Edge AI hardware has never been more capable, yet shipping computer vision and physical AI products still takes months longer than it should. From our work at Ultralytics integrating models with hardware vendor SDKs across many platforms, we’ve learned that the bottleneck is rarely TOPS—it’s SDK friction. We’ll share a practical evaluation checklist that we use in every integration, including export formats, operator coverage, quantization tooling, documentation quality, runtime stability and developer onboarding time. Through three case studies, we’ll show recurring failure modes, such as custom layer rewrites requiring undocumented work-arounds, black box quantization pipelines with unexplained accuracy loss and SDKs locked to obsolete OS versions. We’ll propose an “SDK openness” scorecard (standards compliance, repo activity, issue response, community, docs) and explain why openness is both a technical and commercial advantage. Attendees will leave with concrete criteria to de-risk platform selection and accelerate development.