claude-dev-suite 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 assistants, copilots on laptops/phones, field/IoT devices with intermittent connectivity, privacy-sensitive apps with occasional hard queries.
- If ~all traffic needs the big model → just use cloud serving. If ~none does → go pure edge.