10 Best AI Coding Assistant Tools in 2024 – Guide for Developers

AI tools for coding are becoming more and more popular, revolutionizing the software development industry. They can generate code and automate tedious tasks, providing intelligent code snippets and suggestions that reduce development time. By integrating these tools into your workflow, you can focus on creative problem-solving, resulting in the faster delivery of customer projects. So, if you’re looking to improve your development process, enhance code quality, and stay up-to-date with the latest technological advancements, you’ve come to the right place!

My name is Karol, and I’ve been a mobile developer since 2011 (you can find out more about me in the bio below). I use AI code generation every day, mostly through GitHub Copilot and Cursor. I finished the AI Devs 2 course in November 2023, which covered generative AI, programming AI assistants, and even more exciting topics. Now I’m taking a AI Devs 3 which covers more autonomous solutions like AI agents.

I wanted to share some popular AI tools with you that I think you’ll find really valuable. We’ll go over why they’re so great to use in your workflow, the key features they offer, and some important risks to keep in mind. Plus, I’ll give you some insights into how they integrate with popular IDEs for different programming languages. 

What is an AI coding assistant?

An AI coding assistant is a sophisticated software tool that leverages artificial intelligence to support developers throughout the coding process. Its primary purpose is to:

• help in writing and debugging code
• assist in code reviews
• provide real-time code suggestions, corrections, auto-completions, and optimizations
• detect bugs and security vulnerabilities to improve the code quality 

It works much like a virtual pair programmer. What’s more, it never tires and continuously learns from vast repositories of code. Thanks to the latter, it improves its quality over time.

AI coding assistants target a broad spectrum of users. Novice programmers can benefit from guidance and learning opportunities. Seasoned veterans, on the other hand, can speed up their work by ensuring their source code follows the best practices and latest standards. These tools can adapt to various coding styles and project requirements.

Is it possible to develop digital products with AI tools?

The development of digital products with AI coding tools is not only possible, but very common in the modern tech landscape. AI powered code assistants can significantly enhance various stages of the software development lifecycle, from conceptual design to deployment. Alongside automating repetitive tasks, they can also provide code suggestions. Through this assistance, AI code completion tools contribute to the development process but it’s important to note that the creative and strategic decision-making aspects still rely on human developers. AI coding is not yet at a point where it can independently manage the nuanced requirements of crafting a complete digital product. 

Therefore, AI tools serve as powerful adjuncts to human expertise. They streamline the development process, enabling developers to focus on the more intricate and innovative aspects of product creation.

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Why you should consider using an AI coding assistant

Software development is growing more complex and demanding. Incorporating AI coding assistants into your workflow can be a game-changer. Here are the most compelling reasons why you should consider using AI coding assistants to enhance your programming endeavors:

• Time-to-market reduction: By accelerating the coding process, AI coding assistants can help bring products to market faster, providing a competitive edge.
• Cost savings: Automation can reduce the need for extensive manual labor. This can lead to significant cost savings in the long run.
• Accessibility for non-experts: AI coding assistants can make app development more accessible to those with less experience. They can bridge the gap between novice and expert developers.
• Scalability: AI tools can easily scale with the project as they learn from the growing codebase. This supports projects as they increase in size and complexity.
• Continuous improvement: Because they’re trained with a lot of data, the suggestions and insights offered by AI code tools become more sophisticated and valuable.
• Stress reduction: AI coding assistants can reduce the mental load on developers by handling many of the mundane aspects of coding. This frees the latter to tackle coding challenges with a clearer mind.
• Increased efficiency: AI tools can automate routine or repetitive coding tasks, such as boilerplate code, enabling developers to focus on more complex and creative aspects of their projects.
• Enhanced code quality: With real-time suggestions and the ability to flag potential errors, AI powered code assistants help maintain high coding standards and reduce the likelihood of bugs.
• Learning and growth: Developers can learn from AI-driven recommendations. They often reference best practices and newer, more efficient ways of coding.
• Faster debugging: AI coding assistants can detect issues and offer solutions, speeding up the debugging process significantly.
• Code refactoring: When it comes to improving structure and readability, AI tools can refactor code without changing the external behavior of the codebase.
• Personalized assistance: By adapting to individual coding styles and preferences, AI coding assistants provide individually tailored support that improves over time.
• Integration with development environments: Many AI coding assistants can be integrated into popular IDEs, streamlining the software development process and workflows.
• Image processing: Some AI coding assistants support images in prompts, allowing developers to get answers as quickly as possible.

