# Realtime Realtime APIs respond to requests synchronously and autoscale based on in-flight request volumes. Realtime APIs are a good fit for users who want to run stateless containers as a scalable microservice (for example, deploying machine learning models as APIs). **Key features** * respond to requests synchronously * autoscale based on request volume * avoid cold starts * scale to zero * perform rolling updates * automatically recover from failures and spot instance termination * perform A/B tests and canary deployments ## How it works When you deploy a Realtime API, Cortex initializes a pool of worker pods and attaches a proxy sidecar to each of the pods. The proxy is responsible for receiving incoming requests, queueing them (if necessary), and forwarding them to your pod when it is ready. Autoscaling is based on aggregate in-flight request volume, which is published by the proxy sidecars. ![](https://user-images.githubusercontent.com/808475/146854245-ed0fc153-d083-47d8-a7e2-ac5beb114ee6.png) --- # Agent Instructions: 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://docs.cortexlabs.com/workloads/realtime.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.