25 Best Generative AI for Software Development Tools & Platforms

Software development has witnessed revolutionary advancements with the integration of Generative AI for software development. These AI Copilots expedite the entire development process by automating repetitive tasks, improving code quality, and reducing errors. Leveraging this technology can enhance productivity, streamline workflows, and establish a sustainable development environment. Are you ready to explore how Generative AI can transform your development processes? Let's delve into the potential of AI Copilots and their role in software development.

What Is Generative AI for Software Development

Generative AI leverages machine learning to generate new and innovative coding solutions and predict coding sequences. This type of AI aims to revolutionize the way developers work by freeing them from the mundane tasks of searching for boilerplate code or brainstorming coding solutions. 

Generative AI tools, such as GitHub Copilot, empower developers to focus on big coding dreams and strategic decision-making rather than worry about the details of how to implement their ideas. By shifting the developer's focus to a higher, more abstract level, generative AI enables increased productivity and creativity in software development.

The Growing Significance of Generative AI in Software Development

Generative AI has become increasingly significant in software development due to its unique ability to improve productivity and creativity in coding workflows. Unlike automation, rules-based logic, and machine learning tools, generative AI introduces a novel approach that allows developers to produce innovative solutions to coding challenges. 

By leveraging machine learning models, generative AI coding tools offer code suggestions and functions in response to natural language prompts and existing code, allowing developers to streamline their coding processes and enhance their overall efficiency. As generative AI technology evolves and becomes more advanced, its growing importance in software development is poised to revolutionize how software is conceptualized, developed, and maintained.

Related Reading

How Generative AI for Software Development Works

Generative AI in coding works by leveraging machine learning models trained on extensive datasets of code. These models understand the structure and syntax of programming languages.

Pre-training the model

Generative AI models are pre-trained on large datasets containing various code examples written in different programming languages. During pre-training, the model learns to predict the next word or token in a code sequence based on the context of the preceding words. This process helps the model to capture syntax, semantics, and patterns inherent in different programming languages.

Understanding context

When provided with a coding prompt or query, the generative AI model processes the input and uses its learned knowledge to understand the context and intent. The model considers relationships between different code elements, such as variables, functions, and control structures, to generate relevant and syntactically correct code.

Code generation

Using learned patterns and contextual understanding, the generative AI model generates code snippets as output. The generated code is based on the input prompt and follows the structure and style of the programming languages in which the model was trained.

Adapting to user feedback

Generative AI models often have mechanisms to improve based on user feedback. Developers can provide feedback on the generated code, helping the model refine its understanding and improve future outputs. This iterative feedback loop enhances the model's ability to generate more accurate and contextually relevant code over time.

Revolutionizing MacOS with AI Typing

Envision an AI that can type anywhere on macOS, with full context of what's on your screen. Consider your email writing itself, your terminal fluent in Bash, your team updates becoming effortless. You don’t need to imagine any of this anymore. You can download Omnipilot’s MacOS AI Copilot for free right now and use AI anywhere on MacOS! 

Try our free AI copilot today — just head to and enter your email, and you’ll be able to download our MacOS app in seconds! Let Omnipilot make your life easier.

12 Impacts of Gen AI on Software Development

1. Increased Productivity

Generative AI is revolutionizing the software development industry by reducing time and increasing productivity. With its ability to automate repetitive tasks, such as creating UIs, tests, and documentation, developers can focus on more complex tasks while generative AI handles the rest. 

As per KPMG, this technology can drastically cut down the time required to develop complex codebases by up to 90%. This not only leads to faster time-to-market for products but also enables companies to optimize their resources while delivering top-notch output.

2. Improved Quality

Generative AI has the potential to enhance the quality of software development by minimizing errors and maximizing precision. One illustration of this is its ability to generate high-level architecture diagrams using a set of inputs or specifications, which helps guarantee that all system components are properly integrated.

Generative AI promises to boost the effectiveness of current static-analysis tools in detecting bugs. Generative AI also offers a solution to the inherent risks associated with manual coding errors and unexpected bugs in software development. With automated processes, generative AI minimizes these risks, ultimately reducing costly downtime and data loss.

