> For the complete documentation index, see [llms.txt](https://machine-learning.paperspace.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://machine-learning.paperspace.com/wiki/rest-and-grpc.md). # REST and gRPC Most model-serving frameworks are based on REST. TensorFlow Serving and TensorRT offer gRPC endpoints which are fussier but more performant. ## Benefits of REST * **Stateless -** No client context is stored on the server between requests * **Self-contained -** All information that is needed to service a request is packaged with the request itself * **Flexible -** REST is programming language agnostic, has universal browser and language support, and supports a large number of filetypes ## Benefits of gRPC * **Bi-directional** **-** gRCP supports two-way communication * **Simplicity -** No headers, methods, or body, and better status codes * **Performant -** Binary data via protocol buffers for serializing structure data, performs better under high loads --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://machine-learning.paperspace.com/wiki/rest-and-grpc.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.