Provider Comparison

Crusoe vs Vast.ai

Crusoe and Vast.ai represent contrasting approaches in the GPU cloud market for ML/AI workloads. Crusoe positions itself as a climate-aligned provider, leveraging stranded energy sources like flared natural gas to power sustainable high-performance computing. This appeals to organizations prioritizing ESG compliance, offering reliable batch training with a vertically integrated energy-to-cloud model. Its SOC 2 and GDPR compliance, combined with per-hour billing and spot instances, suits enterprise-grade workloads where carbon footprint metrics matter. However, its smaller geographic footprint limits latency-sensitive applications. Vast.ai, conversely, operates as a decentralized marketplace connecting users directly to GPU hosts worldwide, emphasizing absolute lowest costs through competitive bidding. Ideal for cost-sensitive users and distributed experiments, it features granular filters like DLPerf/$ for performance-per-dollar optimization. Billing is per-hour with spot options, and GDPR compliance, but lacks Crusoe's SOC 2 depth. Availability can be inconsistent due to its peer-hosted nature. Key differentiators include Crusoe's sustainability and reliability versus Vast.ai's cost leadership and flexibility. Crusoe excels for ESG-driven teams needing predictable scaling; Vast.ai for budget-constrained experimenters tolerating variability. Overall, Crusoe offers premium green value, while Vast.ai democratizes access at rock-bottom prices, guiding selection based on priorities like compliance, cost, and uptime.

Visit Crusoe Visit Vast.ai

Our Recommendation

Choose Crusoe for organizations with ESG mandates, medium-to-large teams (10+ engineers) running production batch training or inference where reliability and compliance (SOC 2) outweigh marginal cost savings. It's ideal for budgets allowing 20-50% premiums for sustainability reporting and multi-GPU clusters in US regions. Opt for Vast.ai with small teams or solo practitioners focused on rapid experimentation, fine-tuning, or distributed jobs on tight budgets (<$0.20/GPU-hour). It suits technical setups tolerant of host variability, spot interruptions, and self-managed networking. For hybrid needs, start with Vast.ai for prototyping and migrate to Crusoe for scale-up, ensuring Kubernetes compatibility checks.

Live Pricing

Compare real-time GPU offers from Crusoe and Vast.ai

65 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
QuantaCloud Partner	A100 · H100 / H200 32–1024+ GPUs · InfiniBand	∞	Custom configs	Multiple DCs	Reserved / cluster Get a quote in 24h	Available
Vast.ai	8×NVIDIA GeForce RTX 3060 12GB VRAM	12GB	24 vCPU 126GB RAM 738GB Storage	Quebec	$0.00/GPU/hr $0.01/hr total (8×)	Sold Out
Vast.ai	6×NVIDIA GeForce RTX 3080 Ti 12GB VRAM	12GB	8 vCPU 94GB RAM 1527GB Storage	Ukraine	$0.01/GPU/hr $0.04/hr total (6×)	Sold Out
Vast.ai	6×NVIDIA GeForce RTX 3080 Ti 12GB VRAM	12GB	8 vCPU 94GB RAM 1660GB Storage	Ukraine	$0.01/GPU/hr $0.04/hr total (6×)	Sold Out
Vast.ai	NVIDIA GeForce RTX 3060 12GB VRAM	12GB	4 vCPU 23GB RAM 670GB Storage	Turkey	$0.01/GPU/hr	Sold Out
Vast.ai	NVIDIA GeForce RTX 2080 Ti 11GB VRAM	11GB	16 vCPU 31GB RAM 1549GB Storage	Georgia	$0.01/GPU/hr	Sold Out

QuantaCloud

Partner

Available

A100 · H100 / H200

32–1024+ GPUs · InfiniBand

Reserved / cluster

Get a quote in 24h

Vast.ai

Quebec

Sold Out

NVIDIA GeForce RTX 30608x

12GB VRAM

24 vCPU

126GB RAM

738GB Storage

625 Mbps ↑

626 Mbps ↓

$0.00/GPU/hr

$0.01/hr total (8×)

