Software Alternatives, Accelerators & Startups

TensorFlow VS AppFollow

Compare TensorFlow VS AppFollow and see what are their differences

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TensorFlow logo TensorFlow

TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

AppFollow logo AppFollow

AppFollow is an integrated solution that makes monitoring, analyzing, and elevating your app's reputation easy.
  • TensorFlow Landing page
    Landing page //
    2023-06-19
  • AppFollow Organic dashboard
    Organic dashboard //
    2024-04-25
  • AppFollow AI replies
    AI replies //
    2024-04-25
  • AppFollow Reply to reviews
    Reply to reviews //
    2024-04-25
  • AppFollow Agent Performance
    Agent Performance //
    2024-04-25
  • AppFollow Semantic analysis
    Semantic analysis //
    2024-04-25

Your app's reputation determines success. Apps with 4+ stars capture 80% of market revenue and get conversion rates that make competitors jealous. We built AppFollow as the reputation management platform that turns user feedback into measurable results.

AppFollow filters reviews for app teams who need to improve their product and increase sales. Better feedback management improves app ratings, better ratings boost conversion rates and trust, which then means more downloads and revenue. This loop is your competitive advantage.

Our AI suite does the heavy work: with its help, you can tag feedback by topic, summarize insights across thousands of reviews, translate languages, generate unique responses that sound human, and assist your team with complex cases. Automate routine replies and flag issues that need human attention.

Get the reporting you need. Executive reports deliver full summaries for leadership with granular analytics showing which keywords generate downloads. Reveal how competitors attract your users, identify which marketing channels work best, set up Slack alerts for critical feedback, and optimize the time your team spends on reputation management.

Track ASO performance and organic visibility. Monitor reviews across all app stores. See what drives rankings and conversion rates.

Major companies trust AppFollow to maintain their competitive edge. Easy Brain, Wargaming, Lazada, G5, Gameloft, Indeed, Standard Bank, and Opera rely on our platform. We integrate with App Store Connect, Google Play Console, Trustpilot, and all major app marketplaces, with platforms like Steam joining the list soon. We also connect with your existing tools like Zendesk and Slack.

Turn user feedback into business advantage.

AppFollow

$ Details
freemium
Release Date
2015 January
Startup details
Country
Finland
City
Helsinki
Founder(s)
Anatoly Sharifulin
Employees
50 - 99

TensorFlow features and specs

  • Comprehensive Ecosystem
    TensorFlow offers a complete ecosystem for end-to-end machine learning, covering everything from data preprocessing, model building, training, and deployment to production.
  • Community and Support
    TensorFlow boasts a large and active community, as well as extensive documentation and tutorials, making it easier for beginners to learn and experts to get help.
  • Flexibility
    TensorFlow supports a wide range of platforms such as CPUs, GPUs, TPUs, mobile devices, and embedded systems, providing flexibility depending on the user's needs.
  • Integrations
    TensorFlow integrates well with other Google products and services, including Google Cloud, facilitating seamless deployment and scaling.
  • Versatility
    TensorFlow can be used for a wide range of applications from simple neural networks to more complex projects, including deep learning and artificial intelligence research.

Possible disadvantages of TensorFlow

  • Complexity
    TensorFlow can be challenging to learn due to its complexity and the steep learning curve, particularly for beginners.
  • Performance Overhead
    Although TensorFlow is powerful, it can sometimes exhibit performance overhead compared to other, lighter frameworks, leading to longer training times.
  • Verbose Syntax
    The code in TensorFlow tends to be more verbose and less intuitive, which can make writing and debugging code more cumbersome relative to other frameworks like PyTorch.
  • Compatibility Issues
    Frequent updates and changes can lead to compatibility issues, requiring significant effort to keep libraries and dependencies up to date.
  • Mobile Deployment
    While TensorFlow supports mobile deployment, it is less optimized for mobile platforms compared to some other specialized frameworks, leading to potential performance drawbacks.

AppFollow features and specs

  • Comprehensive Analytics
    AppFollow provides extensive app performance metrics and detailed analytics, which can help users understand their appโ€™s performance and user reviews in depth.
  • Review Management
    The platform offers robust review management tools, allowing users to monitor, analyze, and respond to user feedback directly from the dashboard, making customer interaction more streamlined.
  • Keyword Tracking
    AppFollow includes keyword tracking features that help users improve their app's visibility by identifying the most effective keywords for their appโ€™s ASO strategy.
  • Competitor Analysis
    Users can track competitors' apps and get insights into their performance and strategies. This helps in making informed decisions to stay ahead in the market.
  • Integrations
    AppFollow supports integration with various tools and platforms like Slack, Zendesk, and others, facilitating smoother workflow and collaboration.

Possible disadvantages of AppFollow

  • Pricing
    The service can be quite expensive, especially for startups and small businesses that might find the cost prohibitive.
  • Complexity
    The platform can be complex to navigate for new users, with a steep learning curve that might require additional time and resources to fully utilize.
  • Customization Limitations
    Some users have noted that there are limitations in customizing reports and dashboards, which might not cater to all specific business needs.
  • Limited Free Plan
    The free plan offers very limited functionalities, which may not be sufficient for users who need more comprehensive features and insights.
  • Support Response Time
    There have been instances where users reported slower response times from customer support, which can be a drawback in time-sensitive situations.

Analysis of AppFollow

Overall verdict

  • Overall, AppFollow is highly regarded, especially by app developers and marketing teams looking for a centralized solution to manage app performance and user feedback. It offers a broad range of tools that cater to various facets of app development and marketing, making it a versatile choice for those in need of detailed analytics and effective review management.

