Provider Comparison

Crusoe vs DigitalOcean

Crusoe and DigitalOcean represent distinct approaches in the GPU cloud market for AI/ML workloads. Crusoe positions itself as a climate-aligned provider, leveraging stranded energy sources for sustainable high-performance computing. It appeals to organizations prioritizing ESG compliance and batch training where carbon footprint matters, offering a vertically integrated energy-to-cloud model with spot instances for cost efficiency. However, its smaller geographic footprint limits latency-sensitive global deployments. DigitalOcean, conversely, targets developers and startups with straightforward, predictable GPU Droplets featuring NVIDIA H100 and H200 accelerators. It excels in simplicity, integrating seamlessly with its ecosystem including DOKS Kubernetes, Spaces storage, and the 1-Click Models marketplace from the Paperspace acquisition. This makes it ideal for teams already in the DigitalOcean environment seeking quick GPU scaling without hyperscaler complexity, though its GPU inventory is limited compared to larger players. Key differentiators include Crusoe's environmental focus and spot pricing versus DigitalOcean's developer-friendly tools and broader compliance (HIPAA, ISO 27001). Both offer per-hour billing and SOC 2/GDPR, but Crusoe suits sustainability-driven batch jobs, while DigitalOcean favors rapid prototyping and integrated workflows. Value hinges on priorities: eco-impact and cost savings for Crusoe, ease-of-use and ecosystem lock-in for DigitalOcean.

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Our Recommendation

Choose Crusoe for organizations with ESG mandates running large-scale batch training or inference, especially where spot instances can optimize costs for interruptible workloads. It's ideal for mid-to-large teams (10+ engineers) budgeting for high-utilization runs (>70% cluster uptime) and tolerant of limited regions, prioritizing carbon metrics over global latency. Opt for DigitalOcean when simplicity and speed matter: startups or small teams (1-10 engineers) experimenting with H100/H200 GPUs, fine-tuning models, or deploying via 1-Click marketplace. It's best for budgets favoring predictable per-hour pricing without spot risks, teams in the DO ecosystem needing Kubernetes integration, or HIPAA-compliant apps. Avoid Crusoe for real-time inference due to geo limitations; skip DigitalOcean for massive-scale training lacking inventory depth.

Live Pricing

Compare real-time GPU offers from Crusoe and DigitalOcean

29 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
Crusoe	NVIDIA A40 48GB VRAM	48GB	0 vCPU 0GB RAM	United States	$0.40/GPU/hr
Crusoe	NVIDIA L40S 48GB VRAM	48GB	0 vCPU 0GB RAM	United States	$0.50/GPU/hr
DigitalOcean	NVIDIA RTX 4000 Ada Generation 20GB VRAM	20GB	8 vCPU 32GB RAM 500GB Storage	Toronto	$0.76/GPU/hr	Sold Out
Crusoe	NVIDIA A40 48GB VRAM	48GB	0 vCPU 0GB RAM	United States	$0.90/GPU/hr
Crusoe	AMD Instinct MI300X 192GB VRAM	192GB	0 vCPU 0GB RAM	United States	$0.95/GPU/hr

Crusoe

United States

NVIDIA A40

48GB VRAM

0 vCPU

0GB RAM

$0.40/GPU/hr

Crusoe

United States

NVIDIA L40S

48GB VRAM

0 vCPU

0GB RAM

$0.50/GPU/hr

DigitalOcean

Toronto

Sold Out

NVIDIA RTX 4000 Ada Generation

20GB VRAM

8 vCPU

32GB RAM

500GB Storage

$0.76/GPU/hr

Crusoe

United States

NVIDIA A40

48GB VRAM

0 vCPU

0GB RAM

$0.90/GPU/hr

Crusoe

United States

AMD Instinct MI300X

192GB VRAM

0 vCPU

0GB RAM

$0.95/GPU/hr

View all 29 offers

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

DigitalOcean(Est. 2011)

A developer-focused cloud provider offering simple, predictable GPU Droplets for AI/ML workloads, bringing NVIDIA H100 and H200 accelerators to its global developer community with the same simplicity its CPU droplets are known for.

