Here are strong AI-powered image-recognition options startups use today, grouped by type with short notes and a citation for each. Pick tools based on how fast you need to ship, whether you need custom models, on‑device/edge inference, and data-labeling/MLOps support.

Managed cloud vision APIs (fast to integrate)

• Google Cloud Vision — broad feature set (labeling, OCR, object localization), pay‑as‑you‑go pricing and free tier for small volumes. (cloud.google.com)
• AWS Rekognition — image/video analysis plus face‑liveness and identity workflows (good if you’re on AWS). (aws.amazon.com)
• Azure Custom Vision — easy UI for training custom image classifiers and exporting models for edge or cloud. (azure.microsoft.com)

End‑to‑end CV platforms (dataset → train → deploy)

• Roboflow — dataset hosting/annotation, auto‑labeling, training pipelines and portable inference for cloud or edge (designed for startups & engineers). (inference.roboflow.com)
• Clarifai — hosted APIs plus custom model training, tagging, embeddings and search capabilities. (docs.clarifai.com)
• Cloudinary (AI Vision) — asset management + auto‑tagging / moderation and integrations with major vision APIs (handy if you also need DAM + transformations). (cloudinary.com)

Model hosting / foundation‑model inference (flexible, state‑of‑the‑art)

• Hugging Face Inference & Inference Endpoints — run community & custom vision models in production (serverless or dedicated endpoints). Good for custom/ML-first teams. (huggingface.co)
• OpenAI (vision-enabled GPT‑4o fine‑tuning) — multimodal inference and fine‑tuning with images (OpenAI documents vision fine‑tuning and low‑shot improvements). Good when you need VQA, visual search and LLM reasoning over images. (openai.com)

Open‑source / self‑hosted & research‑grade models (best for control / on‑prem)

• Ultralytics (YOLOv8/YOLOv9 family) — widely used for fast, accurate object detection and easy exports to many runtimes (good when you need real‑time on given hardware). (github.com)
• Meta’s Segment Anything (SAM & SAM‑2) — state‑of‑the‑art segmentation foundation model, great for interactive segmentation, auto‑labeling and building segmentation pipelines. (github.com)

Edge / on‑device inference (low latency, offline)

• Edge Impulse — build, optimize and deploy vision models to microcontrollers / NPUs with a low/no‑code studio (good for IoT / embedded startups). (businesswire.com)

Labeling, datasets and MLOps (critical for production quality)

• Scale AI — managed high‑quality annotation, Rapid self‑serve labeling and large-scale data pipelines for CV and robotics.