Oblivus vs Jarvis Labs: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Oblivus and Jarvis Labs. 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
Jarvis Labs is an ML-focused GPU cloud offering H100, A100, and RTX instances with per-second billing, pre-configured environments for PyTorch, TensorFlow, and other popular frameworks, and a simple interface designed for machine learning engineers. On-demand GPU rental with no minimum commitment makes it easy to spin up and tear down instances for training runs, fine-tuning, and AI inference experiments. A popular choice for ML engineers who want pre-built environments and granular per-second billing.
- Per-second billing
- Pre-configured ML environments
- Simple UI
- Fast provisioning
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.
Jarvis Labs — specialist provider
Jarvis Labs is an ML-focused GPU cloud offering H100, A100, and RTX instances with per-second billing, pre-configured environments for PyTorch, TensorFlow, and other popular frameworks, and a simple interface designed for machine learning engineers. On-demand GPU rental with no minimum commitment makes it easy to spin up and tear down instances for training runs, fine-tuning, and AI inference experiments. A popular choice for ML engineers who want pre-built environments and granular per-second billing.
Billing model comparison
Oblivus uses a On-demand, Spot billing model with a minimum commitment of None. Jarvis Labs uses On-demand (per-second) 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
Oblivus is best suited for: Cost-sensitive training, Budget AI workloads, Startups. Its key strengths are low prices, simple pricing, no hidden fees. Jarvis Labs is best suited for: ML engineers, Notebook-based workflows, Teams wanting pre-built environments. Its key strengths are per-second billing, pre-configured ml environments, simple ui. 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). Jarvis Labs offers Community → Pro support across 2 regions (US, EU). Both providers have comparable region coverage — choose based on which specific regions overlap with your user base or data residency requirements.
Provider background: Oblivus vs Jarvis Labs
Oblivus was founded in 2022 and is headquartered in Europe. Jarvis Labs was founded in 2020 and is headquartered in San Francisco, CA. Jarvis Labs has 2 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.