By using AI coding assistants, developers can improve their current practices. They can also future-proof their skills and projects. These are some of the biggest reasons why the industry as a whole is quickly shifting towards smarter, automated code generation tools.

The 10 best AI coding assistant tools

In crafting this guide to the best AI coding assistant tools, I have selected GitHub Copilot, Tabnine, Replit AI, Amazon CodeWhisperer, IntelliCode, Aider, Cursor, Gemini in Android Studio, SQLAI, and DeepCode AI.

I’ve chosen these 10 tools through extensive research, comparing various options, and determining their potential benefits for developers. In addition to their features, I considered their popularity and widespread adoption within the developer community. Their order reflects their popularity as per Google Trends.

It’s important to note that the popularity of AI powered code assistant tools can fluctuate rapidly. As new advancements are made, developers’ preferences can change. Moreover, each AI code tool has its strengths and may be more or less suited to specific tasks or programming languages. When choosing an AI coding assistant, consider the tool’s compatibility with your preferred programming languages, as well as its ease of integration into your existing workflows.

GitHub Copilot

GitHub Copilot is a code completion tool born from a collaboration between GitHub, OpenAI, and Microsoft. It uses advanced AI to offer coding assistance, understanding the context in which you are writing code. Trained on a vast array of code from open-source projects on GitHub, Copilot enhances the development process by providing smart code completions and explanations directly within the IDE.

According to research, developers using Copilot experience a significant boost in job satisfaction – reportedly 75% higher. They also report up to 55% faster coding. While this increases their productivity, it does not compromise on code quality. It is compatible with major editors like Visual Studio Code, JetBrains IDEs, and Neovim. Moreover, it is uniquely integrated into GitHub’s ecosystem. Copilot is rapidly becoming the go-to AI code completion tool for millions of users and businesses.

While it supports most of the programming languages present in public repositories, the quality of its suggestions varies. Languages like JavaScript receive more robust support due to their prevalence in training data. Copilot is accessible via multiple IDE extensions, GitHub CLI, and will soon be incorporated directly into GitHub’s web and mobile platforms.

Key features:

• High popularity and a large community.
• Integration with many IDEs (Visual Studio Code, IntelliJ products, Neovim).
• Intelligent code completion and easy navigation through multiple code snippets.
• Support for multiple programming languages.
• Code referencing, finding public code that matches AI code generator suggestions. Currently in a public beta, and only currently available in Visual Studio Code.
• Able to block code suggestions matching public code snippets. This is important for avoiding accidental open-source license violations.

Pricing:

• Business – $19 per user per month.
• Enterprise – $39 per user per month.
• Individual – $10 per month or $100 per year. Free for maintainers of popular open source projects, as well as verified students and teachers.

Business and Enterprise plans differ from the Individual plan mostly through administrative features like IP indemnity, license and policy management. It is a discretion of GitHub to decide whether an open-source project qualifies for a free Individual plan or not. As a personal example, I was eligible due to my android-gif-drawable library with 9.5k stars on GitHub.

Tabnine

Tabnine is a trusted AI coding assistant designed with a strong emphasis on privacy and security. It is trained on a vast array of existing code using permissive open-source licenses, mitigating any concerns regarding privacy, security, and compliance. Handily, this also covers copyleft issues. This adherence to best practices is further reinforced by its SOC-2 compliance. Tabnine offers the capability to reflect an organization’s collective knowledge. It tailors its assistance to your specific codebase, aligning with your team’s coding style and best practices without exposing sensitive code.

Tabnine operates in a fully isolated mode. It runs within your chosen environment – be it a secured SaaS setup, on-premises, or on your VPC. This ensures that your intellectual property is always under your control. Tabnine only learns from your code if you opt to connect your codebase. Even then, your code never leaves your environment, guaranteeing complete privacy. It’s a great choice for those who prioritize security.

Furthermore, Tabnine enables centralized policy enforcement. Organizations can standardize the configuration and use of the AI assistant across teams. This centralized approach simplifies management and provides teams with the assurance of full security and privacy control over their coding processes.

Key features:

• High privacy and security level. Only uses code snippets from permitted sources.
• Can be deployed on-premise and on a VPC.
• Automated code documentation.
• Integrations with multiple IDEs (Neovim, IntelliJ products, VS Code, Eclipse and Sublime).

Pricing:

• Basic – free of charge. Only supports basic code completions. Rate limiting.
• Pro – $12 per user, per month, 90 days free of charge. Standard, full-function code completions, including natural language.
• Enterprise – $39 per user per month. Premium support and private deployment.

