Provider Comparison

JarvisLabs vs Scaleway

JarvisLabs and Scaleway represent contrasting approaches in the GPU cloud market for AI/ML workloads. JarvisLabs targets developers, hobbyists, students, and fast.ai learners with a focus on extreme simplicity and cost-effective experimentation. Its per-minute billing, spot instances, pause functionality (halting compute costs while preserving storage), and one-click Jupyter environments make it ideal for quick prototyping and intermittent use. However, it lacks enterprise-grade compliance, limiting adoption for regulated environments. Scaleway, a major European provider, emphasizes data sovereignty, GDPR compliance (SOC 2, ISO 27001), and integrated cloud services. Best suited for teams needing European data residency, it offers the Nabu AI Supercomputer for high-performance computing, strong environmental credentials, and per-hour billing. This positions it well for production-scale workloads requiring reliability and ecosystem integration, though its coarser billing granularity may increase costs for short sessions. Key differentiators include JarvisLabs' affordability and ease for solo users versus Scaleway's compliance, sovereignty, and supercomputing capabilities. JarvisLabs excels in value for bursty, experimental workloads, while Scaleway provides robust infrastructure for sustained, compliant operations. ML engineers should weigh simplicity and cost against regulatory needs and scale when choosing.

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Our Recommendation

Choose JarvisLabs for small teams, students, or solo ML engineers focused on fine-tuning, experimentation, or short training runs where per-minute billing and pause features minimize costs (e.g., budgets under $500/month). It's ideal for non-production use without compliance needs, supporting rapid iteration via Jupyter. Opt for Scaleway when European data sovereignty, GDPR/SOC 2 compliance, or integrated services (storage, Kubernetes) are required, especially for mid-to-large teams running production inference or large-scale training on Nabu supercomputers. It's better for steady workloads with budgets over $1,000/month, where per-hour billing suits longer sessions and environmental priorities matter. For hybrid needs, start with JarvisLabs for prototyping and migrate to Scaleway for production.

Live Pricing

Compare real-time GPU offers from JarvisLabs and Scaleway

40 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
JarvisLabs	NVIDIA Quadro RTX 5000 16GB VRAM	16GB	7 vCPU 16GB RAM	🌍Global	$0.39/GPU/hr
JarvisLabs	NVIDIA L4 24GB VRAM	24GB	32 vCPU 24GB RAM	🌍Global	$0.44/GPU/hr
JarvisLabs	NVIDIA RTX A5000 24GB VRAM	24GB	32 vCPU 24GB RAM	🌍Global	$0.49/GPU/hr
JarvisLabs	NVIDIA RTX A6000 48GB VRAM	48GB	7 vCPU 48GB RAM	🌍Global	$0.79/GPU/hr
JarvisLabs	NVIDIA A100 PCIe 80GB 80GB VRAM	80GB	16 vCPU 40GB RAM	🌍Global	$0.89/GPU/hr

QuantaCloud

Partner

Available

A100 · H100 / H200 · B200 / B300

32–1024+ GPUs · InfiniBand

Reserved / cluster

Get a quote in 24h

JarvisLabs

🌍Global

NVIDIA Quadro RTX 5000

16GB VRAM

7 vCPU

16GB RAM

$0.39/GPU/hr

JarvisLabs

🌍Global

NVIDIA L4

24GB VRAM

32 vCPU

24GB RAM

$0.44/GPU/hr

JarvisLabs

🌍Global

NVIDIA RTX A5000

24GB VRAM

32 vCPU

24GB RAM

$0.49/GPU/hr

JarvisLabs

🌍Global

NVIDIA RTX A6000

48GB VRAM

7 vCPU

48GB RAM

$0.79/GPU/hr

JarvisLabs

🌍Global

NVIDIA A100 PCIe 80GB

80GB VRAM

16 vCPU

40GB RAM

$0.89/GPU/hr

View all 40 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

JarvisLabs(Est. 2019)

A developer and hobbyist-focused provider emphasizing extreme simplicity for AI workloads.

Best For

Students and fast.ai learnersCost-effective experimentation

Unique Features

Pause functionality to stop compute billing while preserving storage
One-click Jupyter environments

Limitations

Lack of enterprise compliance

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	JarvisLabs	Scaleway
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

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

Compliance

Certification	JarvisLabs	Scaleway
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	JarvisLabs	Scaleway
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

JarvisLabs employs per-minute billing with spot instances, enabling precise cost control for variable workloads—ideal for sessions under an hour, as users pay only for active compute time. The pause feature further optimizes by suspending billing while retaining data, contrasting Scaleway's per-hour billing model, which rounds up to the nearest hour and lacks equivalent pausing. Scaleway offers on-demand and potentially reserved options but no spots mentioned, suiting predictable usage. Implications: JarvisLabs favors intermittent experimentation (e.g., 10-30 min runs save 50-80% vs hourly), while Scaleway risks overbilling for short bursts but provides stability for long runs without minute-level granularity risks.

