TensorDock vs Shadeform: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for TensorDock and Shadeform. Updated July 2026.
Provider Overview
Strengths & Best For
TensorDock offers H100, A100, RTX 4090, and RTX 3090 GPU instances across a distributed network of data centers at some of the most competitive on-demand and spot GPU rental prices available. Both on-demand and spot options are available, making it a popular budget AI training platform for cost-sensitive teams and researchers. A practical choice for LLM fine-tuning and batch inference workloads where price-per-GPU-hour is the primary concern.
- Very low prices
- Wide GPU variety
- Spot instances
- Global locations
Shadeform is a GPU cloud aggregator that provisions H100, A100, RTX 4090, and many other GPU types across 30+ underlying cloud providers through a single unified API, automatically routing to the cheapest available instance matching your requirements. On-demand and spot GPU rental options are surfaced from the entire provider network, giving teams multi-cloud flexibility without managing multiple accounts. The fastest way to find and launch the lowest-cost GPU for any AI training or inference workload.
- 30+ provider network
- Single API
- Automatic cheapest-price routing
- Wide GPU selection
Live GPU Pricing
Region Coverage
Popular Comparisons
TensorDock — specialist provider
TensorDock offers H100, A100, RTX 4090, and RTX 3090 GPU instances across a distributed network of data centers at some of the most competitive on-demand and spot GPU rental prices available. Both on-demand and spot options are available, making it a popular budget AI training platform for cost-sensitive teams and researchers. A practical choice for LLM fine-tuning and batch inference workloads where price-per-GPU-hour is the primary concern.
Shadeform — marketplace provider
Shadeform is a GPU cloud aggregator that provisions H100, A100, RTX 4090, and many other GPU types across 30+ underlying cloud providers through a single unified API, automatically routing to the cheapest available instance matching your requirements. On-demand and spot GPU rental options are surfaced from the entire provider network, giving teams multi-cloud flexibility without managing multiple accounts. The fastest way to find and launch the lowest-cost GPU for any AI training or inference workload.
Billing model comparison
TensorDock uses a On-demand, Spot billing model with a minimum commitment of None. Shadeform uses On-demand, Spot 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
TensorDock is best suited for: Budget ML training, Batch inference, Cost-sensitive teams. Its key strengths are very low prices, wide gpu variety, spot instances. Shadeform is best suited for: Teams wanting multi-cloud flexibility, Cost-optimized provisioning, Spot-tolerant workloads. Its key strengths are 30+ provider network, single api, automatic cheapest-price routing. Marketplace providers aggregate GPU supply from multiple sources, often offering the lowest spot rates but with more variable availability and less predictable performance compared to dedicated providers.
Support tiers and region coverage
TensorDock offers Community → Pro support across 4 regions (US, EU, APAC and 1 more). Shadeform offers Community → Enterprise support across 4 regions (US, EU, APAC and 1 more). Both providers have comparable region coverage — choose based on which specific regions overlap with your user base or data residency requirements.
Provider background: TensorDock vs Shadeform
TensorDock was founded in 2020 and is headquartered in Boston, MA. Shadeform was founded in 2023 and is headquartered in San Francisco, CA. TensorDock has 3 years more operational history than Shadeform, 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.