Best AI Tools for Software Developers in 2024

In this article, we will explore in great detail some of the best AI Tools for Software Developers that you can use to enhance your coding experience.

Without a doubt, we’re stepping into a new age. This is because of the incredible advancements in artificial intelligence. Now, AI is also assisting in software creation, and the best AI tools for software developers have also entered the market. 

This promises a future of unprecedented possibilities that also necessitates the need to hire AI developers. 

These advancements have empowered companies to handle challenging tasks more efficiently. Thanks to the evolution of machine learning techniques, the top AI tools in 2024 will likely be capable of processing and analyzing more extensive and complex datasets than ever before. 

Explore our list of the top free AI tools to enhance your team's productivity and quickly integrate AI into your business!

Top AI Tools for Software Development

1. Tabnine

With over a million developers worldwide, Tabnine is a trustworthy AI coding system that many AI development companies use. It provides customized code recommendations that significantly increase. 

• Productivity 
• Streamline tedious development processes 
• Provide excellent, industry-standard code

Moreover, as one of the best AI tools for software developers, it integrates seamlessly with a variety of popular Integrated Development Environments (IDEs) such as Visual Studio Code, IntelliJ IDEA, and more. Tabnine leverages deep learning algorithms to provide highly relevant and context-aware code completions. It learns from your and others' codebase to suggest accurate snippets and complete lines of code.

Key features of Tabnine:

• Uses ML models to predict and suggest codes
• Supports for Multiple Languages like Java, C++, and Go
• Offers both local and cloud-based models
• Integrates smoothly with Integrated Development Environments (IDEs)

Pricing and plans:

• Basic - Free
• Pro - $12/month
• Enterprise - $39/month

2. Codium AI 

Codium AI is a great test-writing assistant designed to streamline the software development process. Recognized as one of the top AI tools for software development, Codium AI is a generative AI platform that assists in creating high-quality code more efficiently. It integrates seamlessly as an IDE extension, allowing developers to generate meaningful tests and receive code explanations directly within their coding environment.

Codium AI supports a broad spectrum of programming languages and is particularly noted for its utility in JavaScript development. Its integration capabilities extend to major IDEs like VS Code and IntelliJ, making it a versatile and powerful tool for developers looking to boost productivity and maintain high standards in code integrity.

Key features of Codium AI:

• Generates code snippets automatically
• Provides real-time code suggestions
• Includes built-in syntax and error-checking capabilities
• Seamlessly integrates with popular IDEs like Visual Studio Code

Pricing and plans:

• Developer - Free 
• Teams - $19/month
• Enterprise - Customized pricing

3. Amazon CodeWhisperer

Amazon CodeWhisperer is a machine learning-powered service tool that aids developers by providing code recommendations to improve productivity and efficiency. This best AI tool for software developers, part of the AWS suite, acts much like a pair-programmer by automatically generating code snippets in real-time as the developer types. It supports popular programming languages such as Python, Java, and JavaScript, making it versatile across different development environments.

CodeWhisperer integrates seamlessly into the developer’s workflow through compatible IDEs like AWS Cloud9, IntelliJ IDEA, PyCharm, WebStorm, and Visual Studio Code. It enhances software development by not only suggesting code but also by providing documentation lookup and implementing best practices. Additionally, top AI tool for software development, CodeWhisperer scans for security vulnerabilities, ensuring that the generated code adheres to safety standards.

Key features of Amazon CodeWhisperer:

• Compatible with several popular programming languages
• Integrates with IDEs like Visual Studio Code, IntelliJ IDEA, and AWS Cloud9.
• Automatically generates technical documentation for the code
• Provides built-in code templates and snippets 

Pricing and plans:

• Individuals - Free 
• Paid plans -  $19/month

4. Stepsize AI 

Stepsize AI is recognized among the best AI tools for software developers, specifically designed to enhance codebase management and maintenance. The tool integrates directly into the development environment. Thus allowing teams to effortlessly track technical debt and other codebase issues without disrupting their workflow. Stepsize AI operates seamlessly with popular Integrated Development Environments (IDEs) such as Visual Studio Code to make it accessible and easy to use.

