Self-Hosted Computer Vision: Run Your Own Vision Stack with Roboflow Inference

Self-hosted computer vision runs inference on an organization’s own hardware rather than through a hosted API, making it useful when images must remain private, internet connectivity is unreliable, low latency is required, or high-volume API costs become impractical. Production deployments need more than model weights: they require an API-based serving layer, model versioning, hardware support, monitoring, and reliable update processes. The post presents Roboflow Inference as an open-source Docker-based server that can run custom models such as RF-DETR as well as foundation models through a consistent local HTTP API, with the same client code usable for local, hosted, or dedicated deployments by changing the API URL. Its walkthrough covers creating and publishing a Roboflow Workflow, installing Docker and compatible Python tools, launching the local server, and running a workflow against localhost, with models cached so inference can continue offline after initial setup. Hardware selection should reflect workload needs, with CPUs suited to lighter or intermittent processing, NVIDIA GPUs for real-time or foundation-model workloads, Jetson devices for compact edge deployments, and Roboflow AI1 providing an integrated industrial option. While local inference improves privacy, resilience, latency, and cost predictability, it also shifts responsibility for uptime, scaling, patching, and operational monitoring to the deploying team, making hybrid approaches that use cloud tooling for training and model management while retaining local inference a practical option.

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