Provider Overview
Strengths & Best For
AWS offers on-demand, reserved, and spot GPU instances across EC2 P4d (A100), P5 (H100), and G6 (L40S) families, spanning 30+ global regions with enterprise SLAs and deep ML tooling via SageMaker. H100 and A100 clusters are available with InfiniBand networking for distributed LLM training and large-scale AI inference. The broadest ecosystem of any GPU cloud provider, making it the default choice for enterprises already invested in the AWS stack.
- Widest global region coverage
- Deep ecosystem integrations
- Enterprise SLAs
- Reserved instance discounts
Salad leverages a distributed network of consumer GPUs — including RTX 4090 and RTX 3090 — to deliver some of the lowest AI inference prices on the market, making it ideal for batch image generation, LLM inference, and cost-sensitive AI workloads. The marketplace model enables per-use billing with no minimum commitment, dramatically undercutting traditional on-demand GPU cloud pricing for fault-tolerant jobs. Best suited for workloads that can tolerate variable hardware rather than requiring guaranteed uptime.
- Extremely low prices
- Consumer GPU network
- Batch inference focus
- Pay-per-use
Live GPU Pricing
Region Coverage
Popular Comparisons
AWS — hyperscaler provider
AWS offers on-demand, reserved, and spot GPU instances across EC2 P4d (A100), P5 (H100), and G6 (L40S) families, spanning 30+ global regions with enterprise SLAs and deep ML tooling via SageMaker. H100 and A100 clusters are available with InfiniBand networking for distributed LLM training and large-scale AI inference. The broadest ecosystem of any GPU cloud provider, making it the default choice for enterprises already invested in the AWS stack.
Salad — marketplace provider
Salad leverages a distributed network of consumer GPUs — including RTX 4090 and RTX 3090 — to deliver some of the lowest AI inference prices on the market, making it ideal for batch image generation, LLM inference, and cost-sensitive AI workloads. The marketplace model enables per-use billing with no minimum commitment, dramatically undercutting traditional on-demand GPU cloud pricing for fault-tolerant jobs. Best suited for workloads that can tolerate variable hardware rather than requiring guaranteed uptime.
Billing model comparison
AWS uses a On-demand, Reserved (1yr/3yr), Spot billing model with a minimum commitment of None (on-demand). Salad uses Per-use (serverless) billing with a None minimum. AWS's no-commitment on-demand model is more flexible for short-term or experimental workloads, while Salad's commitment requirement suits teams with predictable long-running jobs.
Which workloads each provider suits best
AWS is best suited for: Enterprise workloads, Production ML inference, Teams already on AWS. Its key strengths are widest global region coverage, deep ecosystem integrations, enterprise slas. Salad is best suited for: Budget inference workloads, Image generation pipelines, Cost-sensitive batch jobs. Its key strengths are extremely low prices, consumer gpu network, batch inference focus. 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
AWS offers Basic → Enterprise support across 5 regions (us-east-1, us-west-2, eu-west-1 and 2 more). Salad offers Community → Pro support across 2 regions (US, EU). AWS's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: AWS vs Salad
AWS was founded in 2006 and is headquartered in Seattle, WA. Salad was founded in 2020 and is headquartered in Boston, MA. AWS has 14 years more operational history than Salad, 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.