Lambda Labs vs Oblivus: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Lambda Labs and Oblivus. Updated July 2026.
Provider Overview
Strengths & Best For
Lambda Labs offers on-demand and reserved H100, A100, and RTX A6000 GPU instances with simple flat pricing and no egress fees — a refreshing contrast to hyperscaler complexity. Pre-configured PyTorch and TensorFlow environments mean researchers can start LLM training or fine-tuning in minutes without any setup overhead. A go-to on-demand GPU cloud for ML teams that want predictable hourly GPU rental costs without long-term commitments.
- Simple pricing
- Pre-configured ML stack
- No egress fees
- Jupyter notebooks included
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
Live GPU Pricing
Region Coverage
Popular Comparisons
Lambda Labs — specialist provider
Lambda Labs offers on-demand and reserved H100, A100, and RTX A6000 GPU instances with simple flat pricing and no egress fees — a refreshing contrast to hyperscaler complexity. Pre-configured PyTorch and TensorFlow environments mean researchers can start LLM training or fine-tuning in minutes without any setup overhead. A go-to on-demand GPU cloud for ML teams that want predictable hourly GPU rental costs without long-term commitments.
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.
Billing model comparison
Lambda Labs uses a On-demand, Reserved (1yr/3yr) billing model with a minimum commitment of None (on-demand). Oblivus uses On-demand, Spot billing with a None minimum. Lambda Labs's no-commitment on-demand model is more flexible for short-term or experimental workloads, while Oblivus's commitment requirement suits teams with predictable long-running jobs.
Which workloads each provider suits best
Lambda Labs is best suited for: ML researchers, Deep learning training, Teams wanting simplicity. Its key strengths are simple pricing, pre-configured ml stack, no egress fees. Oblivus is best suited for: Cost-sensitive training, Budget AI workloads, Startups. Its key strengths are low prices, simple pricing, no hidden fees. 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
Lambda Labs offers Community → Enterprise support across 5 regions (us-east-1, us-west-1, us-west-3 and 2 more). Oblivus offers Community → Standard support across 2 regions (EU, US). Lambda Labs's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: Lambda Labs vs Oblivus
Lambda Labs was founded in 2012 and is headquartered in San Francisco, CA. Oblivus was founded in 2022 and is headquartered in Europe. Lambda Labs has 10 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.