The core functionality of Stepsize AI revolves around its ability to intelligently tag and manage technical debt straight within the code editor. AI developer tools like this use ML algorithms to analyze code in real time, identify potential improvements, and suggest actionable steps to optimize performance and maintainability. 

For developers looking to streamline their code maintenance processes and reduce future troubleshooting, Stepsize AI emerges as among the excelling software development AI tools, integrating cutting-edge AI with practical, everyday coding tasks.

• Integrates directly with popular issue-tracking systems like Jira and GitHub Issues
• Provides code annotation directly in their IDE (Integrated Development Environment)
• Provides quantitative measurements of technical debt
• Offers actionable insights and recommendations

Pricing and plans:

• Basic - Free
• Team - $19/month
• Business - $49/month
• Enterprise - Custom plan 

5. Figstack 

Figstack stands out as a cutting-edge entry when it comes to AI-powered developer tools for software development. It is designed to assist developers in understanding and writing code via integrating with coding environments. Additionally, it provides real-time insights and solutions directly within the IDE (Integrated Development Environment). 

What sets this best AI tool for software developers apart is its ability to instantly interpret and explain complex code snippets. Just highlight a piece of code and Figstack will get you detailed explanations. Meanwhile, alternative coding suggestions and debugging assistance are provided simultaneously. This functionality is particularly valuable for both new and experienced developers working with unfamiliar or legacy code. Additionally, Figstack supports a variety of programming languages while Its intuitive interface and powerful code analysis capabilities make it a must-have for developers.

Key features of Figstack:

• Uses AI to provide instant explanations for any piece of code
• Translates code between different programming languages
• Helps identify errors in code and offers suggestions for fixes
• Automatically generates documentation for existing codebases

Pricing and plans:

• Individual - $10/month

6. Snyk 

Snyk is a prominent security-focused AI tool in software development, specifically engineered to help developers identify and resolve security vulnerabilities within their codebases and dependencies. Snyk integrates directly into the development workflow and operates across popular development platforms and CI pipelines.

One of the key strengths of Snyk is its ability to provide comprehensive vulnerability scanning. As one of the notable AI software development tools, it scans code, open-source libraries, container images, and infrastructure to detect security threats.

For developers looking to maintain high security and compliance standards in their projects, Snyk offers valuable support. It is one of the best AI tools for software developers that prioritizes security, giving developers peace of mind and saving time by automating the more tedious aspects of security protocol implementation.

Key features of Snyk:

• Provides vulnerability scanning and deep intelligence
• Examines dependencies to detect security risks
• Scans container images for vulnerabilities before they are deployed
• Ensures that IaC templates comply with security best practices

Pricing and plans:

• Basic - Free
• Team - $25/month
• Enterprise - Custom Pricing

7. GitHub Copilot

GitHub Copilot is another big name used to enhance the coding experience. It provides context-aware code suggestions directly in the developer's IDE. Powered by OpenAI's Codex model, it has been trained on a vast array of open-source code repositories. GitHub Copilot offers more than just autocomplete functionality. It generates code snippets, whole functions, and even complex algorithms as you type.

Integrated seamlessly with Visual Studio Code, GitHub Copilot operates quietly in the background. It analyzes the code you write. This allows it to offer intelligent suggestions that are tailored to your current project context. Moreover, the GitHub service has access controls and collaborative capabilities, such as:

• Task management
• Repository storage 
• Team administration

As a standout among the best AI tools for software developers, GitHub Copilot enhances productivity. It promotes best practices and modern coding standards. 

Key features of GitHub Copilot:

• Provides Context-Aware Code Suggestions
• Features Broad Language Compatibility
• Generates stubs for functions, classes, and modules
• Suggests inline comments and documentation for your code

Pricing and plans:

• Individual - $10/month

8. Otter.ai 

Otter.ai is a transformative AI tool for software development. It is particularly beneficial for developers engaged in collaborative projects and meetings. As a powerful real-time transcription service, Otter.ai uses advanced speech recognition technology. Simply means that it converts live spoken conversations into written text. This functionality is quite useful during team meetings and brainstorming sessions. 

