Outerport, a YC S24 startup, has launched tooling that enables runtime swapping of AI model weights without service interruption. The capability allows operators to deploy updated or alternative models to production instances while maintaining active traffic. The operational value centers on eliminating the downtime penalty currently associated with model updates. Production AI systems today require rolling restarts or blue-green deployments to swap weights, creating latency spikes and service gaps. This creates friction in rapid iteration cycles, particularly for teams running inference at scale or operating under SLA constraints. Hot-swapping removes this tradeoff between deployment velocity and availability. For infrastructure operators, this shifts the cost structure of model updates from a coordination problem (scheduling maintenance windows, managing traffic shifts) to a technical one (managing state consistency across weight transitions). Teams can decouple model improvement cadence from operational maintenance schedules. The secondary effect: reduced operational overhead may accelerate adoption of more frequent model retraining and A/B testing patterns in production, since the deployment friction that currently gates experimentation decreases.