Provider Comparison

Lambda Labs vs Scaleway

Lambda Labs and Scaleway represent distinct approaches in the GPU cloud market for ML and AI workloads. Lambda Labs positions itself as a premier provider tailored for ML engineers, emphasizing deep hardware expertise as a system integrator. It offers pre-configured environments via Lambda Stack, which includes optimized Ubuntu, CUDA, PyTorch, and TensorFlow setups, enabling rapid deployment. This makes it ideal for teams seeking minimal setup friction. However, high demand often leads to frequent stock-outs, limiting availability of premium GPUs like H100s or A100s. Billing is per-hour on-demand, with SOC 2, GDPR, and ISO 27001 compliance. Scaleway, a major European cloud provider, focuses on data sovereignty and integrated services, appealing to users prioritizing EU-based operations. Its Nabu AI Supercomputer provides large-scale H100 clusters, complemented by strong environmental credentials through renewable energy usage. It suits organizations needing seamless integration with broader cloud services like object storage and Kubernetes. Like Lambda, it uses per-hour billing and matches compliance standards. Key differentiators include Lambda's ML-centric pre-configurations versus Scaleway's sovereignty and ecosystem integration. Lambda excels in quick prototyping for US-centric or global ML teams, while Scaleway offers better latency and regulatory alignment for European workloads. Value propositions hinge on use case: Lambda for specialized ML efficiency, Scaleway for compliant, scalable European infrastructure. Both deliver high-performance NVIDIA GPUs, but availability and regional focus guide selection.

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Our Recommendation

Choose Lambda Labs for small-to-medium ML teams (1-20 engineers) prioritizing instant productivity with pre-configured environments. It's optimal for US-based or global teams running fine-tuning, experimentation, or short training jobs where setup time is critical, and budgets allow on-demand pricing ($1.10-$3.29/hr for A100/H100 equivalents). Ideal if stock is available and multi-GPU NVLink scaling is needed without sovereignty concerns. Opt for Scaleway when data sovereignty is paramount, such as for EU-regulated industries (healthcare, finance). Suited for larger teams or enterprises leveraging integrated services like managed Kubernetes and Nabu for massive LLM training. Better for long-running jobs with potentially lower latency in Europe, though verify GPU availability. Budget-conscious users benefit from competitive per-hour rates, especially with green energy mandates. Avoid Lambda if frequent stock-outs disrupt workflows; prefer Scaleway for production-scale inference requiring compliance and ecosystem synergy.

Live Pricing

Compare real-time GPU offers from Lambda Labs 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
Lambda Labs	NVIDIA RTX 6000 Ada Generation 48GB VRAM	48GB	14 vCPU 46GB RAM 512GB Storage	🌍global	$0.69/GPU/hr	Sold Out
Lambda Labs	8×NVIDIA Tesla V100 16GB 16GB VRAM	16GB	88 vCPU 448GB RAM 6041GB Storage	🌍global	$0.79/GPU/hr $6.32/hr total (8×)	Sold Out
Lambda Labs	8×NVIDIA Tesla V100 16GB 16GB VRAM	16GB	88 vCPU 448GB RAM 6041GB Storage	Texas	$0.79/GPU/hr $6.32/hr total (8×)	Available
Lambda Labs	8×NVIDIA Tesla V100 16GB 16GB VRAM	16GB	92 vCPU 448GB RAM 6041GB Storage	🌍global	$0.79/GPU/hr $6.32/hr total (8×)	Sold Out
Lambda Labs	NVIDIA RTX A6000 48GB VRAM	48GB	14 vCPU 100GB RAM 256GB Storage	California	$0.80/GPU/hr	Sold Out

QuantaCloud

Partner

Available

A100 · H100 / H200 · B200 / B300

32–1024+ GPUs · InfiniBand

Reserved / cluster

Get a quote in 24h

Lambda Labs

🌍global

Sold Out

NVIDIA RTX 6000 Ada Generation

48GB VRAM

14 vCPU

46GB RAM

512GB Storage

$0.69/GPU/hr

Lambda Labs

🌍global

Sold Out

NVIDIA Tesla V100 16GB8x

16GB VRAM

88 vCPU

448GB RAM

6041GB Storage

$0.79/GPU/hr

$6.32/hr total (8×)

Lambda Labs

Texas

Available

NVIDIA Tesla V100 16GB8x

16GB VRAM

88 vCPU

448GB RAM

6041GB Storage

$0.79/GPU/hr

$6.32/hr total (8×)

Lambda Labs

🌍global

Sold Out

NVIDIA Tesla V100 16GB8x

16GB VRAM

92 vCPU

448GB RAM

6041GB Storage

$0.79/GPU/hr

$6.32/hr total (8×)

Lambda Labs

California

Sold Out

NVIDIA RTX A6000

48GB VRAM

14 vCPU

100GB RAM

256GB Storage

$0.80/GPU/hr

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

Lambda Labs(Est. 2012)

A premier GPU cloud provider with deep hardware expertise, offering pre-configured environments for ML engineers.

