11. Local-First Strategy

Local-first architecture treats on-premise models as the default inference path. Cloud resources serve as capacity amplifiers during peak demand or capability gaps, not as the primary workhorse. This philosophy reduces operational cost, improves latency for domestic users, and provides consistent availability regardless of external service status.

Implementing local-first requires evaluating model capabilities against task requirements. Not every task needs GPT-4-class capabilities. Classification, extraction, and structured generation tasks often perform adequately with 7B-13B parameter models running on commodity GPU hardware. The gateway's routing logic must classify requests and direct them appropriately.

Local model deployment requires infrastructure planning. A single A100 GPU handles roughly 30-50 concurrent inference requests for a 7B model with batched processing. Larger models reduce concurrency proportionally. Horizontal scaling across multiple inference servers demands load balancing configuration and session affinity considerations for stateful interactions.

class LocalFirstRouter:
    def __init__(self, local_models: dict[str, LocalModel], 
                 cloud_gateway: CloudGateway):
        self.local = local_models
        self.cloud = cloud_gateway
        self.capability_map = self._build_capability_map()
    
    def route(self, request: Request) -> Provider:
        required_capability = self._classify_task(request)
        
        # Check local capacity
        if required_capability in self.capability_map:
            model = self.capability_map[required_capability]
            if model.has_capacity():
                return model
        
        # Fall back to cloud for complex tasks or capacity overflow
        if request.priority == Priority.HIGH:
            return self.cloud.primary
        elif self._all_local_at_capacity():
            return self.cloud.secondary
        
        return None  # Queue locally
    
    def _classify_task(self, request: Request) -> str:
        # Simple heuristic; production would use ML classifier
        if request.max_tokens < 200 and not request.has_context:
            return "classification"
        elif "extract" in request.task_type:
            return "extraction"
        elif request.complexity_score > 0.7:
            return "reasoning"
        return "general"

Local-first demands investment in infrastructure maintenance. Model updates, security patches, and hardware monitoring become operational responsibilities. The cost tradeoff favors local-first when usage exceeds roughly 10 million tokens monthly.