Vast.ai

Ukraine

Sold Out

NVIDIA GeForce RTX 3080 Ti6x

12GB VRAM

8 vCPU

94GB RAM

1527GB Storage

$0.01/GPU/hr

$0.04/hr total (6×)

Vast.ai

Ukraine

Sold Out

NVIDIA GeForce RTX 3080 Ti6x

12GB VRAM

8 vCPU

94GB RAM

1660GB Storage

394 Mbps ↑

689 Mbps ↓

$0.01/GPU/hr

$0.04/hr total (6×)

Vast.ai

Turkey

Sold Out

NVIDIA GeForce RTX 3060

12GB VRAM

4 vCPU

23GB RAM

670GB Storage

21 Mbps ↑

99 Mbps ↓

$0.01/GPU/hr

Vast.ai

Georgia

Sold Out

NVIDIA GeForce RTX 2080 Ti

11GB VRAM

16 vCPU

31GB RAM

1549GB Storage

722 Mbps ↑

388 Mbps ↓

$0.01/GPU/hr

View all 65 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

Crusoe(Est. 2018)

A climate-aligned computing provider powering high-performance computing using stranded energy sources to mitigate environmental impact.

Best For

Organizations with strict ESG mandatesBatch training workloads where carbon footprint is a key metric

Unique Features

Vertically integrated energy-to-cloud model
Use of stranded energy sources

Limitations

Smaller geographic footprint compared to hyperscalers

Vast.ai(Est. 2018)

A decentralized marketplace for absolute lowest costs and distributed experiments.

Best For

Absolute lowest costsDistributed experiments

Unique Features

Granular search filters like DLPerf/$
Decentralized marketplace

Feature Comparison

Access Methods

Feature	Crusoe	Vast.ai
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	Crusoe	Vast.ai
Billing Increment	per-hour	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	Crusoe	Vast.ai
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	Crusoe	Vast.ai
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Both providers use per-hour billing with spot instances, but differ in structure and predictability. Crusoe offers on-demand and spot pricing on standardized clusters (e.g., A100 at ~$1.45/hour, H100 competitive), with reserved options for long-term commitments, minimizing billing granularity issues for steady workloads. Vast.ai's marketplace enables dynamic bidding, yielding ultra-low spot rates (A100 often $0.10-0.40/hour), but per-minute billing risks overcharges on interruptions. No reserved instances; costs fluctuate with supply/demand. Implications: Crusoe favors predictable, long-running jobs (e.g., multi-day training saves via spots without surprise bills); Vast.ai suits bursty, interruptible usage where users optimize via DLPerf/$ filters, but demands vigilant monitoring.

Value Assessment

Vast.ai delivers superior value for small experiments and fine-tuning, with 50-80% lower costs enabling 2-5x more iterations on the same budget, ideal for prototyping on consumer GPUs. For large training runs, Crusoe provides better value through reliable multi-GPU scaling and lower effective costs via spots (e.g., 30% discounts), avoiding Vast.ai's downtime losses. Production inference favors Crusoe's consistent uptime; Vast.ai shines for non-critical batch inference if tolerating variability. Overall, Vast.ai maximizes ROI for cost-first, low-commitment scenarios; Crusoe for high-volume, compliance-needing workloads where TCO includes reliability.

Use Case Comparison

LLM Training

Crusoe recommended

Crusoe

Crusoe excels for large-scale LLM training with reliable multi-GPU clusters (A100/H100), leveraging stranded energy for sustainable, cost-effective batch runs. Vertically integrated infra ensures low-latency NVLink scaling and SOC 2 compliance, ideal for enterprise teams tracking carbon metrics. Spot instances reduce costs for interruptible pre-training, though limited regions may constrain global data access.

Vast.ai

Vast.ai supports distributed LLM training via cheap spot GPUs worldwide, with DLPerf/$ filters aiding optimal host selection. Suits cost-sensitive scaling across heterogeneous nodes, but reliability varies—host downtime or mismatched interconnects can disrupt long jobs, requiring custom orchestration.

Batch Inference

Either works

Crusoe

Crusoe handles batch inference efficiently on dedicated clusters, with predictable performance and ESG reporting. Spot pricing lowers costs for non-urgent jobs; Kubernetes support simplifies scaling, though smaller footprint limits edge deployment.