3. More Personalized Experiences

Through the application of generative AI, software developers can create a more personalized user experience based on individual user data. This can lead to greater engagement and improved satisfaction with the software applications. By leveraging the capabilities of generative AI, programmers can optimize their products to better serve the unique needs and preferences of their users.

4. New Business Models

Due to the increasing prevalence of generative AI in software development, businesses could potentially explore new models centred around automated code generation and other AI-powered services.

5. Increased Competition

Generative AI has the unique potential to reduce barriers to entry for developers who are working on complex codebases. This technology also opens up new possibilities for companies, enabling them to achieve goals that were previously impossible or financially unfeasible – such as hiring less experienced developers at lower costs. These developments could potentially lead to a more competitive job market for software engineers in the near future.

6. Loss of Creativity

With the increasing prevalence of artificial intelligence in day-to-day tasks, it’s becoming apparent that developers may become too reliant on automated solutions. This could create a potential loss of creativity and a decrease in innovation in software development. As AI takes over mundane tasks, we must ensure that we continue to challenge ourselves and push boundaries in order to maintain innovation and creativity within the field.

7. Lack of Transparency

The opaqueness of generative AI decision-making can present a challenge in ensuring transparency and accountability within the software development process. As AI continues to advance and become more complex, it is important that we prioritize understanding and implementing measures for maintaining visibility into these systems. Only through diligent efforts toward transparency and accountability can we ensure the ethical use of AI technology.

8. Security Risks

The use of generative AI poses some potential security concerns, as it heavily relies on machine learning algorithms that may be vulnerable to malicious attacks or data manipulation if not properly secured or monitored. 

9. Cost and Complexity Concerns

Although generative AI has the potential to streamline software development and bring about financial savings, there are consequential expenses that come with its implementation and upkeep. This is because it requires specialized hardware and software resources, as well as highly skilled personnel with expert knowledge on how these complex systems operate.

10. Limited Customization

Generative AI may not be the optimal solution for software applications that require high customization or cater to specific niches. This limitation could restrict its utility in certain domains and industries.

11. Ethical Concerns

As is the case with any technology, there are ethical implications that come with utilizing generative AI in software development. Issues such as privacy, data collection, and algorithmic bias need to be taken into consideration. There is a growing fear that this technology may even facilitate unethical practices such as hacking and terrorism. 

It’s important to approach these concerns with critical thinking and an awareness of their potential impact on individuals and society as a whole. Ultimately, we must strive for an ethically responsible use of generative AI in order to ensure its benefits are maximized while any negative consequences are minimized.

12. Intellectual Property Issues

It’s important for companies to be aware of the potential legal issues that could arise from using Generative AI technology. Specifically, there is a risk that content generated by this technology may infringe upon existing intellectual property rights. This could lead to costly legal battles and damage to a company’s reputation. It’s crucial for businesses considering the use of Generative AI to fully understand the risks involved and take measures to avoid any potential legal issues.

25 Best Generative AI for Software Development Tools & Platforms

1. Omnipilot

Imagine an AI that can type anywhere on macOS, with full context of what's on your screen. Imagine if, your email wrote itself, your terminal was fluent in Bash, your team updates became effortless. You don’t have to imagine any of this anymore. You can download Omnipilot’s MacOS AI Copilot for free right now and use AI anywhere on MacOS!

Try our free AI copilot today — just head to and enter your email, and you’ll be able to download our MacOS app in seconds! Let Omnipilot make your life easier.

2. ChatGPT

OpenAI’s ChatGPT is the free app that made the world aware of what generative AI could do. While the chatbot might not be the best app for programming tasks specifically, it is still able to generate boilerplate code, translate or refactor lines of code into different languages, and automate rote tasks.

You don’t have to pay (there is a Plus version for $20 per month), install anything, or give it access to your codebase to see if the suggestions are good, or if it can explain what complex functions do. This makes ChatGPT a good starting point when it comes to exploring what a generative AI tool can do, but there are better coding tools out there.

