[D] Docker Containers (Python) vs Direct C++ Implementation for AI on Edge Devices, Performance Tradeoffs

I'm exploring the best approaches for deploying AI products to production, especially on edge devices like smart home systems and cameras. I'm wondering about the tradeoffs between using Docker containers, python code versus implementing Inference code directly in C++ for these scenarios.

Key considerations:

1. Performance: Does the overhead of Docker significantly impact AI model performance on resource-constrained edge devices?
2. Hardware utilization: Can C++ implementations better leverage specific hardware features for AI acceleration?

I'd love to hear from those with experience deploying AI models to edge devices. What approach have you found most effective, and why? Are there specific use cases where one method clearly outperforms the other?