Best For

Developers and startups wanting simple, predictable GPU pricingTeams already on the DigitalOcean ecosystem needing to add GPU capacity

Unique Features

1-Click Models marketplace for rapid model deployment
Integrated with DigitalOcean Kubernetes (DOKS) and Spaces object storage
Acquired Paperspace to bolster AI/ML platform (Gradient)

Limitations

Smaller GPU inventory compared to hyperscalers
Limited to NVIDIA H100/H200-class offerings

Feature Comparison

Access Methods

Feature	Crusoe	DigitalOcean
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	Crusoe	DigitalOcean
Billing Increment	per-hour	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	Crusoe	DigitalOcean
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	Crusoe	DigitalOcean
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

Both providers use per-hour billing, avoiding per-second granularity seen in hyperscalers, which simplifies budgeting but may underutilize short jobs. Crusoe differentiates with spot instances, offering deep discounts for interruptible capacity powered by stranded energy, ideal for fault-tolerant batch workloads. DigitalOcean sticks to on-demand per-hour pricing for H100/H200 Droplets, emphasizing predictability without reserved options mentioned. Implications vary: spot pricing suits high-utilization, preemptible tasks like training (potential 50-70% savings), but risks interruptions requiring checkpointing. DigitalOcean's model favors consistent, smaller-scale usage without eviction worries, though lacks discounts for commitment. Neither offers reserved instances per available data, making hyperscalers better for long-term locks. For sporadic experiments, per-hour aligns well, but sustained runs amplify spot value in Crusoe.

Value Assessment

DigitalOcean delivers superior value for small experiments and fine-tuning: predictable pricing on H100/H200 suits bursty dev workflows (e.g., $3-5/hr per GPU est.), with 1-Click deployments minimizing setup overhead. Startups save on ops via ecosystem integrations. Crusoe excels in large training runs and batch inference, where spot instances slash costs for 100+ GPU clusters (e.g., 30-50% below on-demand), aligning with ESG goals. Production inference leans DigitalOcean for reliability, absent spot evictions. Overall, DigitalOcean wins short/intermittent jobs (<1 week); Crusoe for prolonged batch (>80% utilization), but verify spot availability as inventory is finite. Limited Crusoe GPU pricing transparency requires direct quotes.

Technical Comparison

Infrastructure

Infrastructure comparison information not available.

Performance

Performance comparison information not available.

Frequently Asked Questions

Which provider offers spot instances for cost savings?▾

Crusoe 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. DigitalOcean 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, Crusoe would be the better choice.

What is the minimum billing increment for each provider?▾

Crusoe bills per-hour, while DigitalOcean 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. DigitalOcean holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. For organizations with strict compliance requirements, DigitalOcean offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

DigitalOcean 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, DigitalOcean's integrated notebooks provide a smoother experience. Additionally, DigitalOcean offers web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

Both Crusoe and DigitalOcean 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?▾

Crusoe is best suited for Organizations with strict ESG mandates; Batch training workloads where carbon footprint is a key metric. DigitalOcean excels at Developers and startups wanting simple, predictable GPU pricing; Teams already on the DigitalOcean ecosystem needing to add GPU capacity. 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 Crusoe and DigitalOcean 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?▾

Both Crusoe and DigitalOcean offer enterprise support tiers with dedicated assistance, faster response times, and potentially custom SLAs. Regarding SLAs: Crusoe has no published SLA; DigitalOcean offers SLA guarantees (99.99% uptime).

Which provider has better API and automation support?▾

Both Crusoe and DigitalOcean 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 DigitalOcean 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. DigitalOcean's standout features include: 1-Click Models marketplace for rapid model deployment; Integrated with DigitalOcean Kubernetes (DOKS) and Spaces object storage; Acquired Paperspace to bolster AI/ML platform (Gradient). 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 DigitalOcean, visit https://www.digitalocean.com/products/gpu-droplets 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.