Consider source framework, target OS, hardware acceleration options, binary size limits, and observability requirements. This turns runtime selection into a measurable decision rather than ecosystem preference.

A runtime that nominally supports your platform can still fail due to operator or delegate gaps. Validate compatibility with production-like graphs before committing roadmap timelines.

Define a runtime selection scorecard with platform coverage, operator fit, startup cost, and maintenance burden. Structured scoring reduces tooling churn.

Stabilize model interfaces and export process so successive model updates remain integration-compatible. Keep export configuration under version control alongside model and eval artifacts.

Treat runtime integration as part of application engineering with test harnesses and rollout policies. This reduces surprises when moving from local validation to user-facing mobile environments.

Mobile regressions often come from hidden fallback paths and device-class heterogeneity. Validate behavior across representative device tiers before release.

Build only required operators and model assets to control application size and startup cost. Packaging discipline is essential when mobile release budgets are strict.

ONNX-based flows can simplify multi-platform deployment when interface parity is maintained across targets. A shared validation suite keeps behavior consistent across OS variants.

Track startup time, memory usage, and inference latency by runtime and device class in the same report format. Consistent reporting improves decision clarity.

A model may pass functional tests but still regress latency if key operations fall back to slower paths. Include delegate and fallback visibility in performance test reports.

Prefer architecture or graph adjustments that preserve accelerated paths instead of patching around fallback behavior late. This usually yields better long-term maintainability and predictability.

Bundle runtime and model versions together in release records to avoid compatibility ambiguity during incident response. Enforcing this consistently prevents scope drift between releases.

Promote new model-runtime bundles through canary cohorts before broad rollout to catch device-specific regressions. Rollback plans should be tested, not just documented.

Track inference latency, failure counts, model version distribution, and resource usage at runtime. This telemetry closes the loop between model teams and mobile engineering.

Keep platform-specific runbooks for fallback diagnostics and performance triage as part of release documentation. Review it at each release checkpoint so assumptions remain current.

Frequent pitfalls include unsupported delegates on older devices, binary size creep from broad operator inclusion, and startup regressions after runtime upgrades. These issues can pass small internal tests and fail at scale.

Use tiered device matrices, staged rollout cohorts, and fallback observability in production telemetry. This reduces broad-impact regressions and enables targeted hotfixes.

Confirm operator compatibility, delegate behavior, startup budget, runtime memory budget, and p95 latency on each supported device tier. Include rollback feasibility in every approval decision.

Require production telemetry hooks to be live before broad rollout so regressions can be detected quickly. Monitoring readiness is a deployment requirement, not a post-launch task.