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Terraform VS Keras

Compare Terraform VS Keras and see what are their differences

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Terraform logo Terraform

Tool for building, changing, and versioning infrastructure safely and efficiently.

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.
  • Terraform Landing page
    Landing page //
    2023-09-24
  • Keras Landing page
    Landing page //
    2023-10-16

Terraform features and specs

  • Infrastructure as Code
    Terraform allows you to define your infrastructure in configuration files that can be versioned and stored in a version control system. This makes it easy to track changes, roll back if necessary, and collaborate with team members.
  • Multi-Cloud Support
    Terraform supports various cloud providers such as AWS, Azure, Google Cloud, and others. This allows you to manage your entire infrastructure using a single tool, regardless of the underlying provider.
  • Immutability
    Terraform promotes immutable infrastructure, meaning once a component is created, it is not modified in place but replaced if changes are needed. This leads to more predictable and stable deployments.
  • State Management
    Terraform maintains the state of your infrastructure, which helps in tracking resource changes over time and making incremental updates. This is crucial for applying changes in a controlled manner.
  • Community and Ecosystem
    Terraform has a large and active community, along with a rich ecosystem of providers and modules. This makes it easier to find support, share solutions, and leverage pre-built components.

Possible disadvantages of Terraform

  • Complex State Management
    While state management is a significant feature, managing state files can become complex and risky. Issues like state file corruption or sharing between team members can lead to challenges.
  • Learning Curve
    Terraform has a steep learning curve for beginners, especially those who are not familiar with infrastructure as code concepts or the HashiCorp Configuration Language (HCL).
  • Partial Updates
    Terraform's plan and apply operations are not atomic, meaning that partial updates can sometimes leave your infrastructure in an inconsistent state if an error occurs during execution.
  • Dependency Management
    Managing dependencies between resources can be challenging in Terraform. Misconfigured dependencies can lead to issues during resource creation, deletion, or updates.
  • Cost Management
    While Terraform is excellent for provisioning resources, it does not have built-in cost management or optimization features. Users need to rely on third-party tools to manage and optimize costs.

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.

Analysis of Terraform

Overall verdict

  • Overall, Terraform is considered a robust and effective tool for infrastructure automation. It’s ideal for organizations seeking to streamline their deployment processes, ensure consistency across environments, and automate the lifecycle of their resources. Its flexibility and provider ecosystem make it a valuable asset for teams working in multi-cloud or hybrid environments.

Why this product is good

  • Terraform, developed by HashiCorp, is widely regarded as an excellent tool for infrastructure as code (IaC) due to its ability to provision and manage infrastructure across multiple cloud providers. It offers a consistent CLI workflow, and its HCL (HashiCorp Configuration Language) is powerful yet simple, allowing users to define complex infrastructure configurations in a human-readable format. Terraform’s state management, modules, and community support further contribute to its strengths, enabling efficient resource management and scalability.

Recommended for

    Terraform is particularly recommended for DevOps teams, infrastructure engineers, and IT professionals looking to implement infrastructure as code practices. It's also suitable for organizations aiming to adopt DevOps methodologies, enhance their cloud infrastructure management, or manage complex infrastructure at scale. Additionally, teams operating in multi-cloud environments or those looking to automate infrastructure changes can greatly benefit from using Terraform.

Analysis of Keras

Overall verdict

  • Keras is a solid choice for deep learning projects, offering simplicity and flexibility without sacrificing performance. It is well-suited for educational purposes, research, and even deploying models in production environments.

Why this product is good

  • Keras is widely regarded as a good deep learning library because it provides a user-friendly API that allows for easy and fast prototyping of neural networks. It is built on top of other libraries like TensorFlow, making it robust and efficient for both beginners and experienced developers. Its modularity, extensibility, and compatibility with other tools and libraries make it a popular choice for developing deep learning models.

Recommended for

  • Beginners who are new to deep learning
  • Researchers looking for an easy-to-use platform for prototyping models
  • Developers working on projects that require quick experimentation and development
  • Individuals and companies deploying models into production environments

Terraform videos

+ Add

Wampler Terraform | Reverb Tone Report Demo

More videos:

  • Review - MOD PEDAL POWERHOUSE! Wampler TERRAFORM
  • Demo - IT'S FINALLY HERE! | Wampler Terraform Demo | It's as good as you hoped!!!

