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
Koyeb is a serverless GPU platform for deploying AI inference endpoints without managing infrastructure, with automatic scaling to zero and pay-per-use billing across EU and US regions. Git-based deployment and a simple dashboard make it easy to ship LLM inference APIs and AI model serving endpoints in minutes. A strong choice for teams that want zero-ops GPU inference with automatic scaling and no idle compute costs.
- Serverless — no infrastructure management
- Automatic scaling to zero
- EU and US regions
- Git-based deployment
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.
Koyeb — specialist provider
Koyeb is a serverless GPU platform for deploying AI inference endpoints without managing infrastructure, with automatic scaling to zero and pay-per-use billing across EU and US regions. Git-based deployment and a simple dashboard make it easy to ship LLM inference APIs and AI model serving endpoints in minutes. A strong choice for teams that want zero-ops GPU inference with automatic scaling and no idle compute costs.
Billing model comparison
AWS uses a On-demand, Reserved (1yr/3yr), Spot billing model with a minimum commitment of None (on-demand). Koyeb uses Pay-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 Koyeb'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. Koyeb is best suited for: Inference API deployments, Serverless AI apps, Teams wanting zero-ops GPU. Its key strengths are serverless — no infrastructure management, automatic scaling to zero, eu and us regions. As a hyperscaler, AWS offers broader ecosystem integration and compliance certifications at a premium price. Koyeb as a specialist provider typically offers lower per-GPU rates for teams that don't need the full hyperscaler ecosystem.
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). Koyeb offers Community → Standard support across 2 regions (EU, US). 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 Koyeb
AWS was founded in 2006 and is headquartered in Seattle, WA. Koyeb was founded in 2021 and is headquartered in Paris, France. AWS has 15 years more operational history than Koyeb, 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.