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Keras VS Heroku

Compare Keras VS Heroku and see what are their differences

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Keras logo Keras

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

Heroku logo Heroku

Agile deployment platform for Ruby, Node.js, Clojure, Java, Python, and Scala. Setup takes only minutes and deploys are instant through git. Leave tedious server maintenance to Heroku and focus on your code.
  • Keras Landing page
    Landing page //
    2023-10-16
  • Heroku Landing page
    Landing page //
    2023-10-05

Keras features and specs

  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlow’s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages of Keras

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.

Heroku features and specs

  • Ease of Use
    Heroku offers an extremely user-friendly interface and a high level of abstraction, making it easy for developers to deploy, manage, and scale applications without worrying about the underlying infrastructure.
  • Quick Deployment
    One of Heroku’s strongest points is the ability to deploy applications quickly using Git. Developers can push their code to Heroku with a simple command, streamlining the entire process.
  • Scalability
    Heroku provides effortless scaling options by allowing developers to add more dynos (containers) with a single command to handle increased traffic and workload.
  • Add-Ons Ecosystem
    Heroku offers a rich ecosystem of add-ons, such as databases, caching, monitoring, and more, which can be easily integrated into applications to extend their functionality.
  • Automatic Updates
    Heroku automatically handles operating system and server updates, allowing developers to focus solely on their application code rather than maintenance tasks.
  • Free Tier
    Heroku offers a free tier with sufficient resources to host small projects and learn the platform without incurring costs, making it accessible for beginners and small-scale applications.

Possible disadvantages of Heroku

  • Cost
    While Heroku offers a free tier, the costs can quickly add up for larger applications and professional use. Paid plans and additional dynos or add-ons can become expensive.
  • Performance
    Heroku’s performance can sometimes be suboptimal compared to other cloud providers, particularly when running high-performance or resource-intensive applications.
  • Limited Control
    Heroku abstracts away a lot of infrastructure management, which can be a downside for developers who need fine-grained control over their environments and configurations.
  • Dyno Sleeping
    Applications running on Heroku’s free tier experience 'dyno sleeping,' where the application goes to sleep after 30 minutes of inactivity, causing a delay when it wakes up after receiving a new request.
  • Vendor Lock-In
    Relying heavily on Heroku’s ecosystem and platform-specific features can lead to vendor lock-in, making it challenging to migrate to another platform if needed.
  • Add-On Costs
    The costs for add-ons can also become significant, as many useful features and integrations require paid add-ons, increasing the overall expense.

Keras videos

+ Add

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos:

  • Review - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • Review - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

Heroku videos

+ Add

What is Heroku | Ask a Dev Episode 14

More videos:

Category Popularity

0-100% (relative to Keras and Heroku)

Keras

Heroku

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Cloud Computing
0 0%
Cloud Computing
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Cloud Hosting
0 0%
Cloud Hosting
100% 100

User comments

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Reviews

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

Keras Reviews

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
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...
Source: searchbusinessanalytics.techtarget.com

Heroku Reviews

  1. mark-mercer
    Useful Cloud Platform

    Great service to build, run and manage applications entirely in the cloud!

    🏁 Competitors: Amazon AWS, Dokku on Digital Ocean, Firebase
    👍 Pros:    Easy user interface|Good customer service|Multi-language cloud application platform
    👎 Cons:    Limitation with some addons|Low network performance
  2. jamestelford
    · Full Stack Developer at OutDev ·
    🏁 Competitors: Docker, Amazon AWS
    👍 Pros:    Powerful development environments|Great value for the money|Great customer support|Paas
10 Top Firebase Alternatives to Ignite Your Development in 2024
Heroku’s focus on simplicity and developer experience makes it a perfect fit for those who want to focus on building their apps, not babysitting servers. Startups and small businesses, in particular, can benefit from Heroku’s ability to accelerate development and deployment, allowing them to get their ideas to market faster.
Source: genezio.com
2023 Firebase Alternatives: Top 10 Open-Source & Free
Heroku Postgres – Majority of businesses like Heroku because of its SQL database support. Yes, PostgreSQL as a service is an appealing product of this PaaS vendor with quick deployment approaches.
Source: digitalcruch.com
5 Free Heroku Alternatives with Free Plan for Developers
Koyeb is a decent alternative to Heroku that you can consider for hosting or deploying your web apps and APIs. It has all the features of Heroku that you will need for your projects. So far, I have not encountered an importer tool for migrating Heroku deployments but I am sure doing that manually will not be that hard. Just like Heroku it offers you an intuitive web UI as...
Source: www.ilovefreesoftware.com
Choosing the best Next.js hosting platform
However, there are a few disadvantages to Heroku. First of all, despite its build pack, Heroku will run your project as a Node.js application. As a result, you will lose some of Next.js’ most interesting features, such as Incremental Static Regeneration. Analytics are replaced by metrics and measured throughput, response time, and memory usage (only on paid plans).
Source: blog.logrocket.com
Top 10 Netlify Alternatives
Heroku is another alternative to Netlify that doesn’t only host static websites but has the ability to host dynamic websites. This PaaS platform was launched in 2007 and conferred highly scalable features to deploy, host and launch applications.
Source: blog.back4app.com

Social recommendations and mentions

Based on our record, Heroku should be more popular than Keras. It has been mentiond 73 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.

Keras mentions (35)

  • Top Programming Languages for AI Development in 2025
    The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / 18 days ago
  • Top 8 OpenSource Tools for AI Startups
    If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and running—an essential part of the startup hustle. - Source: dev.to / 7 months ago
  • Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
    At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / 8 months ago
  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 11 months ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 year ago
View more

Heroku mentions (73)

  • How to deploy your web application? 3 different approaches to consider (+1 bonus)
    Providers include Digital Ocean, Heroku or Render for example. - Source: dev.to / 7 months ago
  • Heroku Reviews Apps prevent delivering bugs on production
    Review Apps run the code in any GitHub PR in a complete, disposable Heroku application. Review Apps each have a unique URL you can share. It’s then super easy for anyone to try the new code. - Source: dev.to / 12 months ago
  • How to keep an HTTP connection alive for 9 hours
    The app is deployed to Heroku and when it came time to switch the mode to email-on-account-creation mode, it was a very simple environment change:. - Source: dev.to / over 1 year ago
  • How to Process Scheduled Queue Jobs in Node.js with BullMQ and Redis on Heroku
    Heroku is a cloud platform that makes it easy to deploy and scale web applications. It provides a number of features that make it ideal for deploying background job applications, including:. - Source: dev.to / over 1 year ago
  • I made a Bot.. How do I use it?
    Once you've created it you can host it locally (this means leaving the program running on your computer) or host it through a service online. I haven't personally tried this yet, but I believe you can use a site like heroku.com or other similar services. Source: almost 2 years ago
View more

What are some alternatives?

When comparing Keras and Heroku, you can also consider the following products

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.

DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.

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

Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.

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

Amazon AWS - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.

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