Artificial Intelligence
Multi-Model AI Architectures
Routing between models by cost, latency and capability.
Use models as components
Different models offer different balances of quality, latency, cost, context and modality. A multi-model system can route work to the smallest capable option while reserving stronger models for difficult or high-impact tasks.
The architecture should begin with task classes and service requirements, not a catalogue of providers.
Route with evidence
Use deterministic rules where the distinction is clear and measured classifiers where it is not. Record why a route was chosen and evaluate routing quality separately from model quality.
Normalise input, output and error contracts so the application is not tightly coupled to provider-specific behaviour. Keep provider capabilities visible rather than hiding every difference behind a false universal interface.
Plan failure and governance
Define fallbacks for outages, rate limits and safety failures. A fallback should not silently reduce a required safety or quality level.
Track cost, latency, evaluation scores and incidents by route and model version. Permissions, retention and data-location requirements must remain intact when traffic moves between providers.