Software Alternatives, Accelerators & Startups
Register | Login

Backendless VS TensorFlow

Compare Backendless VS TensorFlow and see what are their differences

KeywordSearch
Supercharge your Ad audiences with AI featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Backendless logo Backendless

Backendless is a mobile Backend as a Service (mBaaS) platform.

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.
  • Backendless Landing page
    Landing page //
    2023-07-20
  • TensorFlow Landing page
    Landing page //
    2023-06-19

Backendless features and specs

  • Codeless Development
    Backendless offers a 'Codeless' feature, which allows users to build backend logic without writing any code. This is particularly beneficial for those who are not familiar with complex coding languages.
  • Real-Time Database
    The platform provides real-time data synchronization, allowing applications to update data instantly across all clients. This is essential for interactive applications such as chat apps and real-time data feeds.
  • API Services
    Backendless allows the creation of REST and SOAP APIs effortlessly. This makes it easier to integrate with other services and provides a clear pathway for extending app functionality.
  • User Management
    The platform comes with built-in user management features such as registration, login, password recovery, and social logins. This helps in reducing the effort required to implement user authentication and authorization.
  • Mobile and Web App Support
    Backendless supports both mobile (iOS/Android) and web applications, offering SDKs for multiple platforms which streamlines the development process.

Possible disadvantages of Backendless

  • Pricing
    Although Backendless offers a free tier, many features and higher usage levels are locked behind a paywall. This may be prohibitive for startups or small projects with limited budgets.
  • Learning Curve
    Even though Backendless offers codeless development, mastering the platform as a whole can be challenging for beginners. There are many features and settings that require some time to understand fully.
  • Vendor Lock-In
    Relying too much on Backendless-specific features can create difficulties if you decide to migrate to another backend service in the future. The migration process can be complex and time-consuming.
  • Limited Customization
    While Backendless offers many out-of-the-box features, there can be limitations in terms of customizing the backend behavior in comparison to building a custom backend.
  • Community and Support
    The community around Backendless is smaller compared to more established backend solutions like Firebase. This can make finding community support, third-party plugins, or comprehensive tutorials harder.

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.

Backendless videos

+ Add

Backendless 5 Release Overview (webinar)

More videos:

  • Review - Functionality Visibility Control in Backendless Console
  • Review - Backendless version 3.0 Overview

TensorFlow videos

+ Add

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)

Category Popularity

0-100% (relative to Backendless and TensorFlow)

Backendless

TensorFlow

Developer Tools
100 100%
Developer Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Databases
100 100%
Databases
0% 0
AI
0 0%
AI
100% 100

User comments

Share your experience with using Backendless and TensorFlow. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

Backendless Reviews

2023 Firebase Alternatives: Top 10 Open-Source & Free
There are three comprehensive plans of this BaaS vendor: Backendless Cloud, Pro and Managed. But it only opened the pricing details of Backendless Cloud in this regard. Here are the key components of Backendless Cloud pricing:
Source: digitalcruch.com
Firebase Alternatives – Top 10 Competitors
Backendless is a highly scalable mobile Backend-as-a-Service (mBaaS) platform providing gazillion of features, including user authentication, live audio and video streaming, message filtering, push notifications, auto-scalability, data persistence, file storage, geo-location, cloud-code, analytics, and custom business logic. It has it all what you need to build awesome...
Source: blog.back4app.com

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.
Source: www.labellerr.com
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 François 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 Deepmind’s Acme framework is implemented in TensorFlow. OpenAI’s Baselines model repository is also implemented in TensorFlow, although OpenAI’s Gym can be...
Source: www.assemblyai.com

Social recommendations and mentions

Based on our record, Backendless should be more popular than TensorFlow. It has been mentiond 21 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.

Backendless mentions (21)

  • Ap Developer
    Go here: https://backendless.com/ . If that don't work for you, Let me know and I'll tell you what next to do. Source: about 2 years ago
  • Join the Free Database Training Course From Backendless
    This article first appeared on https://backendless.com. - Source: dev.to / over 2 years ago
  • free-for.dev
    Backendless.com — Mobile and Web Baas, with 1 GB file storage free, push notifications 50000/month, and 1000 data objects in table. - Source: dev.to / over 2 years ago
  • How Much Does Custom Software Development Cost?
    Luckily, instead of building the backend from scratch, some backend Application Programming Interfaces (APIs) are available. Consider the following options: REST API, Firebase, Backendless, and JHipster. Using APIs is a great way to adopt a functional backend with lower custom software development pricing. - Source: dev.to / over 2 years ago
  • Urgent: Low code / No Code App Builders
    The best no-code/low-code platform for building both the frontend and backend in one place is Backendless. They have the best backend features and a really solid UI Builder that gives you pretty much all capabilities you'll likely need. Source: almost 3 years ago
View more

TensorFlow mentions (7)

  • 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 2 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: almost 3 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 3 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: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 3 years ago
View more

What are some alternatives?

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

Datomic - The fully transactional, cloud-ready, distributed database

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

MarkLogic Server - MarkLogic Server is a multi-model database that has both NoSQL and trusted enterprise data management capabilities.

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

Valentina Server - Valentina Server is 3 in 1: Valentina DB Server / SQLite Server / Report Server

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Loading...