Provider Comparison

FluidStack vs Scaleway

Q: Which provider offers spot instances for cost savings?

FluidStack 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, FluidStack would be the better choice.

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

FluidStack 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?

FluidStack holds SOC 2, ISO 27001 certifications. 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 FluidStack 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?

Both FluidStack 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?

FluidStack is best suited for Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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 FluidStack 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?

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

Q: Which provider has better API and automation support?

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

Q: Which provider has better container and Docker support?

FluidStack 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.

FluidStack and Scaleway represent distinct approaches in the GPU cloud market for ML/AI workloads. FluidStack operates as a supercloud aggregator, unifying access to GPUs across global data centers from Tier 1-4 facilities, including spare capacity. This positions it ideally for ML engineers needing massive, on-demand scale for large training runs, with global reach minimizing procurement delays. Its per-minute billing and spot instances enable cost optimization for variable workloads, though consistency can vary by underlying facility. Compliance includes SOC 2 and ISO 27001. Scaleway, a prominent European provider, emphasizes data sovereignty and integrated services, appealing to teams prioritizing GDPR compliance and EU residency. Its Nabu AI Supercomputer offers dedicated large-scale clusters, complemented by strong environmental credentials like renewable energy usage. Billing is per-hour, supporting predictable costs, with SOC 2, GDPR, and ISO 27001 certifications. Key differentiators: FluidStack excels in raw capacity and flexibility via aggregation, suiting distributed, bursty global teams; Scaleway provides seamless integration with its ecosystem (e.g., Kubernetes, object storage) and sovereignty guarantees, best for regulated European operations. Value propositions hinge on scale vs. reliability: FluidStack for cost-effective hyperscale, Scaleway for sovereign, eco-friendly infrastructure. Both cater to AI workloads but target different priorities—global opportunism versus regional control—making selection dependent on geography, compliance, and workload patterns. (238 words)

Visit FluidStack Visit Scaleway

Our Recommendation

Choose FluidStack for large-scale, global ML projects requiring immediate access to thousands of GPUs, such as multi-week training runs on teams with 10+ engineers handling distributed data. It's ideal for budget-conscious setups leveraging spot instances for non-critical workloads, or when EU data residency isn't mandated. Suited for startups scaling rapidly without long-term commitments. Opt for Scaleway when EU data sovereignty is essential (e.g., GDPR-regulated enterprises), or for teams needing integrated services like managed Kubernetes and object storage alongside GPUs. Best for mid-sized teams (5-20 engineers) running production workloads with predictable hourly billing, especially leveraging Nabu for structured large-scale compute. Prioritize Scaleway for sustainability-focused orgs or when consistency trumps raw global capacity. For hybrid needs, evaluate based on primary region and compliance. (142 words)

Live Pricing

Compare real-time GPU offers from FluidStack and Scaleway

35 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 35 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

FluidStack(Est. 2017)

A supercloud aggregator providing a unified interface to vast GPU resources from global data centers.

Best For

Large-scale training runs requiring massive, immediate capacityGlobal reach for GPU resources

Unique Features

Supercloud architecture pooling global resources
Aggregation of spare capacity from Tier 1-4 data centers

Limitations

Consistency may vary depending on underlying facility

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	FluidStack	Scaleway
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	FluidStack	Scaleway
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	FluidStack	Scaleway
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

FluidStack's per-minute billing with spot instances contrasts Scaleway's per-hour on-demand model. FluidStack enables granular cost control, charging only for active usage, ideal for bursty or interruptible jobs; spot pricing aggregates spare capacity for discounts up to 70-80% versus on-demand, but risks interruptions. No reserved instances noted. Scaleway's hourly billing suits steady workloads, with potential volume discounts, but incurs overhead for short runs (<1 hour). Implications: FluidStack favors experimentation and variable large-scale training (e.g., save 30-50% on spots), while Scaleway supports predictable inference/production with less granular metering. Both lack public per-GPU rates, requiring quotes, but FluidStack's model reduces costs for sub-hour tasks, potentially 20-40% cheaper for intermittent use versus Scaleway's minimums. (152 words)

