleway

Warsaw

Available

NVIDIA L42x

24GB VRAM

16 vCPU

96GB RAM

5000 Mbps ↑

5000 Mbps ↓

$0.90/GPU/hr

$1.80/hr total (2×)

Scaleway

Warsaw

Available

NVIDIA L48x

24GB VRAM

64 vCPU

384GB RAM

20000 Mbps ↑

20000 Mbps ↓

$0.90/GPU/hr

$7.20/hr total (8×)

View all 41 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

CoreWeave(Est. 2017)

A premier specialized GPU cloud designed for massive-scale AI training and VFX rendering with Kubernetes-native architecture.

Best For

Sophisticated engineering teams training LLMs at scaleVFX studios requiring burst rendering capacity

Unique Features

Kubernetes-native architecture
Access to massive-scale InfiniBand clusters

Limitations

Inventory often constrained for new or smaller users

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	CoreWeave	Scaleway
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	CoreWeave	Scaleway
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	CoreWeave	Scaleway
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

CoreWeave's per-second billing with spot instances contrasts sharply with Scaleway's per-hour model. CoreWeave allows granular charging, ideal for short bursts or preemptible workloads, potentially saving 50-90% via spots during low-demand periods. No reserved instances are highlighted, but on-demand ensures flexibility. Scaleway's hourly billing suits steady-state usage, with minimum 1-hour charges that can inflate costs for quick jobs. Neither emphasizes long-term commitments publicly, but CoreWeave's model favors variable patterns like experimentation or failed runs, while Scaleway penalizes interruptions. Implications: intermittent users save with CoreWeave; consistent inference favors Scaleway's predictability, though per-hour granularity limits fine-grained optimization.

Value Assessment

CoreWeave delivers superior value for large training runs and batch inference, where spot per-second billing minimizes costs during multi-day jobs—e.g., 70% savings on H100 clusters for 100-GPU LLM fine-tunes. It's less ideal for tiny experiments due to onboarding friction. Scaleway offers better value for production real-time inference and small-scale fine-tuning, with per-hour stability and integrated services reducing total ownership costs in EU setups. For sub-hour experiments, CoreWeave wins; steady mid-scale workloads (e.g., daily batches) favor Scaleway's no-surprise pricing. Overall, CoreWeave edges hyperscalers, Scaleway budget-conscious Europeans.

Use Case Comparison

LLM Training

CoreWeave recommended

CoreWeave

CoreWeave excels with Kubernetes-native scaling across massive InfiniBand clusters, enabling efficient multi-node LLM pre-training for billions of parameters. Spot instances optimize costs for long runs, though inventory limits may delay startups. Ideal for teams needing 100+ GPUs without virtualization overhead.

Scaleway

Scaleway's Nabu Supercomputer supports large-scale training but lacks CoreWeave's InfiniBand density, relying on standard Ethernet. Suitable for mid-scale EU-compliant runs, with integrated storage aiding data pipelines, but per-hour billing increases costs for extended jobs.

Batch Inference

Either works

CoreWeave

CoreWeave's per-second spot pricing shines for bursty batch jobs, with Kubernetes orchestration streamlining scaling. High GPU density accelerates large inference queues, though access constraints affect smaller batches.

Scaleway

Scaleway handles batches well via Nabu, with sovereignty ensuring compliant processing. Per-hour billing suits predictable volumes, and integrated services like object storage simplify workflows for European teams.

Real-time Inference

Scaleway recommended

CoreWeave

CoreWeave supports low-latency inference via optimized clusters, but its AI-training focus may overprovision for steady serving. Kubernetes aids autoscaling, yet inventory and setup complexity suit larger deployments.

Scaleway

Scaleway's integrated ecosystem excels for production serving, with Nabu GPUs and EU networking ensuring low-latency, sovereign real-time apps. Predictable hourly costs align with always-on needs.

Fine-tuning & Experimentation

Scaleway recommended

CoreWeave

CoreWeave's per-second billing is cost-effective for iterative experiments, with easy Kubernetes spins-ups. However, constrained inventory frustrates small teams needing quick 1-8 GPU access.

Scaleway

Scaleway offers accessible GPUs for fine-tuning via Nabu, with per-hour simplicity and integrated tools for rapid prototyping. Strong for EU users avoiding waitlists.

Technical Comparison

Infrastructure

CoreWeave employs a Kubernetes-native, bare-metal-like architecture with massive InfiniBand fabrics for low-latency multi-GPU scaling, paired with high-performance storage. It prioritizes AI workloads over general cloud. Scaleway uses virtualized instances with Nabu dedicated clusters, Ethernet-heavy networking, and integrated block/object storage. Kubernetes support exists but is less native; focus on EU data centers ensures sovereignty but limits global reach.

Performance

CoreWeave leads in multi-GPU scaling via InfiniBand (up to 3.2 Tbps/node), ideal for LLM training with minimal overhead—users report 95% scaling efficiency on 1000+ H100s. GPU availability is high for approved users but spotty for newcomers. Scaleway's Nabu delivers strong single-cluster performance (e.g., A100/H100 pods) but Ethernet bottlenecks large-scale interconnects. Reliable for mid-scale, with good uptime; environmental optimizations may slightly impact raw density.

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

CoreWeave 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, CoreWeave would be the better choice.

What is the minimum billing increment for each provider?▾

CoreWeave bills per-second, while Scaleway bills per-hour. Per-second billing from CoreWeave 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?▾

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

Which provider offers better development tools like Jupyter notebooks?▾

Both CoreWeave 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 CoreWeave 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?▾

CoreWeave is best suited for Sophisticated engineering teams training LLMs at scale; VFX studios requiring burst rendering capacity. 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 CoreWeave 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?▾

CoreWeave offers dedicated enterprise support options, while Scaleway may have more limited support tiers. Regarding SLAs: CoreWeave offers SLA guarantees; Scaleway has no published SLA.

Which provider has better API and automation support?▾

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

Which provider has better container and Docker support?▾

CoreWeave 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?▾

CoreWeave's standout features include: Kubernetes-native architecture; Access to massive-scale InfiniBand clusters. 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 CoreWeave, visit their website at https://www.coreweave.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.