Outerport enables real-time model weight swapping during inference without pausing serving or dropping requests. The YC S24 startup addresses a core operational constraint in production ML: transitioning between model versions currently requires service restarts, canary deployments, or maintaining parallel infrastructure. The capability matters because it collapses the operational friction between experimentation and production. Teams running A/B tests, fine-tuning variants, or deploying corrected weights face a false choice between deployment latency and testing velocity. Hot-swapping eliminates this tradeoff, reducing time-to-validation and lowering the infrastructure overhead of multi-model serving strategies. For operators, this reduces deployment complexity in workflows requiring frequent model iteration. Canary deployment patterns become cheaper to execute. More critically, it enables real-time weight patching without queue purging or connection resets—valuable for systems where inference interruption carries measurable cost. The operational gain is most acute for high-traffic serving where traditional blue-green deployments create unnecessary latency windows.