Beam vs Shadeform: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Beam and Shadeform. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Attribute

Beam

Shadeform

Provider type

Specialist

Marketplace

Founded

2022

2023

Headquarters

New York, NY

San Francisco, CA

Billing model

Per-second usage

On-demand, Spot

Min commitment

None

Support tier

Standard

Community → Enterprise

Regions

1 regions

4 regions

Strengths & Best For

Beam

Beam is a serverless GPU platform that lets developers deploy AI models and run H100, A100, and T4 compute jobs with automatic scaling and per-second pay-per-use billing — no infrastructure management required. A Python-native SDK (`beam deploy`) and fast cold starts make it easy to build and ship LLM inference APIs, batch ML pipelines, and AI model serving endpoints quickly. Beam handles container builds, GPU provisioning, and scaling automatically. Free tier available. A strong choice for Python-first teams that want serverless GPU infrastructure with predictable, usage-based pricing.

Strengths

Python-native SDK — `beam deploy` ships a GPU endpoint in minutes
Auto-scaling with fast cold starts — no idle cost
Per-second billing — pay only for actual compute time
Free tier available for prototyping
Handles container builds and GPU provisioning automatically

Weaknesses

Less control over underlying infrastructure vs raw VMs
Smaller GPU catalog than dedicated cluster providers
Not suitable for multi-node distributed training

Best For

Serverless LLM inference APIsBatch ML processing pipelinesPython-first teams wanting zero-ops GPU infrastructureRapid prototyping with free tier

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Shadeform

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.

Strengths