3. Google Gemini (formerly Bard)

Gemini (formerly known as Bard) is Google’s response to ChatGPT. Google developed the transformer architecture that has underpinned the recent boom in LLMs, so it has a huge amount of AI expertise, but it has been slow to leverage that into consumer products.  

As a coding assistant, Gemini can code in 20 programming languages, including Python, Java, C++, and Javascript. It can generate code from your prompts and comments, explain code you paste in, and help you refactor any code you’ve already written.

4. Auto-GPT

Auto-GPT has been described as “ChatGPT on steroids”, and is a tool that attempts to make GPT operate independently by using one GPT instance to oversee additional GPT instances or agents. The top-level instance can be prompted to create a simple web app. It will then break the overall task down into a list of subtasks. Instead of just telling you what those subtasks are, as ChatGPT would, it hands each one off to another GPT instance to perform. 

This allows it to continue to iterate through complex jobs. There are two main ways to use Auto-GPT: You can install the open-source version from the GitHub repo yourself, or you can use the AgentGPT web app.

5. Copilot

GitHub Copilot has been designed to be an AI pair programmer. Unlike ChatGPT, Google Bard, and Auto-GPT, it’s not built using a general-purpose LLM; instead, it uses OpenAI Codex, which was trained on billions of lines of code and explicitly designed to be capable of writing functional code in languages like Python, Javascript, Go, PHP, Ruby, and Swift. 

On top of that, because Copilot has full access to your project, it is able to generate specific code suggestions based on architecture, style conventions, and context. If you ask it to create a function it won’t just give you boilerplate code – it will do it using the variables, classes, and methods you’ve already defined. Right now, Copilot integrates with a small number of popular integrated development environments (IDEs), including Neovim and Visual Studio.

6. Amazon CodeWhisperer

Amazon CodeWhisperer is Amazon’s answer to GitHub Copilot. It was trained on billions of lines of publicly available code, as well as Amazon code. It similarly works with multiple programming languages like Python and Java, integrates with your IDE, and suggests complete functions based on your prompts, comments, and project code. 

The key difference is that CodeWhisperer is optimized for AWS APIs, including the popular EC2, Lambda, and S3 infrastructure types. If you’re trying to build on top of Amazon’s cloud platforms, it can suggest code using the relevant services that meet AWS best practices. While it can also suggest non-AWS code, it’s less likely to get things perfect.

7. Tabnine

Tabnine is another AI coding assistant that uses OpenAI Codex to generate code suggestions. It can auto-complete whole lines of code or full functions, as well as match the style and conventions of your existing codebase. Where Tabnine stands out is with its availability. GitHub Copilot and Amazon CodeWhisperer are only available in a handful of major IDEs, like VS Code, whereas Tabnine is also available in a wider variety of apps, including Sublime, Emacs, Vim, and Android Studio. 

8. CodeWP

WordPress is the most popular content management system (CMS) in the world, powering more than 40% of the world’s websites. If you’re one of the developers who has built a career on top of WordPress or any of its popular plugins like WooCommerce, then CodeWP might be exactly what you’re looking for.

CodeWP has been designed and trained explicitly to generate PHP, Javascript, and jQuery that’s compatible with WordPress, its plugins, and its database. Like most AI coding assistants, it's a relatively new app, so it might not have all the capabilities you’d expect, but it is under active development, with new features rolling out regularly.

9. CodeSquire

CodeSquire is another tool that brings an AI coding assistant to different apps. In this case, it uses a Chrome Extension to bring its AI features to Google Colab, BigQuery, and JupyterLab. Aimed at data scientists, CodeSquires makes it possible to turn comments into code, write structured query language (SQL) requests using natural language prompts, and intelligently complete single lines and generate complex new functions. It doesn’t do anything wildly different from the other tools on this list, but it enables you to use them in apps that aren’t otherwise supported. 

10. What the Diff

What the Diff is designed to help teams review code and create better documentation. What the Diff integrates with your GitHub and GitLab repositories. When someone on your team commits a pull request, it analyzes the difference between the new and old code – and automatically creates a natural language summary. Instead of a pull request comment just saying “bug fixes”, there will be an accurate summary of what code has changed and what it’s done. It’s a great way to keep non-technical team members in the loop, and just make documentation neater for everyone.

