Google Cloud Vision API vs MediaPipe
Google Cloud Vision API
cloud.google.comMediaPipe
developers.google.comSide by side
| Figure | Google Cloud Vision API | MediaPipe |
|---|---|---|
| Price from | $0.60/mo | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 4 | 2 |
| Spec score · AI Image Recognition Software | 9.2 (#1) | 8.6 (#8) |
| Company founding | Gartner lists Google's year founded as 1998. | Google's official history says Google Inc. was established in August 1998. |
| Company headquarters | LinkedIn lists Google's headquarters in Mountain View, California. | LinkedIn lists Google's headquarters in Mountain View, California. |
| Company size | LinkedIn lists Google as a public software-development company with 10,001+ employees. | LinkedIn lists Google as a public software-development company with 10,001+ employees. |
| Documentation depth | Documentation includes setup guides, quickstarts, OCR and handwriting guides, REST/RPC references, quotas, languages, samples and release notes. | The official site provides task guides, platform setup guides, API references, examples, framework concepts and build-from-source material. |
| Support | Google Cloud Standard Support offers unlimited individual access to technical support and billing support through multiple channels. | Google directs technical questions to Stack Overflow and bug reports or feature requests to GitHub issues, which are limited to community-benefiting problems. |
| What it does | Cloud Vision API provides REST and RPC interfaces for adding pretrained image-analysis capabilities to applications. | MediaPipe supplies customizable machine-learning libraries and tools for deploying AI features in applications across development platforms. |
Plans
Google Cloud Vision API
- Cloud Vision API pay-as-you-goFree
- First 1,000 unitsFree
- Units above 5,000,000$0.60 a month
- Units 1,001 to 5,000,000$1.50 a month
In their own words
Google Cloud Vision API
Google Cloud Vision API provides image-analysis features that developers can integrate into applications through REST or RPC. Its capabilities include image labeling, face and landmark detection, text recognition, object localization, web detection, and explicit-content tagging. Text Detection reads text in images, while Document Text Detection supports dense text, handwriting, and PDF or TIFF files. Object Localization returns labels and bounding boxes for multiple recognized objects. Web Detection can return related web entities, matching image URLs and pages, visually similar images, and a best-guess label. Face Detection locates faces and facial landmarks and supplies likelihood ratings, but it does not recognize specific individuals. Documented workflows connect it with Cloud Storage, Cloud Functions, Pub/Sub, and Cloud Translation API. For Android and iOS development using Google Cloud Vision services, Google recommends ML Kit for Firebase. Limits include 20 MB per image, 10 MB per JSON request, 16 images per synchronous annotate request, and 2,000 images per asynchronous batch request. The first 1,000 units per feature each month are free; subsequent charges vary by feature and usage tier. The listed plan price is free, with Label Detection listed at 1.50 USD per 1,000 units for 1,001–5,000,000 units.
MediaPipe
MediaPipe gives developers libraries, tools, ready-to-run models and customizable APIs for adding AI and machine learning to applications. Its vision tasks include face and hand landmarks, gesture recognition, image classification and segmentation, object detection, and pose landmarks. Text tasks cover language detection, classification, embeddings, proofreading and summarization, with audio classification also available. MediaPipe Tasks supports Android, Web/JavaScript, Python and iOS; its cross-platform APIs are described as requiring as few as five lines of code for inference. The optimized on-device pipelines use CPU, GPU and TPU acceleration for real-time use cases. Developers can try web samples in MediaPipe Studio with text, webcam input or uploaded images. Model Maker supports transfer learning for compatible models, but it is deprecated and cannot change a model's original task. Solution API inputs are processed on-device and not sent to Google; usage and performance metrics are sent to Google. The framework can be built from source for Linux, macOS, Windows and Docker, with some configurations marked experimental.