Google Cloud TPU vs AWS GPU in 2026: which is actually cheaper per million tokens?

A 2026 comparison of cloud accelerators argues that Google Cloud TPU Trillium v6e and AWS H100 GPUs have nearly identical on-demand costs for a comparable 70B Llama-class serving workload, at roughly $0.94 and $0.91 per million output tokens respectively, while long-term commitments can reduce Trillium to about $0.42 per million tokens. AWS L40S instances are presented as a particularly economical option for 8B models, reaching about $0.32 per million tokens with a far smaller hourly billing unit, while CUDA compatibility, mature tooling, and flexibility favor AWS GPUs for many teams. TPU economics are strongest for high-utilization, XLA-native workloads with committed capacity, but porting effort and limited support for custom CUDA-dependent models can add engineering costs. AWS Inferentia2, Trainium2, Azure H100, Scaleway, and neutral GPU clouds offer alternatives shaped primarily by model compatibility, regional requirements, capacity, and cloud commitments rather than headline prices. The comparison emphasizes that utilization, batching, quantization, autoscaling, spot capacity, and reserved-use discounts usually affect total cost more than accelerator selection, noting that idle hardware can multiply per-token costs. It also positions Qovery as a Kubernetes platform for operating workloads within customers’ own cloud accounts so that they retain discounts, data residency, and infrastructure control.

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