Scaling PaddleOCR to Zero: A Multi-Cloud GPU Pipeline with KEDA

Running GPU-intensive tasks like OCR can get expensive quickly. If you leave a GPU server running 24/7, you pay for idle time. If you use a standard CPU, processing multi-page PDFs takes forever. We built a document analysis API that solves this by splitting the workload across AWS and Azure, using a "scale-to-zero" architecture. Here is the technical breakdown. The Architecture The system follows an asynchronous worker pattern to ensure the API stays responsive even when processing 100-page documents. 1. The Entry Point (AWS) We use AWS Lambda as our API gateway. It handles the "light" work: Validation : Checking file signatures (hex headers) to verify if the file is a real PDF/JPG. Storage : Saving the raw file to Amazon S3. State Management : Creating a job record in Amazon DynamoDB. 2. The Bridge (Azure Queue) Once the file is safe in S3, the Lambda sends a Base64-encoded message to Azure Queue Storage. This acts as our buffer. 3. The GPU Worker (Azure Container Apps) This is where