Amazon CodeWhisperer

Amazon CodeWhisperer is a machine learning-driven code generation tool. It offers real-time coding recommendations tailored to your individual style and current work. As you type out the text, CodeWhisperer presents relevant code suggestions. They can range from simple code snippets to entire functions, depending on the context of your existing code and past inputs. CodeWhisperer can also generate comments and code documentation.

Its standout feature, however, is the ability to predict and complete code blocks or functions as you write. You can additionally can pair it with IDEs like Visual Studio Code or JetBrains products. CodeWhisperer works in conjunction with Amazon CodeGuru, which conducts security scans on your code and related files, proactively identifying potential security issues.

Key features:

• Integration with Amazon’s ecosystem.
• Accurate code suggestions in real time.
• Security scans.

Pricing:

• Individual tier – free of charge.
• Professional tier – $19 per user per month. This includes policy and license management, larger usage limits and does not share your code snippets with Amazon.

Replit AI

Replit AI is a collection of AI code tools designed to elevate the coding experience on Replit’s platform. This suite includes several features, with intelligent code completions being its flagship functionality. Replit can also generate code like the other AI code tools on this list. Finally, it also provides code explanation. It’s worth emphasizing that all of these tools work in unison. They not only streamline the development process but also provide insightful explanations and modifications to the code.

The AI draws from a pool of publicly sourced code, fine-tuned by Replit. It writes code using context-aware suggestions and clarifications. All of this is tailored to the specific language and nuances of your project, providing high quality, and error-free code.

Although Replit AI showcases a strong performance with JavaScript and Python, it extends its capabilities across a spectrum of multiple languages. It includes not only programming languages but also SQL, HTML and CSS. Access to these AI features is freely available to anyone with a Replit account. Only the enhanced functionality, such as unlimited messages and access to a more sophisticated chat model, require a paid plan.

Key features:

• A lot of functionalities available free of charge.
• Proactive code debugging. AI fixes the issues for you without needing to analyze the error messages manually.
• AI chat within the IDE.

Pricing:

• Starter – free of charge
• Core – $20 per month. Unlimited access to AI chat and unlimited private projects.
• Teams – $40 per month. Centralized billing and user management.

IntelliCode

IntelliCode is a tool developed by Microsoft. It is integrated into IDEs: Visual Studio Code and Visual Studio. One of its standout features is its capability to prioritize suggestions based on patterns. Those patterns are sourced from thousands of top-rated GitHub repositories.

IntelliCode can detect the code repetitions and allow to apply the same changes in many places. This tool supports various programming languages. C#, Python, Java and JavaScript among the others. Availability of certain features depend on the programming language and the IDE variant. For instance the whole-line autocompletions are available for C# in Visual Studio 2022 and for Python in Visual Studio Code.

Key features:

• Context-aware code completions.
• Whole-line code completions.
• Repeated edits.

Pricing:

• Free – free of charge. But some Visual Studio IDE variants are paid.

Aider

Aider is an AI pair programming tool that operates within the terminal. It integrates with your local Git repository, allowing you to collaborate with LLMs to edit code. However, it does not include any LLM out of the box. You have to connect one or more external LLMs. The authors recommend Claude 3.5 Sonnet and GPT-4o. But, you can also use some free models like Gemini or even local ones via ollama.

Aider allows you to edit multiple files simultaneously and requesting changes. You may ask to add features, fix bugs, refactor the code, or update documentation. Aider automatically commits changes with sensible messages. Additionally, you can enhance your interaction by adding images, URLs, or even coding with your voice, creating a dynamic pair programming experience with AI.

Key features:

• Ability to connect with any LLM.
• Working in terminal – independence from IDE.

Pricing:

• Free – free of charge. But can be connected to paid LLMs.

Cursor

Source: cursor.sh

Cursor is the code editor dedicated to pair programming with AI. It supports code generation and autocomplete suggestions. There is also a chat to talk with AI taking the currently open file into account. The Cursor can crawl the documentation of third-party libraries.

The Auto-Debug proposes fixes directly in the open terminal window. Similarly, there are quick fixes for issues detected by lint available. The Terminal feature turns the English language into proper commands. Cursor supports images in the prompt.

Key features:

• Pair programming with AI.
• Ability to use images in prompts.

Pricing:

• Hobby – Free of charge, limited number of interactions.
• Pro – $20 per month, unlimited completions.
• Business – $40 per user, per month, team billing, administration, privacy mode.

Gemini in Android Studio

Gemini serves as an AI-powered assistant within Android Studio. It is designed to boost productivity through its capability to interpret and respond to development inquiries in natural language. This enables users to pose questions in simple English and receive assistance with code generation and resource location. It can also point developers towards best practices, saving their valuable time and frustration.