Vast.ai

Vast.ai offers unbeatable low costs for batch inference via marketplace bidding, enabling massive parallelization on diverse GPUs. Granular filters optimize for throughput/$, but interruptions and variable networking demand fault-tolerant designs.

Real-time Inference

Crusoe recommended

Crusoe

Crusoe provides stable low-latency inference via on-demand instances in US data centers, with compliant storage options. Lacks global POPs, potentially increasing cold-start times for international users; suits internal enterprise APIs.

Vast.ai

Vast.ai enables cheap real-time inference on edge-like hosts globally, but inconsistent uptime and networking (no guaranteed SLAs) risk latency spikes, making it unsuitable for production SLAs without heavy redundancy.

Fine-tuning & Experimentation

Vast.ai recommended

Crusoe

Crusoe supports experimentation on high-end GPUs with spots, but higher base rates limit hyper-iteration; best for structured teams valuing compliance over volume.

Vast.ai

Vast.ai dominates with rock-bottom pricing and vast GPU variety, allowing 10x more experiments via quick spin-up/down. DLPerf/$ and search tools accelerate iteration, perfect for solo devs or rapid prototyping despite variability.

Technical Comparison

Infrastructure

Crusoe employs vertically integrated bare-metal clusters powered by stranded energy, offering standardized NVIDIA GPUs (A100, H100) with NVLink, high-bandwidth networking (e.g., 400Gbps InfiniBand), EBS-like storage, and managed Kubernetes. Limited to US/Canada regions. Vast.ai's decentralized marketplace aggregates heterogeneous hosts—bare metal to virtualized—from global providers/individuals, with varied storage (local SSD/NVMe) and networking (1-100Gbps). Supports Docker/K8s via user config, but no unified managed services.

Performance

Crusoe delivers consistent high performance for multi-GPU scaling (e.g., 8x H100 pods with full NVLink), low inter-node latency, and high availability (>99%). Vast.ai offers strong DLPerf/$ value on filtered hosts, but performance varies: excellent single-node, challenged by heterogeneous multi-GPU (e.g., PCIe limits). GPU availability is abundant but spotty; Crusoe better for sustained workloads, Vast.ai for opportunistic bursts.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both Crusoe and Vast.ai offer spot/preemptible instances, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and distributed training with checkpointing. The actual savings depend on current demand and GPU availability, so we recommend comparing real-time spot prices for your specific GPU requirements on both platforms.

What is the minimum billing increment for each provider?▾

Crusoe bills per-hour, while Vast.ai 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?▾

Crusoe holds SOC 2, GDPR certifications. Vast.ai holds GDPR certification. For organizations with strict compliance requirements, Crusoe offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

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

Which provider has better Kubernetes support for orchestration?▾

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

What is each provider best suited for?▾

Crusoe is best suited for Organizations with strict ESG mandates; Batch training workloads where carbon footprint is a key metric. Vast.ai excels at Absolute lowest costs; Distributed experiments. 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?▾

Crusoe offers reserved instance pricing for long-term commitments, while Vast.ai 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?▾

Crusoe offers dedicated enterprise support options, while Vast.ai may have more limited support tiers.

Which provider has better API and automation support?▾

Both Crusoe and Vast.ai provide APIs for programmatic instance management, enabling automation of provisioning, scaling, and teardown operations. This is essential for integrating GPU resources into CI/CD pipelines and automated ML workflows.

Which provider has better container and Docker support?▾

Both Crusoe and Vast.ai support containerized workloads, allowing you to deploy Docker images with your ML frameworks, dependencies, and models pre-configured. This ensures reproducibility and simplifies deployment across development, staging, and production environments.

What unique features differentiate these providers?▾

Crusoe's standout features include: Vertically integrated energy-to-cloud model; Use of stranded energy sources. Vast.ai's standout features include: Granular search filters like DLPerf/$; Decentralized marketplace. 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 Crusoe, visit their website at https://crusoe.ai?utm_source=gpuperhour&utm_medium=referral to create an account and explore available GPU options. For Vast.ai, visit https://cloud.vast.ai/?ref_id=375842&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.