Best For

ML engineers wanting a pre-configured environment

Unique Features

Lambda Stack for easy setup
Deep hardware expertise as a system integrator

Limitations

Frequent stock-outs due to high demand

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	Lambda Labs	Scaleway
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	Lambda Labs	Scaleway
Billing Increment	per-hour	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	Lambda Labs	Scaleway
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	Lambda Labs	Scaleway
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Both Lambda Labs and Scaleway employ per-hour billing for on-demand GPU instances, with no per-second granularity or reserved instances prominently featured. Lambda offers straightforward on-demand pricing (e.g., A100 at ~$1.29/hr single GPU, scaling to multi-GPU clusters) and occasional spot instances at discounts up to 50%, though availability is limited by demand. Scaleway mirrors this with per-hour on-demand (H100s from ~€2.50/hr equivalent) and spot/preemptible options via their marketplace, integrated with broader cloud billing. Implications vary by usage: short, intermittent experiments favor spot discounts on both, minimizing costs for bursty workloads. Long-running training benefits from on-demand predictability, but Lambda's stock-outs may force pricier alternatives. Scaleway's unified billing suits hybrid CPU/GPU workflows, reducing administrative overhead. Neither emphasizes commitments for deeper discounts, making them flexible but potentially costlier than AWS/GCP reservations for sustained use.

Value Assessment

Lambda Labs provides superior value for small experiments and fine-tuning, where Lambda Stack slashes setup costs (hours saved vs. manual config) and per-hour rates compete favorably for 1-4 GPU jobs. Spot instances enhance value for non-urgent batch work, though stock limits reliability. Scaleway excels in large training runs and production inference, leveraging Nabu for efficient H100 scaling at EU-competitive prices, plus free ingress/egress in Europe cuts data transfer costs. Integrated storage (e.g., Object Storage at €0.01/GB) boosts value for data-heavy pipelines. For real-time inference, Scaleway's sovereignty and low-latency EU networking offer better TCO for compliant deployments. Overall, Lambda wins for rapid prototyping (better for <1 week jobs); Scaleway for scale/compliance (better for >1 month commitments). Evaluate via calculators for precise scenarios.

Use Case Comparison

LLM Training

Scaleway recommended

Lambda Labs

Lambda Labs suits LLM training well with multi-GPU instances (up to 8x H100 NVLink), pre-configured Lambda Stack for PyTorch/DeepSpeed, and hardware expertise ensuring optimal scaling. Quick 1-click deploys accelerate large model training, though stock-outs may delay H100 access, forcing A100 fallbacks.

Scaleway

Scaleway's Nabu Supercomputer excels for massive LLM training with dense H100 clusters, EU-low latency, and Kubernetes integration for orchestration. Sovereignty ensures compliant data handling, but less ML-specific pre-configs require more setup than Lambda.

Batch Inference

Either works

Lambda Labs

Lambda handles batch inference efficiently via scalable GPU clusters and easy TensorRT deployment on Lambda Stack. Per-hour billing fits variable loads, with spot options reducing costs for non-urgent jobs, though availability issues could interrupt pipelines.

Scaleway

Scaleway supports batch inference through Nabu H100s and integrated Object Storage for datasets, with preemptible instances for cost savings. EU data locality aids compliance-heavy batches, and managed services streamline autoscaling.

Real-time Inference

Scaleway recommended

Lambda Labs

Lambda's dedicated instances with low-latency NVLink suit real-time inference, optimized via Stack for ONNX/TensorRT. Single-GPU options are readily available when stocked, ideal for low-volume production serving.

Scaleway

Scaleway offers strong real-time performance with Nabu GPUs and EU networking for minimal latency in regional apps. Integrated load balancers and sovereignty make it preferable for compliant, high-availability inference services.

Fine-tuning & Experimentation

Lambda Labs recommended

Lambda Labs

Lambda shines here with 1-click Lambda Stack, enabling instant fine-tuning on A100/H100s. Pre-configs save days of setup, perfect for iterative experiments; spot pricing enhances affordability for short runs.

Scaleway

Scaleway works for experimentation via flexible GPU access and Kubernetes, but lacks Lambda's ML-specific optimizations, requiring custom env setup. Better for EU teams needing sovereignty during prototyping.

Technical Comparison

Infrastructure

Lambda Labs focuses on dedicated, non-virtualized GPU instances (1-8 GPUs per node, NVLink for H100/A100), with high-speed InfiniBand networking (up to 400Gb/s) and NVMe storage. Supports Kubernetes via bring-your-own but emphasizes 1-click Jupyter/SSH. Scaleway blends virtualized and bare-metal via Nabu (liquid-cooled H100 clusters) with 400Gb/s RoCE networking, elastic block storage (up to 32TB NVMe), and native managed Kubernetes. Lambda prioritizes ML isolation; Scaleway offers broader integration.

Performance

Lambda delivers top-tier multi-GPU scaling (e.g., 8x H100 at near-linear efficiency for training), with consistent CUDA performance due to custom tuning, but stock-outs limit H100 access. Scaleway's Nabu provides comparable H100 throughput and excellent EU intra-cluster bandwidth, strong for large-scale jobs; newer platform may have teething issues. Both support MIG for partitioning. Lambda edges in setup speed; Scaleway in regional availability and green efficiency.

Frequently Asked Questions

What is the minimum billing increment for each provider?▾

Lambda Labs bills per-hour, while Scaleway bills per-hour. Both providers use the same billing granularity, so this factor won't differentiate your decision.

Which provider has better compliance certifications for enterprise use?▾

Lambda Labs holds SOC 2, GDPR, ISO 27001 certifications. Scaleway holds SOC 2, 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 Lambda Labs 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 Lambda Labs 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?▾

Lambda Labs is best suited for ML engineers wanting a pre-configured environment. 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 Lambda Labs 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?▾

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

Which provider has better API and automation support?▾

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

Which provider has better container and Docker support?▾

Container support details are not prominently listed for either provider. Check their documentation for Docker and container runtime compatibility.

What unique features differentiate these providers?▾

Lambda Labs's standout features include: Lambda Stack for easy setup; Deep hardware expertise as a system integrator. 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 Lambda Labs, visit their website at https://lambdalabs.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.