Value Assessment

JarvisLabs delivers superior value for small experiments and fine-tuning (e.g., 20-50% cheaper for <1hr sessions via per-minute/spot), making it optimal for hobbyists or prototyping budgets. For large LLM training or batch inference (multi-hour), Scaleway's per-hour model and Nabu infrastructure offer better economies at scale, especially with compliance overhead avoided elsewhere. Production real-time inference favors Scaleway for reliability, though JarvisLabs edges spot-driven intermittency. Overall, JarvisLabs wins for <10hr/week usage; Scaleway for sustained >20hr/week with sovereignty needs.

Use Case Comparison

LLM Training

Scaleway recommended

JarvisLabs

JarvisLabs suits smaller-scale LLM training with spot instances and per-minute billing, allowing cost-effective multi-GPU setups for models under 7B params. Pause functionality aids iterative training pauses, but limited enterprise features and potential spot interruptions hinder massive, uninterrupted runs.

Scaleway

Scaleway excels for large LLM training via Nabu AI Supercomputer, offering high GPU density, reliable multi-node scaling, and European sovereignty. Per-hour billing supports long sessions, with compliance ensuring data security for sensitive models.

Batch Inference

Scaleway recommended

JarvisLabs

JarvisLabs fits well for ad-hoc batch inference with quick spin-up Jupyter environments and spot pricing, ideal for variable batch sizes. Per-minute billing optimizes sporadic jobs, though lacks advanced orchestration for high-volume production batches.

Scaleway

Scaleway handles large-scale batch inference effectively through integrated storage/Kubernetes and Nabu clusters, providing consistent performance and compliance for enterprise pipelines processing massive datasets.

Real-time Inference

Scaleway recommended

JarvisLabs

JarvisLabs supports basic real-time inference via simple GPU instances but lacks robust autoscaling or low-latency networking, making it less ideal for production SLAs. Pause feature helps testing but not continuous serving.

Scaleway

Scaleway is stronger for real-time inference with Nabu supercomputer's low-latency interconnects, Kubernetes support, and compliance, enabling scalable, sovereign deployments for production APIs.

Fine-tuning & Experimentation

JarvisLabs recommended

JarvisLabs

JarvisLabs is perfectly suited with one-click Jupyter, per-minute/spot billing, and pause for rapid, low-cost iterations—ideal for students experimenting on datasets like fast.ai courses without commitment.

Scaleway

Scaleway works for experimentation but per-hour billing inflates short-run costs; better for compliant fine-tuning needing integrated services, though less agile for hobbyist-style quick tests.

Technical Comparison

Infrastructure

JarvisLabs focuses on virtualized GPU instances with simple, on-demand provisioning, emphasizing ease over complexity—no native Kubernetes but supports Jupyter/Docker. Storage is persistent across pauses. Scaleway offers a broader stack: bare-metal options via Nabu, virtual instances, high-speed networking (up to 100Gbps), block/object storage, and managed Kubernetes for orchestration, prioritizing integrated, sovereign European infrastructure.

Performance

JarvisLabs provides reliable single/multi-GPU (A100/H100) availability for small clusters, with good per-instance perf but potential spot preemptions affecting scaling. Scaleway's Nabu delivers top-tier multi-GPU scaling (hundreds of GPUs) via custom interconnects, excelling in large-model training throughput; strong uptime but regional focus may limit global latency. Both handle AI workloads well, with Scaleway edging sustained perf.

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

JarvisLabs 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, JarvisLabs would be the better choice.

What is the minimum billing increment for each provider?▾

JarvisLabs 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?▾

JarvisLabs holds no publicly listed 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?▾

Both JarvisLabs 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?▾

Scaleway offers native Kubernetes support for container orchestration, while JarvisLabs 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?▾

JarvisLabs is best suited for Students and fast.ai learners; Cost-effective experimentation. 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?▾

Scaleway offers reserved instance pricing for long-term commitments, while JarvisLabs does not currently offer this option. 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?▾

Neither provider prominently advertises enterprise support tiers. Contact each provider directly to discuss custom support arrangements for production deployments.

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?▾

JarvisLabs 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?▾

JarvisLabs's standout features include: Pause functionality to stop compute billing while preserving storage; One-click Jupyter environments. 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 JarvisLabs, visit their website at https://jarvislabs.ai?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.