How to Choose an AI Coding Tool

Before selecting a tool, take into account these practical aspects:

• IDE Compatibility: Does the tool support your primary editor or IDE, such as VS Code, JetBrains IDEs, Neovim, etc.?
• Codebase Context Size: Is it able to handle large repositories, multiple files, and project-wide symbols?
• Privacy and Security: Are the completions based on your code? Is user data retained? Does it offer on-premise or self-hosted options?
• Language and Framework Support: Does the tool cater to your tech stack effectively, including tests, infrastructure as code, and documentation?
• Agentic Features: Apart from autocomplete, can it perform tasks, refactor code safely, open pull requests, or fix failing tests?
• Pricing and Limits: Free tiers are great for experimentation, but be aware that rate limits and repository size constraints can vary significantly.

The 10 Best AI Coding Tools to Try in 2025

1) GitHub Copilot

GitHub Copilot continues to be the most widely adopted AI pair programmer, seamlessly integrated with GitHub repositories, pull requests, and Actions. It offers in-editor chat, inline completions, and code reasoning features, with enhanced capabilities in Copilot Chat and Copilot Workspace for task management and repository-level context.

• Standout Features: Excellent autocomplete, pull request summarization, test generation, GitHub integrations, and wide-ranging language support. GitHub has also rolled out deeper repository context and planning through Copilot Workspace (announcement).
• Best For: Teams using GitHub, polyglot codebases, and developers seeking a familiar, seamless assistant.
• Pricing: Free trials available; paid per-user plans for individuals, businesses, and enterprises (pricing).

2) Cursor

Cursor is a developer-centric code editor designed around AI. It enhances the traditional editor experience with repository-wide chat, intelligent refactoring, and task-oriented workflows that can modify multiple files with human oversight.

• Standout Features: Smart diffs, multi-file edits, task runners, and robust prompt management within the editor.
• Best For: Developers looking for an editor tailored for AI-driven workflows.
• Pricing: Free and paid plans with higher limits and advanced features (pricing).

3) Codeium

Codeium offers fast autocomplete, chat, and refactoring capabilities across popular IDEs. It focuses on offering low-latency suggestions, broad language support, and enterprise privacy options, with a generous free tier for individual users.

• Standout Features: Inline suggestions, repository-aware chat, and team functionalities. Codeium also supports on-premise or private deployment for enterprises (security).
• Best For: Developers in search of robust autocomplete and chat features without the need to switch editors.
• Pricing: Free for individuals; team and enterprise plans are available (pricing).

4) Tabnine

Tabnine emphasizes privacy in code completion, offering options to run models in a VPC or on-premise for sensitive code environments. It integrates multiple models and provides enterprise governance, auditing, and compliance controls.

• Standout Features: Data residency options, policy controls, and AI completion tailored for enterprise requirements (privacy).
• Best For: Organizations that prioritize data security and need strict data management protocols.
• Pricing: Free and paid tiers; enterprise deployments are available (pricing).

5) Sourcegraph Cody

Cody combines powerful code search with an AI assistant capable of reasoning across large monorepos. It uses retrieval-augmented generation to provide answers grounded in your code, integrating with editors and Sourcegraph’s advanced search UI.

• Standout Features: Accurate code search, repository-level context windows, and task execution capabilities that generate and explain diffs (overview).
• Best For: Large codebases, monorepos, and teams that already utilize Sourcegraph’s search functionality.
• Pricing: Free, Pro, and Enterprise plans (pricing).

6) Amazon Q Developer

Amazon Q Developer is AWS’s AI coding assistant capable of generating code, answering questions about your technology stack, and assisting with upgrades, including guided migrations for Java and .NET. It integrates seamlessly with AWS services and IDEs, having evolved from CodeWhisperer and other AWS AI tools.

• Standout Features: Cloud-aware assistance for AWS projects, code generation, test creation, and security scanning (announcement).
• Best For: Teams primarily building on AWS who require cloud-native support.
• Pricing: Tiered pricing per user with free trial options (pricing).

7) Google Gemini Code Assist

Gemini Code Assist (the successor to Duet AI for Developers) provides code completion, chat, and code modernization support across Google Cloud tools, with integration for various IDEs and Cloud Workstations.

