Roboflow Inference is an open-source inference server that turns any computer or edge device into a command center for computer vision projects. With a single Docker-based install, you can self-host your own fine-tuned models or run leading foundation models, connect cameras and video streams, and serve predictions through a clean API. The project has earned more than 2,400 stars on GitHub and was still actively updated in October 2026.
Deploying vision models usually means stitching together model servers, stream handling, and hardware-specific optimization yourself. Roboflow Inference packages all of that: it supports object detection, classification, and instance segmentation models including YOLO variants, plus foundation models such as Florence-2, CLIP, and SAM2. Its Workflows system provides composable blocks that chain models together with tracking, counting, timing, OCR, and notifications, so a simple detector can grow into a complete visual application. The server runs on everything from cloud GPUs to NVIDIA Jetson devices and Raspberry Pi, and can also connect to Roboflow’s hosted compute when you do not want to manage hardware.
Running Inference on your own hardware is free and does not consume credits. Roboflow’s optional serverless cloud API bills per image and dedicated hosted deployments bill hourly, so the overall offering is freemium.
Roboflow Inference is for computer vision engineers and product teams deploying detection or segmentation models to cameras, factories, retail spaces, or edge devices. It fits teams that want production-grade model serving without building the infrastructure from scratch.