Why this product is good

  • AppFollow is considered a valuable tool for app developers and marketers because it provides comprehensive app tracking, analytics, and review management. It helps users monitor app store performance, gather user feedback, and optimize app visibility with features like keyword tracking and ASO tools. Users appreciate its user-friendly interface and integration capabilities with platforms such as Slack, Zendesk, and others.

Recommended for

  • Mobile app developers
  • Product managers
  • Marketing teams
  • ASO specialists
  • Customer support teams focusing on app feedback

TensorFlow videos

What is Tensorflow? - Learn Tensorflow for Machine Learning and Neural Networks

More videos:

  • Tutorial - TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka
  • Review - TensorFlow in 5 Minutes (tutorial)

AppFollow videos

ASO Tool for Keyword Research (AppFollow Review)

More videos:

  • Review - Intro to AppFollow Review Management Tools
  • Review - AppFollow and Slack Integration for App Review Management

Category Popularity

0-100% (relative to TensorFlow and AppFollow)
Data Science And Machine Learning
App Reviews
0 0%
100% 100
AI
100 100%
0% 0
Analytics
0 0%
100% 100

Questions & Answers

As answered by people managing TensorFlow and AppFollow.

What makes your product unique?

AppFollow's answer:

AppFollow uniquely combines AI-powered review analysis, reply automation, and app store optimization into one platform. We help mobile-first teams understand user feedback at scale and turn ratings and reviews into a real growth lever โ€” not just a support task.

Why should a person choose your product over its competitors?

AppFollow's answer:

AppFollow is built for teams that care about outcomes, not just data. Customers choose us because we:

  • Save time with smart automation

  • Reveal product and UX insights hidden in reviews

  • Help improve ratings faster with data-backed actions

In short: fewer tools, clearer decisions, better ratings.

What's the story behind your product?

AppFollow's answer:

AppFollow started with a simple problem: mobile teams were drowning in user feedback but couldnโ€™t act on it fast enough. What began as a way to track and respond to app store reviews quickly evolved into a full platform helping teams turn user voice into a competitive advantage.

Which are the primary technologies used for building your product?

AppFollow's answer:

AppFollow is built using modern cloud infrastructure and scalable web technologies, with a strong focus on AI/ML for text analysis, automation, and secure data processing. The platform is designed to handle large volumes of app store data reliably and in real time.

Who are some of the biggest customers of your product?

AppFollow's answer:

  • Easy Brain
  • Wargaming
  • Lazada
  • G5
  • Gameloft
  • Indeed
  • Standard Bank
  • Opera

How would you describe the primary audience of your product?

AppFollow's answer:

The platform is built for product managers, growth and ASO marketers, customer experience leaders, and app teams who manage large volumes of user feedback across app stores, regions, and languages. These teams rely on AppFollow to filter signal from noise, identify reputation risks early, and turn user feedback into faster product improvements and measurable business results.

AppFollow is especially valuable for organizations where:

  • A small change in star rating creates outsized financial impact

  • Review volume makes manual analysis impossible

  • Speed matters when bugs, crashes, or UX issues affect ratings

  • Reputation management must scale without increasing headcount

From fast-growing app publishers to international brands managing apps across dozens of markets, AppFollow serves teams that view reputation management as a growth engine, not a support task.

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare TensorFlow and AppFollow

TensorFlow Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by Franรงois Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks such as machine learning, computer vision, and natural language processing. It provides excellent support for deep learning models and is widely used in several industries. TensorFlow offers several pre-trained models for image classification, object detection,...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
There are a couple of notable exceptions to this rule, the most notable being that those in Reinforcement Learning should consider using TensorFlow. TensorFlow has a native Agents library for Reinforcement Learning, and Deepmindโ€™s Acme framework is implemented in TensorFlow. OpenAIโ€™s Baselines model repository is also implemented in TensorFlow, although OpenAIโ€™s Gym can be...

AppFollow Reviews

We have no reviews of AppFollow yet.
Be the first one to post

Social recommendations and mentions

Based on our record, TensorFlow seems to be more popular. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

TensorFlow mentions (8)

  • Why 70% of Americans See AI as a Wealth Inequality Machine: The Developer's Role in Building Fairer Tech
    The open-source movement offers hope here. Projects like Hugging Face are democratizing access to state-of-the-art models, while initiatives like Google's TensorFlow provide powerful frameworks without licensing costs. But even open-source solutions require technical expertise that many lack. - Source: dev.to / 4 months ago
  • Creating Image Frames from Videos for Deep Learning Models
    Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 3 years ago
  • Need help with a Tensorflow function
    So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: about 4 years ago
  • Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
    Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 4 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: over 4 years ago
View more

AppFollow mentions (0)

We have not tracked any mentions of AppFollow yet. Tracking of AppFollow recommendations started around Mar 2021.

What are some alternatives?

When comparing TensorFlow and AppFollow, you can also consider the following products

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

appfigures - Cross-platform app store analytics for all of your mobile apps.

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

AppTweak - The most comprehensive ASO & Apple Search Ads platform to optimize your apps' organic and paid performance in the app stores

IBM Watson Studio - Learn more about Watson Studio. Increase productivity by giving your team a single environment to work with the best of open source and IBM software, to build and deploy an AI solution.

Sensor Tower - Sensor Tower is a platform for app store optimization and app industry intelligence.