As well as improving documentation, What the Diff can also directly refactor code based on a prompt or comment, and create weekly reports summarizing all the pull requests your team has made.

11. AI Query

Not many people enjoy writing database queries, and those are the people AI Query is trying to help. It can turn plain English into SQL queries, as well as translate complex SQL queries back into English. Right now, it supports PostgreSQL, MySQL, MariaDB, and Microsoft SQL Server, though there are plans to support Google BigQuery, Amazon Aurora, MongoDB, and other database engines.

While other apps can also generate SQL queries, AI Query has the advantage of easily allowing you to define your database structure so that the code it generates is specific to your project. If you just need an app to help with SQL, it’s worth a look.

12. DALL-E 2, Stable Diffusion, and Midjourney

It’s not just back-end developers who can get the benefits of the current generative AI boom. Text-to-image tools like DALL-E 2, Stable Diffusion, and Midjourney allow you to generate any image you want from a simple, written prompt to quickly generate app icons, interface elements, and placeholders for any front-end design elements. If you don’t have the time – or the design chops – and need something workable right now, even just as a placeholder, they’re surprisingly effective.

13. Adobe Sensei

It is designed to help designers be more creative. It provides tools like content-aware fill and image manipulation that use generative AI.

14. Microsoft IntelliCode

This tool combines generative AI with code editing tools. This combo enhances developer productivity. It does so by providing intelligent code completion and error detection.

15. Figma’s Auto Layout

This tool helps to arrange UI elements automatically. This feature saves time and improves design consistency for designers.

16. IBM Watson Assistant

This tool uses Gen AI to understand and answer natural language questions. It offers custom help and support.

17. Runway ML

This tool is for artists and developers to try. They use them in various creative projects, such as art, music, and fashion.

18. by Anthropic is a chatbot that has recently been powered by the newest Claude 2.1 model with increased capabilities. Developed by Anthropic, the advanced AI can be used by enterprises to solve true business problems. Its potential can be a valuable asset for deploying artificial intelligence in many operational aspects. is also available over API. This GenAI tool is a powerful alternative to ChatGPT.

19. Amazon Bedrock

Amazon Bedrock by Amazon Web Services (AWS) is a fully managed service with access to models from different AI companies through an API. This GenAI platform also has developer tools that facilitate the utilization of these models. 

Amazon Bedrock can support AI tasks and applications. This generative AI tool offers a single API with access to a choice of advanced foundation models (FMs). It effectively supports security, privacy, and responsible AI practices. In November 2023, AWS announced new model choices were added to Amazon Bedrock and made their Knowledge Bases generally available.

20. Google Vertex AI

Google Vertex AI by Google Cloud offers a wide range of generative AI, including AI solutions, search and conversation, a unified platform, and over 100 foundation models. It’s a generative AI machine-learning platform that combines data engineering and machine learning, making it easier for teams to collaborate and leverage the benefits of Google Cloud for their AI projects.

21. Replit Ghostwriter

Ghostwriter by Replit is another “AI pair programmer” that facilitates writing code. With this GenAI tool, programmers can code more efficiently and effectively. It helps by providing suggestions and code completions in real-time as the code is being created. Ghostwriter, as a GenAI tool supporting at work, can complete, explain, generate, and transform code. It enables a fast transformation and refactoring of large blocks of code. It’s one of the alternatives to GitHub Copilot.

22. DataStax

DataStax offers different AI tools supporting real-time AI-powered applications. Real-time AI solutions are a great source for companies developing applications using Large Language Models (LLMs) and generative AI. 

They allow companies to access the right data at the right time to take advantage of AI-powered applications. DataStax helps organizations understand how AI tools work and how to maximize their use, thanks to the research conducted by the company. Their database platform, including Apache Cassandra, enables companies to manage data effectively across many data centers and clouds.

23. Snyk

Snyk is a platform for developers that allows for securing applications while coding. It helps find and improve security weaknesses. The platform offers many tools and solutions that help secure the code, avoid vulnerable dependencies, reduce risk for the business and many more. This GenAI platform helps build secure code from the beginning.

