Effortlessly create AI services using our intuitive UI—select your model, allocate deployment resources, and define the serving method (real-time, API, batch serving) for a streamlined deployment process.
Set up REST API endpoints for model deployment without heavy lines of code to configure a separate environment. Connect with data sources, deploy models in real-time, and integrate inference results into various applications for streamlined AI service implementation.
Optimal Deployment Strategy
Enhance model performance through dynamic deployment strategies (Canary, Shadow, etc.) for newly deployed models. Select the final model based on results and leverage advanced features, such as log management, to build and optimize AI services within Runway.
Unified Model Deployment and Management
Serve and govern external models from open source platforms like Weights & Biases and MLFlow within Runway.
Autoscaling Resource Type
Streamline resource management by implementing autoscaling for API deployments, reducing both labor and compute resource costs.