Gemini may occasionally deliver confident yet inaccurate or incomplete advice. It’s crucial to scrutinize and rigorously test the code it suggests. The generated code may not meet the expected outcomes or maintain quality standards.

Incorporating AI-driven code completion, Gemini predicts and displays potential code continuation. It expedits the coding process by proposing entire functions. This AI code completion, when activated, may transmit snippets of the code and other relevant details to enhance the context understood by the underlying language model, ensuring more pertinent code suggestions.

Key features:

• Developed by Google, and built-in into Android Studio – an official IDE for developing native Android and Flutter apps.
• Requires logging in to Google Account.

Pricing:

• Free.

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SQLAI

SQLAI is a tool that generates queries for SQL and NoSQL databases. The platform delivers immediate results and offers a personal library to store and share custom SQL and NoSQL snippets. It boasts effortless 1-click integrations with popular databases such as MySQL, Postgres, Oracle, SQL Server, and MongoDB. Yet it also supports manual connections, including CSV imports, for broader compatibility.

By simplifying the process of obtaining real-time data insights, users can run AI-generated queries directly on their connected data sources, with results presented in tables or visualized through AI-crafted charts. This tool is designed to make the acquisition and display of data intelligence as straightforward as possible.

Key features:

• Tool for SQL and NoSQL databases, not for programming languages.
• Focus on quick results.

Pricing:

• Starter – $7 per month. Low usage limits.
• Pro – $14 per month. Medium usage limits, with real-time data analytics.
• Advanced – $21 per month. Highest usage limits, real-time data analytics, and saveable data dashboards.

Snyk powered by DeepCode AI

DeepCode AI is a component of the Snyk SAST platform. It is equipped with multiple AI models, each informed by an extensive security-focused dataset and the expertise of leading security researchers. DeepCode AI is engineered to identify and remediate security vulnerabilities as well as manage technical debt efficiently.

This code assistant uses open-source projects to train the AI — with a strict policy of not using customer data. Unlike single-model systems such as ChatGPT, DeepCode AI adopts a hybrid model approach, ensuring an unparalleled level of scanning precision. Developers have the capability to craft custom queries using DeepCode AI’s logic.

Key features:

• Integration with the Snyk ecosystem.
• Focus on security checks.

Pricing:

DeepCode cannot be purchased separately. Pricing applies to the entire Snyk service.

• Free. Limited number of tests,
• Team – $25 per month. Unlimited tests, license compliance.

Programming with AI tools – pros and cons

As you can see, we’ve got a ton of AI tools to choose from as developers, and they’re getting better all the time. Artificial Intelligence assistants are a real asset for speeding up our work, handling repetitive tasks and suggesting code snippets. This frees us up to focus on the more complex and creative aspects of development. They’re also pretty good at spotting bugs and keeping our code clean and up to standards.

However, it’s not all perfect. They can struggle with complex problems, leading to code that doesn’t quite fit our project needs. What’s more, integration with our existing workflows can be a pain, and debugging AI-generated code can be tricky if we’re not fully in tune with the generated snippets. Also, for teams in highly regulated industries, making sure AI-generated code complies with all regulations is an extra challenge.

AI-powered app development at Droids On Roids

Most of developers at Droids On Roids use GitHub Copilot. Its intelligent code completions, based on context, enable us to write code faster. We adhere to laws and licenses, so we have disabled suggestions that match public code.

In our development practice, GitHub Copilot is a supportive ally, not a sole authority. It assists in streamlining our processes, but the responsibility for building robust, maintainable, and high quality code rests firmly on our shoulders. We use AI to enhance our skills, not to replace the nuanced judgment and creative problem-solving that only experienced developers can provide.

What I appreciate about this tool is that it not only streamlines my coding process by suggesting accurate snippets of code but also helps me debug and optimize the code in real time, significantly reducing development time.

Read also: Native vs. Cross-platform App Development

Wrap-up: Ship software faster with AI tools, but do it smartly

In conclusion, using coding assistants like GitHub Copilot, Tabnine, Replit AI or others can improve your software development. These tools offer an array of benefits. By generating code for developers, they can increase productivity, as well as enhance learning opportunities, improve the quality of the project code, and accelerate development cycles. You should approach these tools with a balanced perspective, recognizing their potential as supplements, not replacements, for human expertise and ingenuity.

Integrating the best AI coding assistants into your workflow can lead to significant advantages. However, you must approach their adoption strategically. By doing so, you stand to gain more benefits than losses. This ensures that the AI serves to bolster your development process rather than undermine it.

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