Shadeform

Shadeform GPU pricing overview

Shadeform is a marketplace GPU cloud provider headquartered in San Francisco, CA. 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. A fast way to find and launch low-cost GPUs for AI training or inference workloads. Billing is On-demand, Spot with a minimum commitment of None. Available regions include US, EU, APAC, Various. GPU instances can be provisioned on-demand, making Shadeform suitable for both short-duration experiments and sustained production workloads.

Shadeform vs other GPU providers

Shadeform competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: 30+ provider network via single API; Automatic cheapest-price routing; Wide GPU selection across providers. Use the side-by-side comparison tool above to see Shadeform pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 4 Shadeform listings alongside 91+ providers in a single sortable view.

Best use cases for Shadeform

Shadeform is best suited for: Teams wanting multi-cloud GPU flexibility, Cost-optimized GPU provisioning, Spot-tolerant workloads needing lowest price. Support tiers range from Community → Enterprise, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 4 active GPU listings on Shadeform, covering L4, L40, A40, L40S. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

Shadeform billing model and cost structure

Shadeform uses On-demand, Spot pricing. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Shadeform

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Shadeform pricing data is collected

Prices shown are sourced from Shadeform's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are kept in our history archive and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.