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

Category Popularity

0-100% (relative to Terraform and Keras)

Terraform

Keras

DevOps Tools
100 100%
DevOps Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Terraform and Keras

Terraform Reviews

Do not use AWS CloudFormation
Terraform, on the other hand, will occupy your shell until the directly-involved AWS service coughs up an error. No additional tooling is required. Terraform will just relay the error message from the affected service indicating what you’ve done wrong.
Source: gswallow.medium.com
Top 5 Ansible Alternatives in 2022: Server Automation Solutions by Alexander Fashakin on the 19th Aug 2021 facebook Linked In Twitter
Although Terraform and Ansible are both server automation tools, there are still a few significant differences between the two. For example, Terraform is declarative while Ansible allows for both procedural configurations and declarative configurations. Also, Ansible works best as a configuration management tool while Terraform leans towards cloud orchestration.
Source: www.servertribe.com
35+ Of The Best CI/CD Tools: Organized By Category
Terraform is compatible with a wide range of Cloud providers, including Azure, VMWare, and AWS. If you’re subscribed to multiple cloud providers, Terraform is a great way to ensure that they have consistent configurations.
Source: www.cloudzero.com
Why we use Terraform and not Chef, Puppet, Ansible, SaltStack, or CloudFormation
Example: Terraform and Ansible. You use Terraform to deploy all the underlying infrastructure, including the network topology (i.e., VPCs, subnets, route tables), data stores (e.g., MySQL, Redis), load balancers, and servers. You then use Ansible to deploy your apps on top of those servers.This is an easy approach to start with, as there is no extra infrastructure to run...
Source: blog.gruntwork.io
Ansible overtakes Chef and Puppet as the top cloud configuration management tool
Breaking these results down year-over-year, use of Ansible grew from 36% in 2018 to 41% in 2019--surpassing Chef, which grew from 36% to 37%, as well as Puppet, which grew from 34% to 37%. Rounding out the list is Terraform, which experienced a jump from 20% to 31%, and Salt, which increased in usage from 13% to 18%.
Source: www.techrepublic.com

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

Social recommendations and mentions

Keras might be a bit more popular than Terraform. We know about 35 links to it since March 2021 and only 32 links to Terraform. 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.

Terraform mentions (32)

  • Scaffolding Serverless Web Application on AWS
    Terraform is an infrastructure as code tool that lets you build, change, and version infrastructure safely and efficiently. Terraform code is in the terraform directory. - Source: dev.to / 11 months ago
  • Integrating Terraform with CI/CD Pipelines
    In recent years, there has been a significant shift towards automation of infrastructure deployment processes. One popular tool that has emerged as a key player in this space is Terraform, an open-source infrastructure as code (IaC) software tool developed by HashiCorp. This article will explore how Terraform can be integrated into continuous integration and delivery (CI/CD) pipelines using GitHub Actions as an... - Source: dev.to / about 1 year ago
  • Deploying Your Outdoor Activities Map with Terraform
    Terraform is an open-source infrastructure-as-code software tool created by HashiCorp. It allows you to define and manage your infrastructure as code, making it easy to provision and manage resources across multiple cloud providers. With Terraform, you can ensure consistent and repeatable deployments, making it an ideal choice for automating your cloud infrastructure. - Source: dev.to / almost 2 years ago
  • Trigger CI using Terraform Cloud
    Continuous Integration(CI) pipelines needs a target infrastructure to which the CI artifacts are deployed. The deployments are handled by CI or we can leverage Continuous Deployment pipelines. Modern day architecture uses automation tools like terraform, ansible to provision the target infrastructure, this type of provisioning is called IaaC. - Source: dev.to / about 2 years ago
  • Using Let's Encrypt with the Puppet Enterprise console
    Had an itch I've been meaning to scratch for a while. I build my Puppet environment using Terraform, which makes it nice and easy to tear things down and rebuild them. That is great, but it does leave me with an issue when it comes to the console SSL certificates. - Source: dev.to / about 2 years ago
View more

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 / about 1 month 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 / 12 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

What are some alternatives?

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

Rancher - Open Source Platform for Running a Private Container Service

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.

Puppet Enterprise - Get started with Puppet Enterprise, or upgrade or expand.

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

Packer - Packer is an open-source software for creating identical machine images from a single source configuration.

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

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