Checklist

Edge & On-Device AI Opportunities Checklist (Interactive)

Interactive checklist to help teams assess whether on-device or edge AI deployment is likely to deliver meaningful benefits (latency, privacy, cost, reliability) while revealing key engineering tradeoffs for updates, monitoring, security, and maintainability.

Interactive Tool

Edge & On-Device AI Checklist

Use this guided checklist to evaluate whether deploying models on-device or at the edge is likely to deliver material value for your product, feature, or fleet. Answer the questions as specifically as you can — saved responses can form the basis of a short decision brief. The checklist covers business needs, latency and connectivity, device constraints, updates & monitoring, privacy & security, cost and scale, and an explicit recommendation field to capture your decision.

Select the reasons you are considering on-device deployment.
Enter the worst-case latency the feature must meet from input to usable result.
Measured round-trip or median latency for equivalent cloud-based inference (if available).
Choose the connectivity pattern your devices experience in the target environment.
Estimate the number of devices that would run the on-device model. Scale affects update, monitoring, and cost tradeoffs.
Battery-operated devices often limit model size, runtime, and telemetry frequency.
If known, provide average or continuous power budget for inference and sensing.
Identify the hardware capabilities your target devices provide.
Consider storage, memory, and distribution constraints when estimating target size.
Choose how models will be updated; consider bandwidth, security, and operational complexity.
How often will you need to retrain and redeploy models to maintain quality?
Monitoring allows you to detect drift, failures, and safety issues — but may conflict with privacy constraints.
Select monitoring approaches you can implement given privacy and bandwidth constraints.
Describe legal, privacy, or operational constraints that limit telemetry or logging.
Examples: HIPAA, GDPR data residency, export controls, or other sector rules.
Summarize specific requirements, consent models, or data minimization needs.
Select security controls required to protect models and updates.
Fallback strategies can reduce on-device complexity but reintroduce latency and privacy exposure.
Include storage, OTA bandwidth, maintenance, and device-side compute amortized costs.
Include per-inference costs, bandwidth, and storage for logs and monitoring.
Rate the expected engineering and operations burden of an edge deployment across hundreds or thousands of devices.
1.0 10.0
Capture the most important operational or business tradeoffs you foresee (updates, rollback, fleet testing, etc.).
Choose the recommendation that best reflects your answers and constraints.
Summarize why you chose the recommendation and list any next steps (pilot, performance testing, legal review).
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