Oblivus vs QuantaCloud: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Oblivus and QuantaCloud. Updated July 2026.
Provider Overview
Strengths & Best For
Oblivus offers H100 and A100 GPU instances on-demand at competitive rates with straightforward pricing and no hidden fees, making it an accessible option for AI training and inference workloads on a budget. Spot GPU rental is also available for further cost savings on interruptible jobs. A no-frills GPU cloud for startups and developers who want simple, transparent pricing without enterprise complexity.
- Low prices
- Simple pricing
- No hidden fees
- H100 availability
QuantaCloud provides bare-metal A100, H100, H200, and B300 GPU clusters with InfiniBand interconnect and no virtualization overhead, purpose-built for large-scale LLM training and multi-node distributed AI workloads. Reserved and cluster configurations are available for organizations that need dedicated GPU infrastructure with consistent performance for long-running training runs. A specialist bare-metal GPU cloud for AI labs and enterprises that need maximum cluster performance for frontier model training.
- Bare-metal performance
- InfiniBand networking
- Large cluster configs
- H200 and B300 availability
Live GPU Pricing
Region Coverage
Popular Comparisons
Oblivus — specialist provider
Oblivus offers H100 and A100 GPU instances on-demand at competitive rates with straightforward pricing and no hidden fees, making it an accessible option for AI training and inference workloads on a budget. Spot GPU rental is also available for further cost savings on interruptible jobs. A no-frills GPU cloud for startups and developers who want simple, transparent pricing without enterprise complexity.
QuantaCloud — bare-metal provider
QuantaCloud provides bare-metal A100, H100, H200, and B300 GPU clusters with InfiniBand interconnect and no virtualization overhead, purpose-built for large-scale LLM training and multi-node distributed AI workloads. Reserved and cluster configurations are available for organizations that need dedicated GPU infrastructure with consistent performance for long-running training runs. A specialist bare-metal GPU cloud for AI labs and enterprises that need maximum cluster performance for frontier model training.
Billing model comparison
Oblivus uses a On-demand, Spot billing model with a minimum commitment of None. QuantaCloud uses Reserved / On-demand billing with a Varies by config 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
Oblivus is best suited for: Cost-sensitive training, Budget AI workloads, Startups. Its key strengths are low prices, simple pricing, no hidden fees. QuantaCloud is best suited for: Large-scale LLM training, Multi-node clusters, Reserved GPU capacity. Its key strengths are bare-metal performance, infiniband networking, large cluster configs. 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
Oblivus offers Community → Standard support across 2 regions (EU, US). QuantaCloud offers Standard → Enterprise support across 1 region (US). Oblivus's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: Oblivus vs QuantaCloud
Oblivus was founded in 2022 and is headquartered in Europe. QuantaCloud was founded in 2021 and is headquartered in United States. QuantaCloud has 1 years more operational history than Oblivus, 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.