CompreFace vs Restb.ai
CompreFace
github.comRestb.ai
restb.aiSide by side
| Figure | CompreFace | Restb.ai |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 2 | 1 |
| Spec score · AI Image Recognition Software | 8.9 (#2) | 7.6 (#22) |
| Integrations | The project invites contributors to add support for other platforms such as Home Assistant or DreamFactory; it does not describe these as built-in integrations on the page. | Restb.ai lists integrations with Cotality, dynaConnections, Rapattoni, FBS, ICE, Stratus Data Systems, and Remine, and offers APIs for custom integrations. |
| What it does | CompreFace is Exadel’s free, open-source facial-recognition server designed for integration into other systems. | Restb.ai applies computer vision and AI to property imagery to produce actionable visual insights for real estate businesses. |
Plans
In their own words
CompreFace
CompreFace is a free, open-source face recognition service that provides capabilities through a REST API. It supports face recognition and verification, plus face, landmark and mask detection, head pose detection, age recognition and gender recognition. The project says setup and use do not require prior machine-learning skills. It is packaged as a Docker Compose configuration and can run as a standalone server or in the cloud; listed deployment options also include hybrid use. Setup instructions cover Windows, macOS and Linux and require Docker with Docker Compose or Docker Desktop. The service supports CPU and GPU models, with an x86 processor supporting AVX among the stated requirements. A UI panel and role management let users manage roles and control access to face recognition services. The project names FaceNet and InsightFace among its underlying libraries and links JavaScript, Python and .NET SDK repositories. CompreFace is self-hosted and released under Apache 2.0. The documented quick start uses a release archive and starts the service with docker-compose up -d; the project also points users to GitHub channels and a community chat.