Serverless GPU Inference Cost Comparison: Roboflow, GCP, AWS, Azure

In the blog post by Erik Kokalj, the cost and functionality of deploying custom vision model inference using the RF-DETR model are compared across various cloud providers such as Roboflow, Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure. The RF-DETR model, optimized for GPU inference, is evaluated in terms of continuous and burst inference scenarios, with each provider offering different pricing structures and capabilities. Roboflow's Serverless Hosted API is highlighted for its cost-effectiveness with sporadic AI workloads, despite occasional unpredictability in inference time. GCP Cloud Run's pricing is based on instance-based billing, while AWS SageMaker and Azure's Serverless GPU options also necessitate always-on instances due to the inability to scale down to zero, resulting in varying hourly costs. The post underscores the importance of selecting a cloud provider based on specific traffic patterns and the trade-offs between cost, cold starts, and managing GPU idle times.

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