For developers, Otter.ai simplifies the documentation process. It automatically generates accurate meeting notes. These notes can be easily shared and integrated into project management tools. This ensures that all team members have access to the same information. Moreover, the AI’s ability to understand context is notable. It can also differentiate between speakers. This makes it a standout option among generative AI tools for software development. 

Key features of Otter.ai:

• Provides live transcription services
• Can differentiate between speakers, labeling and distinguishing who said what
• Users can add specific terms, names, or industry jargon.
• Offers robust search functionality

Pricing and plans:

• Basic - Free
• Pro - $16.99/month
• Business - $30/month
• Enterprise - Custom plan

9. OpenAI Codex

OpenAI Codex empowers developers by automating several routine aspects of coding. Built on the GPT-3 model, this best AI tool for software developers has been trained on a wide range of code from publicly available sources like GitHub. This extensive training allows Codex to understand and generate code in over a dozen programming languages. At its core, it is a multitasking system that can perform: 

• Multilingual voice recognition 
• Language identification 
• Speech translation 

However, the real power of OpenAI Codex lies in its ability to generate code from natural language descriptions. It effectively acts as a bridge between human language and programming languages. This capability accelerates the coding process. Not to mention that it also makes coding more accessible to professionals. As one of the most advanced AI tools for software development, Codex helps streamline project workflows. It provides code suggestions, automates bug fixes, and can even write scripts and functions that integrate into larger projects.

Key features of OpenAI Codex:

• Can translate natural language descriptions into executable code
• Supports over a dozen programming languages
• Provides context-aware suggestions and completions
• Can help identify and suggest fixes for bugs

Pricing and plans:

• It is a free AI tool for developers

10. Hugging Face

With the aid of Hugging Face, users can integrate multiple ML frameworks. For instance, PyTorch and TensorFlow are just two ML frameworks with which the transformer library connects. The best AI tool for software developers makes it possible to prototype and implement NLP and ML applications quickly.

It offers a robust platform that supports various machine learning capabilities, especially in training, deploying, and maintaining NLP models. Hugging Face's Transformers library is especially popular among developers for its comprehensive collection of pre-trained models that can be easily integrated into diverse applications to add sophisticated language understanding capabilities.

Key features of Hugging Face:

• Provides a wide range of pre-trained models for various NLP tasks
• Supports integration with deep learning frameworks like TensorFlow and PyTorch
• Offers highly optimized tokenizers capable of handling large datasets efficiently
• Simplifies the process of loading and pre-processing datasets

Pricing and plans:

• Basic - Free
• Pro Account - $9/month
• Enterprise - $20/month

11. TensorFlow 

TensorFlow is one of the most widely recognized AI tools for software development, particularly favored for its powerful capabilities in numerical computation and large-scale machine learning. Developed by Google and available as an open-source platform, TensorFlow enables developers to easily construct and train neural networks with an array of tools and libraries for dataflow programming across a range of tasks. 

This tool is highly adaptable, supporting both desktop and mobile platforms, and can be used for a variety of applications, from simple regression to complex neural networks involving deep learning. The best AI tool for software developers has a flexible architecture that allows developers to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API, making it a highly scalable option for projects of any size.

Key features of TensorFlow:

• Comes with a comprehensive ecosystem
• Provides a repository of pre-trained models
• Integrates with Keras, a high-level neural network API
• supports distributed training across multiple GPUs and TPUs

12. ChatGPT

ChatGPT, developed by OpenAI, is a state-of-the-art language processing AI tool for software development. This powerful tool is based on the GPT (Generative Pre-trained Transformer) architecture. It utilizes deep learning to produce human-like text and is particularly valuable for developers. They seek to integrate advanced conversational capabilities into applications, websites, and other digital products.

For those in the software development field, integrating ChatGPT can significantly enhance application functionality. It makes applications more interactive and user-friendly. Its robust API supports easy integration. This allows developers to harness its capabilities. They do not need extensive expertise in machine learning or natural language processing. Moreover, the best AI tool for software developers also helps developers at the ground level to identify bugs and errors in their code and even suggest corrections or assist in code generation. 

