What is scaling PaddleOCR to zero with KEDA?

It's autoscaling GPU workers for OCR tasks — they start on queue messages, shut down to zero replicas when idle, slashing costs via Kubernetes Event-Driven Autoscaling.

How does multi-cloud GPU pipeline work for OCR?

AWS Lambda validates/uploads files, queues to Azure; KEDA spins T4 Container Apps with PaddleOCR only as needed, outputs JSON to Blob.

Is KEDA autoscaling reliable for production OCR?

Yes for bursty workloads, but test cold starts and egress fees — great for dev tools, risky for mission-critical without monitoring.

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They Scaled PaddleOCR GPUs to Literal Zero Across AWS and Azure—Here's the Catch

GPUs guzzle cash—$2.50 an hour for an NVIDIA T4, idle 90% of the time in typical setups. This multi-cloud hack with PaddleOCR and KEDA flips that script, scaling workers to zero when quiet.

theAIcatchup Apr 10, 2026 4 min read

Diagram of AWS-Azure multi-cloud pipeline scaling PaddleOCR GPUs to zero with KEDA

⚡ Key Takeaways

Scale-to-zero GPUs via KEDA eliminate idle costs, processing PDFs in seconds. 𝕏
Multi-cloud (AWS + Azure) mixes strengths but adds debugging complexity. 𝕏
Ready-made API from Silverlining.cloud lets devs skip the ops nightmare. 𝕏

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#KEDA #OCR API #OCR pipeline #PaddleOCR #multi-cloud GPU #scale-to-zero

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Originally reported by dev.to

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