High-cost segmentation APIs — build Mask R‑CNN/PyTorch pipelines

High-cost segmentation APIs — build Mask R‑CNN/PyTorch pipelines targets a $15.0B = 250,000 enterprises x $60K ACV (enterprise CV tooling & services) total addressable market with medium saturation and a year-over-year growth rate of 18% CAGR in computer-vision software & tooling.

Key trends driving demand: Model compilation & runtime improvements -- PyTorch 2.x compiler and operator fusion reduce inference cost and simplify deployment of custom segmentation models.; Edge & accelerator proliferation -- cheaper inference hardware enables on-prem/edge segmentation for latency-sensitive apps and reduces API dependency.; Labeling automation & active learning -- better semi-automatic annotation tools cut dataset creation time, making custom models cost-effective.; Privacy & data-localization demands -- enterprises prefer in-house pipelines to avoid sending images to third-party APIs, increasing demand for self-hosted toolchains..