• Excels in understanding and generating human-like text
• Leverages attention mechanisms to process input sequences and generate output
• Can be fine-tuned on specific datasets to cater to particular applications or domains
• Developers can integrate ChatGPT using provided APIs and SDKs

Pricing and plans:

• Basic - Free
• Plus - $20/month
• Team - $30/month
• Enterprise - Custom plan

13. IBM Watson Studio

Watson Studio is a highly versatile tool that offers compatibility with multiple programming languages such as Python, R and Scala. The tool combines advanced data analytics capabilities. Its robust set of features includes visual data exploration, automated predictive analytics and an easy-to-use drag-and-drop model building. 

All these features make IBM Watson Studio an invaluable resource for developers. They aim to leverage AI to drive innovation. They also want to improve application performance. Ensuring data security and compliance with industry standards is essential.

Because of its cognitive and analytical powers, Watson can:

• Understand human speech 
• Digest enormous amounts of data 
• Answer problems that businesses have never been able to

Key features of IBM Watson Studio:

• Provides a complete environment for data preparation, model development, and deployment.
• Supports languages like Python, R, and Scala
• Compatible with TensorFlow, PyTorch, and Keras
• Provides tools for cleansing, shaping, and transforming data

Pricing and plans:

• $1.02/Capacity Unit-Hour

14. Vertex AI

Vertex AI is yet another of the best AI tools for software developers that supports both custom model development and AutoML. This flexibility allows developers of various skill levels to leverage AI capabilities tailored to their specific project needs. Vertex AI integrates seamlessly with other Google services, enhancing its functionality and enabling developers to incorporate robust AI capabilities into their applications efficiently.

Key features include Vertex Vizier for optimizing ML models, Vertex Feature Store for managing data needed for machine learning, and Vertex Experiments to track, compare, and evaluate ML models. For developers looking for a comprehensive, scalable, and integrated approach to AI development, Vertex AI provides powerful tools and a cohesive environment to significantly enhance project outcomes.

Key features of Vertex AI:

• Combines various Google Cloud AI services into a single, cohesive platform
• Manages and automates the ML workflow
• Provides a centralized repository for storing, sharing, and managing ML features
• Supports both batch and real-time feature serving

Pricing and plans:

• Custom pricing

15. Microsoft Azure AI 

Microsoft Azure AI is a robust suite of AI tools for software development designed to accommodate developers at all levels within the Azure cloud platform. It offers a mix of no-code solutions and advanced machine learning capabilities through services like Azure Machine Learning, which simplifies the entire lifecycle of model development. 

Azure Cognitive Services allows developers to integrate AI features such as vision, speech, and language processing easily. Additionally, Azure Bot Service supports the creation of intelligent conversational agents, making it robust, reliable, and the best AI tool for software developers. With support for multiple programming languages and frameworks, Azure AI ensures seamless integration and scalability for cutting-edge, efficient applications.

Key features of Microsoft Azure AI:

• Provides an end-to-end Machine Learning Lifecycle
• Offers APIs for image and video analysis
• Facilitates the creation and deployment of chatbots
• Implements search functionalities with AI capabilities

Pricing and plans:

• Custom pricing

Exploring Some Noteworthy Case Studies of the Best AI Tools for Developers

There are many uses of AI especially when it comes to software development. These case studies below show how some of these AI software has helped reputable companies in a big way. 

Tabnine for LG 

LG Electronics witnessed a 35% increase in code completion productivity and a 25% reduction in coding errors with the help of Tabnine. It helped developers in LG to code much faster and get minimal bugs and errors overtime. Thus, this boost in efficiency helped improve the overall quality of software products developed by LG. (source: Selected AI)

Amazon CodeWhisperer for Accenture

Accenture is a notable example of a company that has effectively utilized Amazon CodeWhisperer to enhance its software development processes. By leveraging CodeWhisperer's capabilities, Accenture has reported a reduction in development effort by up to 30%. The tool has enabled quicker coding, better adherence to best practices, and enhanced security within their projects.

Snyk for REI

According to this Snyk report, the case of REI demonstrates how Snyk facilitated its digital transformation and cloud migration by introducing a Devsecops culture. The company integrated Snyk into its development workflow and was able to improve the security posture of its applications significantly. 

