StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Amazon Rekognition vs TensorFlow

Amazon Rekognition vs TensorFlow

OverviewDecisionsComparisonAlternatives

Overview

TensorFlow
TensorFlow
Stacks3.9K
Followers3.5K
Votes106
GitHub Stars192.3K
Forks74.9K
Amazon Rekognition
Amazon Rekognition
Stacks79
Followers152
Votes4

Amazon Rekognition vs TensorFlow: What are the differences?

Amazon Rekognition and TensorFlow are two powerful tools used in the field of computer vision and machine learning. Here are the key differences between them:

  1. Platform and Vendor: Amazon Rekognition is a cloud-based image and video analysis service provided by Amazon Web Services (AWS), while TensorFlow is an open-source machine learning framework developed by Google. Amazon Rekognition offers a fully managed solution with pre-trained models, whereas TensorFlow provides a more flexible and customizable environment for building and training models.

  2. Use Case and Accessibility: Amazon Rekognition is designed for users who need quick and easy access to pre-trained models for tasks like image and video analysis, facial recognition, and object detection. TensorFlow, on the other hand, caters to developers and researchers who require more control and customization over their machine learning models. It is widely used for training complex neural networks and handling a wide range of machine learning tasks.

  3. Model Training: With Amazon Rekognition, the model training process is abstracted away, and users mainly work with pre-trained models. This makes it suitable for scenarios where model training is not the primary focus. TensorFlow, being a deep learning framework, provides comprehensive support for model training, fine-tuning, and transfer learning. It empowers developers to build custom models or modify existing ones to suit their specific needs.

  4. Integration and Deployment: Amazon Rekognition is tightly integrated with other AWS services, making it easy to incorporate image analysis capabilities into AWS-based applications. On the other hand, TensorFlow is more versatile in terms of deployment options. It can be deployed on-premises, on the cloud, or even on edge devices, providing more flexibility for various deployment scenarios.

  5. Cost and Pricing: Amazon Rekognition follows a pay-as-you-go pricing model, where users are billed based on their usage of the service. TensorFlow, being open-source, does not have any licensing costs. However, the total cost of using TensorFlow depends on factors such as hardware, cloud infrastructure, and developer expertise needed for model development and deployment.

In summary, Amazon Rekognition is a user-friendly cloud service that offers pre-trained models and simplified image and video analysis, while TensorFlow is a powerful open-source framework that provides more control and flexibility for building and training custom machine learning models.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Advice on TensorFlow, Amazon Rekognition

Adithya
Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k
Comments
Vladyslav
Vladyslav

Sr. Directory of Technology at Shelf

Oct 25, 2019

Decided

AWS Rekognition has an OCR feature but can recognize only up to 50 words per image, which is a deal-breaker for us. (see my tweet).

Also, we discovered fantastic speed and quality improvements in the 4.x versions of Tesseract. Meanwhile, the quality of AWS Rekognition's OCR remains to be mediocre in comparison.

We run Tesseract serverlessly in AWS Lambda via aws-lambda-tesseract library that we made open-source.

53.3k views53.3k
Comments

Detailed Comparison

TensorFlow
TensorFlow
Amazon Rekognition
Amazon Rekognition

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.

Statistics
GitHub Stars
192.3K
GitHub Stars
-
GitHub Forks
74.9K
GitHub Forks
-
Stacks
3.9K
Stacks
79
Followers
3.5K
Followers
152
Votes
106
Votes
4
Pros & Cons
Pros
  • 32
    High Performance
  • 19
    Connect Research and Production
  • 16
    Deep Flexibility
  • 12
    Auto-Differentiation
  • 11
    True Portability
Cons
  • 9
    Hard
  • 6
    Hard to debug
  • 2
    Documentation not very helpful
Pros
  • 4
    Integrate easily with AWS
Cons
  • 1
    AWS
Integrations
JavaScript
JavaScript
No integrations available

What are some alternatives to TensorFlow, Amazon Rekognition?

scikit-learn

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Google Cloud Vision API

Google Cloud Vision API

Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API.

Polyaxon

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase