GCP's image and video AI is built around three services — Cloud Vision API (image analysis),Video Intelligence API (video analysis), and Vertex AI Vision (custom models + edge). This article summarizes the features of each, when to use which, and how they compare to AWS and Azure.
| Feature | Use case |
|---|---|
| Label Detection | Object and concept labeling |
| Object Detection / Localization | Bounding box detection |
| OCR (Text Detection) | Text extraction (Japanese supported) |
| Document Text Detection | Document OCR (Document AI recommended) |
| Face Detection | Face position, emotion, attributes (not identification) |
| Landmark Detection | Recognizes famous landmarks worldwide |
| Logo Detection | Corporate logo recognition |
| SafeSearch | Unsafe content detection |
| Web Detection | Similar image web search |
| Image Properties | Dominant colors / crop hints |
| Product Search | Product image search |
from google.cloud import vision
client = vision.ImageAnnotatorClient()
image = vision.Image()
image.source.image_uri = "gs://my-bucket/photo.jpg"
# 複数機能を 1 リクエストで
features = [
vision.Feature(type_=vision.Feature.Type.LABEL_DETECTION, max_results=10),
vision.Feature(type_=vision.Feature.Type.TEXT_DETECTION),
vision.Feature(type_=vision.Feature.Type.SAFE_SEARCH_DETECTION),
]
request = vision.AnnotateImageRequest(image=image, features=features)
response = client.annotate_image(request=request)
for label in response.label_annotations:
print(f"{label.description}: {label.score:.2f}")
print("OCR:", response.text_annotations[0].description)
print("Adult:", response.safe_search_annotation.adult.name)| Service | Price |
|---|---|
| Vision API (Label / OCR) | $1.50/1000 unit |
| Vision API free tier | 1,000 units / feature / month |
| Video Intelligence Label | $0.10 / minute |
| Video Intelligence Speech | $0.048 / minute |
| Vertex AI Vision (custom training) | $3.15/h |
| Vertex AI Vision Stream | $0.30/h (per stream) |
| Item | Vision AI | AWS Rekognition | Azure Computer Vision |
|---|---|---|---|
| OCR (Japanese) | Excellent | Good (English-centric) | Good |
| Face identification | — | Excellent | Good |
| Video analysis | Video Intelligence | Video Analysis | Video Indexer |
| Custom training | AutoML Vision / Vertex | Custom Labels | Custom Vision |
| Pricing (Label) | $1.50/1k | $1/1k | $1/1k |
What is Vision AI / Cloud Vision API?
A pre-trained API that detects objects, faces, text, logos, landmarks, unsafe content, and more from images. You can combine multiple features in a single request.
What can the Video Intelligence API do?
Extracts shot changes, object tracking, text, logos, explicit content, person detection, and speech transcription from video. CDN integration enables large-scale video processing.
How does Vertex AI Vision differ?
Vision API is a pre-trained API; Vertex AI Vision adds custom model training and IoT deployment. Use Vertex AI Vision for real-time monitoring and edge inference.
How does it relate to Imagen?
Vision API analyzes existing images; Imagen generates images from text. They are different products but composable (e.g., generate a product image with Imagen, then run Vision API for quality checks).
What is the pricing model?
Vision API: $1.50 per 1,000 units (per feature), with 1,000 units free per month. Video Intelligence: $0.10 per minute for Label Detection, with prices that vary by feature.
How does it compare to AWS Rekognition and Azure Computer Vision?
All three offer roughly equivalent features. Vision AI has the edge for Japanese OCR accuracy, Web Detection, and SafeSearch. Rekognition is stronger for face recognition.
What can you do with AutoML Vision?
Train industry-specific image classification and object detection models with no code. Useful for custom categories the standard Vision API cannot detect (e.g., your own product catalog).
Is edge deployment possible?
Yes. Vertex AI Vision can deploy models to IoT edge devices, and AutoML Vision Edge produces lightweight models (TensorFlow Lite, Core ML, TPU).
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Note: Google Cloud is a trademark of Google LLC. For the latest information, see the official Cloud Vision documentation.
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