Google Cloud vs Koyeb: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Google Cloud and Koyeb. Updated July 2026.
Provider Overview
Strengths & Best For
Google Cloud provides A100 and H100 GPU instances via Compute Engine and Vertex AI, with sustained use discounts and committed use contracts that can significantly cut hourly GPU rental costs. TPU v4 and v5 accelerators are also available for TensorFlow and JAX workloads, giving teams a unique alternative to NVIDIA hardware. Spanning 30+ regions, it is the top choice for ML pipelines deeply integrated with the TensorFlow and Google ecosystem.
- Sustained use discounts
- Vertex AI integration
- TPU availability
- Strong networking
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
Google Cloud — hyperscaler provider
Google Cloud provides A100 and H100 GPU instances via Compute Engine and Vertex AI, with sustained use discounts and committed use contracts that can significantly cut hourly GPU rental costs. TPU v4 and v5 accelerators are also available for TensorFlow and JAX workloads, giving teams a unique alternative to NVIDIA hardware. Spanning 30+ regions, it is the top choice for ML pipelines deeply integrated with the TensorFlow and Google ecosystem.
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
Google Cloud uses a On-demand, Committed Use (1yr/3yr), Spot/Preemptible billing model with a minimum commitment of None (on-demand). Koyeb uses Pay-per-use (serverless) billing with a None minimum. Google Cloud'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
Google Cloud is best suited for: ML training pipelines, TensorFlow workloads, Teams using GCP services. Its key strengths are sustained use discounts, vertex ai integration, tpu availability. 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, Google Cloud 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
Google Cloud offers Basic → Premium support across 5 regions (us-central1, us-east4, europe-west4 and 2 more). Koyeb offers Community → Standard support across 2 regions (EU, US). Google Cloud's broader region footprint gives it an advantage for latency-sensitive workloads or teams with data residency requirements in specific geographies.
Provider background: Google Cloud vs Koyeb
Google Cloud was founded in 2008 and is headquartered in Sunnyvale, CA. Koyeb was founded in 2021 and is headquartered in Paris, France. Google Cloud has 13 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.