Gradient Deployments enable a hassle-free, automatic “push to deploy” option for any trained model. These allow ML practitioners to quickly validate “end-to-end” services, from R&D to production.


Deploy any model as a high-performance, low-latency micro-service with a RESTful API. Easily monitor, scale, and version deployments. Deployments take a trained model and expose them as a persistent service at a known, secure URL endpoint.

Current Capabilities

  • Out-of-the-box integration with TensorFlow, ONNX, and TensorRT, as well as Flask for Custom models

  • A variety of GPU & CPU types to deploy on

  • Per second pay-as-you-go billing

  • Multi-instance deployments with automatic load balancing

  • A dedicated, secure endpoint URL per deployment

  • Accessible via the Gradient CLI, Web UI, or API, or from your own custom applications