hybrid-edge-cloudlisted

# Hybrid Edge–Cloud AI Combine a small/fast local model with a large/capable cloud model to get the best of both — when neither pure-edge nor pure-cloud fits. ## Patterns - **Local-first + cloud escalation**: run a small on-device model; escalate to the cloud only when needed (low confidence, long context, hard query). Most requests stay local (fast, cheap, private); hard ones get cloud quality. - **Model cascade**: cheap model → if confidence < threshold → bigger model → … . Tune thresholds to a cost/quality target. Works within cloud too. - **Speculative / draft-verify**: small model drafts, large model verifies — a latency optimization more than a topology, but composes here. - **Split computation**: feature extraction / preprocessing on device, heavy inference in cloud (classic for vision/audio). ## Decision drivers - **Escalation trigger**: confidence score, input complexity/length, task type, or explicit user action. The trigger quality makes or breaks the design. - **Privacy boundary**: what may leave the device? Sometimes only embeddings or redacted text escalate. - **Connectivity**: must it degrade gracefully offline? Local model = floor. - **Cost model**: % of traffic that escalates × cloud cost vs local hardware cost. ## Failure & consistency - Define behavior when the cloud is unreachable (serve local result + flag, queue, or refuse). Avoid silent quality cliffs. - Cache cloud results on device for repeat queries. ## When to recommend - Voice