Paperspace vs Sesterce: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Paperspace and Sesterce. Updated July 2026.
Provider Overview
Strengths & Best For
Paperspace (now part of DigitalOcean) offers A100, RTX 4000 ADA, and RTX 5000 ADA GPU instances alongside Gradient, its managed ML platform with Jupyter notebooks, experiment tracking, and one-click model deployment. On-demand and monthly billing options make it accessible for individuals and small teams exploring AI training and fine-tuning without complex infrastructure setup. A beginner-friendly on-demand GPU cloud with a polished notebook-centric experience.
- Managed ML platform
- Jupyter notebooks
- Simple UI
- DigitalOcean integration
Sesterce is a French GPU cloud offering one of the widest GPU selections in Europe — 26 GPU types including A30, A100, and H100 — across 11 EU regions, making it the broadest European GPU cloud for teams with diverse hardware requirements. On-demand and reserved billing options are available with competitive pricing for AI training, LLM fine-tuning, and inference workloads. A top choice for EU AI teams that need GPU variety and GDPR-compliant European data residency.
- 26 GPU types
- 11 regions
- EU-based infrastructure
- Competitive A30/A100 pricing
Live GPU Pricing
Region Coverage
Popular Comparisons
Paperspace — specialist provider
Paperspace (now part of DigitalOcean) offers A100, RTX 4000 ADA, and RTX 5000 ADA GPU instances alongside Gradient, its managed ML platform with Jupyter notebooks, experiment tracking, and one-click model deployment. On-demand and monthly billing options make it accessible for individuals and small teams exploring AI training and fine-tuning without complex infrastructure setup. A beginner-friendly on-demand GPU cloud with a polished notebook-centric experience.
Sesterce — specialist provider
Sesterce is a French GPU cloud offering one of the widest GPU selections in Europe — 26 GPU types including A30, A100, and H100 — across 11 EU regions, making it the broadest European GPU cloud for teams with diverse hardware requirements. On-demand and reserved billing options are available with competitive pricing for AI training, LLM fine-tuning, and inference workloads. A top choice for EU AI teams that need GPU variety and GDPR-compliant European data residency.
Billing model comparison
Paperspace uses a On-demand, Monthly billing model with a minimum commitment of None. Sesterce uses On-demand, Reserved billing with a None minimum. Both providers offer flexible billing options — compare the live pricing table above to find the best rate for your specific GPU model and workload duration.
Which workloads each provider suits best
Paperspace is best suited for: ML beginners, Notebook-based workflows, Small teams. Its key strengths are managed ml platform, jupyter notebooks, simple ui. Sesterce is best suited for: EU AI teams, Wide GPU variety needs, Cost-sensitive European workloads. Its key strengths are 26 gpu types, 11 regions, eu-based infrastructure. Both providers target similar workload profiles — the live pricing table above is the most reliable way to determine which offers better value for your specific GPU model and region requirements.
Support tiers and region coverage
Paperspace offers Community → Growth support across 3 regions (US-East, US-West, EU-West). Sesterce offers Standard → Enterprise support across 4 regions (EU-West, EU-Central, US and 1 more). Sesterce's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: Paperspace vs Sesterce
Paperspace was founded in 2014 and is headquartered in New York, NY. Sesterce was founded in 2018 and is headquartered in Marseille, France. Paperspace has 4 years more operational history than Sesterce, which may matter for teams evaluating provider stability and long-term contract risk. Use the live pricing table above to compare current on-demand and spot rates for specific GPU models, and the region map to verify coverage in your target geography.