24. AlphaCode

AlphaCode is an advanced AI-powered coding assistant that could greatly enhance the capabilities and efficiency of developers by automating several aspects of the coding process. Using machine learning models trained on vast amounts of code, AlphaCode can provide real-time suggestions and corrections, significantly speeding up the development cycle and reducing errors.

25. Duet AI

Duet AI is a collaborative AI tool for developers and development teams. It allows developers from the same team to synchronize with multiple coding environments. This would be particularly useful in large projects where maintaining a common coding standard and architecture is crucial for the project’s success. 

Duet AI can integrate with project management tools to help track progress and predict project timelines based on coding speed and complexity. By doing so, Duet AI can not only improve individual productivity but also enhance team coordination and project management efficiency, making it an indispensable tool for developers looking to optimize both their personal output and team synergy.

Related Reading

4 Best Practices for Effective Use of Generative AI in Software Development


1. Collaborative Development Approach 

One of the most critical best practices for leveraging generative AI in software development is adopting a collaborative development approach that promotes interdisciplinary collaboration and communication among team members. Generative AI projects often require expertise from multiple domains, including machine learning, software engineering, data science, and domain-specific knowledge. 

By fostering collaboration and cross-functional teamwork, developers can leverage the diverse perspectives and expertise of team members to tackle complex challenges, identify innovative solutions, and ensure the successful integration of generative AI into the development process.

2. Continuous Monitoring and Evaluation 

Another critical best practice for effectively using generative AI in software development is implementing robust monitoring and evaluation mechanisms to assess model performance, detect issues, and iteratively improve the quality of generated outputs. 

Generative AI models are inherently probabilistic and subject to uncertainties and variations, making continuous monitoring and evaluation essential for ensuring their reliability and effectiveness in real-world applications.

3. Incorporating Human Oversight 

While generative AI has the potential to automate many aspects of software development, it's essential to incorporate human oversight and intervention to ensure the accuracy, fairness, and ethical integrity of generated outputs. Human oversight plays a crucial role in verifying the quality and relevance of generated content, identifying errors or biases, and providing corrective feedback to improve model performance.

4. Regular Updates and Maintenance 

Finally, developers should prioritize regular updates and maintenance of generative AI models to ensure their continued effectiveness, relevance, and reliability in evolving software development environments. Generative AI models are not static entities but rather dynamic systems that require ongoing updates and refinements to adapt to changing data distributions, user preferences, and application requirements.

Try Our MacOS AI Copilot for Free Today

Imagine having an AI assistant at your fingertips that automatically generates code for you as you type. This is the future of software development thanks to Generative AI. Omnipilot is here to make your life easier by helping you write code, emails, terminal commands, and more on MacOS. 

Enhancing Software Development with Omnipilot's AI Copilot

Omnipilot's MacOS AI Copilot is a game-changer for developers, allowing you to create code with just a few keystrokes. This tool uses the power of Generative AI to understand the context of your tasks and provide you with the most relevant suggestions. The AI copilot is like having an expert coder sitting next to you, ready to help you at every step of your development process.

Streamlining Workflow with Omnipilot's AI Assistance

By downloading Omnipilot, you can revolutionize your software development workflow. The AI copilot can help you write emails, update team members, and execute terminal commands with ease. This tool is not just a simple autocomplete feature; it's a sophisticated AI assistant that understands your needs and provides the most appropriate suggestions to help you complete your tasks more efficiently.

Seamless Integration: Downloading Omnipilot for MacOS

Don't miss this opportunity to try Omnipilot's MacOS AI Copilot for free. Head to, enter your email, and download the MacOS app within seconds. Let Omnipilot transform the way you work and make your software development tasks a breeze.

Related Reading

  • Copilot Alternatives
  • Best AI coding Assistant
  • Tabnine Alternative
  • Copilot Vs Code
  • Codegpt Vscode
  • Replit Ghostwriter Vs Copilot
  • Sourcegraph Alternatives
  • Best AI code Generator
  • Openai Codex Alternative