• Standout Features: Guidance for GCP services, code transformation, and robust security and governance in Google Cloud (Google Cloud).
• Best For: Teams developing on GCP who desire a tightly integrated experience with Google Cloud services.
• Pricing: Per-user plans through Google Cloud, with trials and promotions available depending on the account (pricing).

8) JetBrains AI Assistant

JetBrains AI Assistant integrates AI chat, code completion, and refactoring functionalities directly into JetBrains IDEs like IntelliJ IDEA, PyCharm, and WebStorm. It leverages a combination of models (including partner LLMs) along with project context.

• Standout Features: IDE-native refactorings and code actions, automatic documentation generation, and test scaffolding (update).
• Best For: Teams using JetBrains who want AI features integrated into their existing workflow.
• Pricing: Paid add-on with trial options available (pricing).

9) Replit AI

Replit AI (formerly known as Ghostwriter) is a cloud IDE featuring an AI assistant for code generation, explanations, and in-editor debugging. It’s particularly useful for rapid prototyping and educational purposes.

• Standout Features: One-click environment setup, in-editor AI chat, and easy sharing/deployment directly from the browser (docs).
• Best For: Quick prototyping, teaching, hackathons, and learning new programming languages.
• Pricing: Free tier available with credits; paid plans provide increased compute and AI usage (pricing).

10) Continue.dev (Open Source)

Continue is an open-source extension for VS Code and JetBrains that transforms your editor into a chat-focused AI assistant. You have the option to self-host models or connect to providers like OpenAI, Anthropic, and more, giving teams power over data accessibility and costs.

• Standout Features: Bring-your-own-model capabilities, local or self-hosted configurations, and customizable system prompts (docs).
• Best For: Developers who desire the flexibility of open source, local models, or enhanced control over their AI tooling.
• Pricing: Fully open-source and free; enterprise support options available through partners or community.

Privacy, IP, and Responsible Use

For professional development teams, privacy and intellectual property are crucial. Always review your vendor’s policies to understand what data is recorded, retained, or utilized for training models, and whether any code snippets are stored. Many enterprise plans are designed to support data residency, single sign-on (SSO), audit trails, and private deployments. If you’re unsure, opting for a bring-your-own-model or self-hosting option and establishing clear team protocols is advisable.

Tips for Maximizing AI Coding Tools

• Prime with Context: Provide relevant files, tests, stack information, and constraints upfront.
• Work in Small Loops: Request diffs or small patches, carefully review them, and then iterate.
• Write Clear Prompts: Clearly specify intent, constraints, and acceptance criteria. Include examples whenever possible.
• Ground Answers: Utilize tools that maintain repository-level context or retrieval, ensuring outputs reference your code.
• Keep Humans in the Loop: Treat AI as a collaborator. Conduct tests, linting, and security scans as you usually would.

Bottom Line

The landscape of AI coding tools is rapidly evolving, but finding the right one will depend on matching your editor with your tech stack and security needs. Begin by trying one or two assistants from this list on a real task and evaluate the results. If your work revolves around GitHub, Copilot is an ideal starting point. For an AI-centric editor, give Cursor a try. If you prefer open-source flexibility or self-hosting, Continue is an excellent choice. If you’re primarily working on AWS or GCP, Amazon Q Developer and Gemini Code Assist will seamlessly incorporate cloud-native support into your workflow.

FAQs

Are AI coding tools safe to use for proprietary code?

Yes, if configured correctly. Choose vendors that do not train their models on your private code, provide enterprise data controls, and support on-premise or private deployments. Always review the security documentation of each product along with your organization’s compliance requirements.

Will AI coding assistants replace developers?

No, they serve as accelerators, not replacements. AI tools excel at handling boilerplate code, refactoring, and explanations, while developers are essential for system design, product context, code review, and accountability.

Which AI code assistant is best for large monorepos?

Sourcegraph Cody and Cursor are robust options due to their repository-wide context and task execution capabilities. GitHub Copilot with Workspace is also beneficial for GitHub-centric teams.

What about local or offline coding?

Consider Continue with a local model runner (for instance, via Ollama or similar setups). Tabnine and Codeium also provide privacy-centered and enterprise-friendly options.

How much do these tools cost?

Most of them offer a free tier along with paid plans based on per-user usage. Enterprise pricing may vary based on security features, context limitations, and deployment models. Always check the current pricing details before making a commitment.