This integration helped REI maintain a robust security standard throughout its transition to cloud-based environments, enhancing both developer productivity and application security.

GitHub Copilot for Harness

One study conducted by Harness SEI showed that integrating GitHub Copilot led to a noticeable improvement in development cycles. Developers using Copilot experienced a 10.6% increase in pull request (PR) activity and a reduction in cycle time by an average of 3.5 hours. This indicates that Copilot not only enhances the efficiency of code generation but also fosters better collaboration among developers.

Another case study involving the company Bench, as reported by Jellyfish, highlighted the real-world impact of GitHub Copilot on engineering workflows. The trial with Bench revealed a 40% decrease in the amount of time engineers spent coding, while an 11% time reduction was observed in writing code per ticket. 

Otter.ai for Aiden Technologies 

Aiden Technologies significantly enhanced its sales team's productivity via Otter.ai, according to their case study. The IT cybersecurity company faced challenges with manual note-taking during sales calls. This was a time-consuming and cumbersome process, especially when revisiting specific discussion points in recorded calls. With the help of Otter.ai, Aiden Technologies managed to streamline the note-taking process and was able to witness a 33% boost in productivity. 

Hugging Face for Fetch

Fetch, a consumer rewards company, utilized Hugging Face on AWS to significantly enhance its AI-powered platform, according to a Hugging Face case study. This led to a 30% reduction in development time. Moreover, the transition also allowed Fetch to implement more robust machine learning models and improve data processing latency by 50%. Thus enhancing customer experience by providing quicker responses. 

TensorFlow for Airbus

As per the TensorFlow case study, Airbus has utilized TensorFlow Extended (TFX) for a project aimed at enhancing the operations of the Columbus module on the International Space Station (ISS). The ISS Analytics project by Airbus aims to automate the detection of anomalies in telemetry data using machine learning. This project handles a vast amount of data—approximately 17,000 unique telemetry parameters are monitored, generating over 5 trillion data points over a decade.

The solution architecture included a lambda architecture with two main components. First was the batch layer, and then the speed layer. The batch layer is responsible for learning from historical data using TFX on a Kubeflow platform, where models are trained using LSTM Autoencoders. 

By deploying this system, Airbus has enhanced its ability to monitor the health of the Columbus module's systems and respond more swiftly and effectively to any anomalies. This use of TFX not only improves the efficiency of the operations team but also increases the overall safety of the ISS's onboard crew.

How to Choose the Right AI Tool for Your Development Needs 

Choosing these best AI tools for software developers might seem easy, but the reality is different. Thus, 

1. Set Goals

Start by clearly defining your project goals. Are you looking for a tool to assist with natural language processing, image recognition, or predictive analytics? Answering these questions would be the first step in the process. Later, set quantifiable targets in line with your company's aims, such as:

• Boosting productivity 
• Cutting expenses
• Raising client satisfaction
• Obtaining a competitive edge 

These objectives will direct your assessment procedure.

2. Project Requirements

AI tools frequently use data for both training and operation. Determine if the tool needs real-time data, a sizable dataset, or a particular format for the data. Consider whether you have the data architecture set up to meet the tool's needs.

3. Analyze the ROI and Costs

Consider the price of purchasing, deploying, and maintaining the AI technology. Estimate the benefits it can offer your company to determine the potential return on investment (ROI). Weigh the advantages and disadvantages of several instruments to make an informed choice.

4. Look for Reviews

Look for case studies, testimonials, or user evaluations from companies already using the AI solutions you are considering. Examine their accomplishments, experiences, and any difficulties they had with implementation. 

5. Make Informed Decision

In the end, read over all the data acquired during the assessment process and select the best AI tools for software developers that best suits your business needs, fulfills your objectives, and provides the most significant return on your investment.

Conclusion

Businesses that achieved greater heights were those that recognized early on the possibilities of artificial intelligence in their operations. AI not only powers the company but also increases its efficiency quickly, especially if you hire remote AI developers.  

Artificial Intelligence (AI) in business might be likened to the introduction of digital processes that alleviated the burden of paper-based processes. Similar to how this revolution brought about a significant change, artificial intelligence (AI) will radically transform all industries.