Value Assessment

FluidStack delivers superior value for large training runs and batch jobs via spot per-minute savings, offering 2-3x cost efficiency for 100+ GPU clusters used sporadically. Ideal for high-utilization (>70%) hyperscale but less so for always-on inference due to variability. Scaleway provides better value for small-to-mid experiments and real-time inference in Europe, with hourly billing avoiding per-minute overhead and integrated services reducing TCO (e.g., no egress fees within ecosystem). Nabu clusters excel for sustained fine-tuning at potentially lower effective rates for EU users. Overall, FluidStack wins for cost-optimized scale (e.g., $0.5-1/hr H100 spots estimated); Scaleway for sovereign production ($1-2/hr equivalents) and green ops. Evaluate via trials for exact ROI. (148 words)

Use Case Comparison

LLM Training

FluidStack recommended

FluidStack

FluidStack shines for massive LLM training with global aggregation enabling 1000+ GPU clusters on-demand, spot pricing slashing costs for long runs. Unified API simplifies scaling across DCs, ideal for pre-training on petabyte datasets despite potential latency variability between facilities. (62 words)

Scaleway

Scaleway's Nabu Supercomputer supports large-scale training with dedicated EU clusters (up to 576 H100s noted), ensuring low-latency interconnects and sovereignty. Integrated storage aids data pipelines, but capacity may lag global hyperscalers for extreme scales. (64 words)

Batch Inference

Either works

FluidStack

FluidStack suits batch inference via elastic spot GPUs for cost-effective parallel processing, pooling spare capacity for high throughput. Per-minute billing optimizes variable batch sizes, though job queuing possible during peaks. (60 words)

Scaleway

Scaleway excels with integrated Kubernetes for orchestrated batch jobs, Nabu for GPU-dense inference. Hourly billing fits scheduled runs, EU storage minimizing data movement costs. Reliable for compliance-heavy batches. (62 words)

Real-time Inference

Scaleway recommended

FluidStack

FluidStack adequate for inference via on-demand GPUs, but aggregator variability may impact low-latency SLAs. Global DCs aid edge deployment, per-minute good for scaling traffic spikes. Less ideal for persistent services. (61 words)

Scaleway

Scaleway preferable with integrated services (e.g., serverless, load balancers) and Nabu for consistent low-latency inference. Hourly model supports always-on deployments, sovereignty for user data processing. (60 words)

Fine-tuning & Experimentation

FluidStack recommended

FluidStack

FluidStack's spot per-minute instances perfect for short, iterative fine-tuning, accessing diverse GPUs cheaply. Rapid provisioning suits rapid prototyping, global variety for testing configs. (60 words)

Scaleway

Scaleway supports experimentation via flexible instances and ecosystem tools, but hourly billing less efficient for <1hr runs. Nabu viable for larger tunes, EU focus aids collab. (61 words)

Technical Comparison

Infrastructure

FluidStack aggregates bare-metal GPUs from diverse global DCs (Tier 1-4), offering unified APIs over varied hardware; supports Kubernetes via bring-your-own, with high-bandwidth networking (up to 400Gbps InfiniBand in some). Storage via attached NVMe or cloud volumes. Scaleway provides virtualized/dedicated instances in EU DCs, full managed Kubernetes (Kapsule), block/object storage, and Nabu large-scale bare-metal clusters with NVLink/RoCE. FluidStack emphasizes capacity over integration; Scaleway prioritizes sovereign ecosystem. (102 words)

Performance

FluidStack offers high GPU availability via aggregation (e.g., A100/H100 rapid scaling), strong multi-node scaling for training (e.g., 10k+ GPUs), but inter-DC latency/performance varies. Scaleway's Nabu delivers consistent intra-cluster perf (e.g., 700TB/s aggregate on H100s), reliable EU scaling, potentially better for latency-sensitive. Both support NVLink/InfiniBand; FluidStack edges massive parallelism, Scaleway uniform reliability. Availability strong in FluidStack for spots, Scaleway for reservations. Benchmarks sparse—test for workloads. (98 words)

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

FluidStack 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, FluidStack would be the better choice.

What is the minimum billing increment for each provider?▾

FluidStack 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?▾

FluidStack holds SOC 2, ISO 27001 certifications. 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 FluidStack 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?▾

Both FluidStack 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?▾

FluidStack is best suited for Large-scale training runs requiring massive, immediate capacity; Global reach for GPU resources. 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 FluidStack 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?▾

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

Which provider has better API and automation support?▾

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

Which provider has better container and Docker support?▾

FluidStack 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?▾

FluidStack's standout features include: Supercloud architecture pooling global resources; Aggregation of spare capacity from Tier 1-4 data centers. 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 FluidStack, visit their website at https://www.fluidstack.io?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.