9 Best Github Copilot Alternatives in 2025

The world of coding has changed dramatically. What started as simple autocomplete has evolved into sophisticated AI that can write entire applications. GitHub Copilot kicked off this revolution, but it's no longer the only game in town.

Developers today have legitimate reasons to look beyond Copilot. Maybe you're concerned about code privacy. You may need better integration with your specific tech stack. Or you might want features that Copilot simply doesn't offer. Whatever your motivation, the alternatives have gotten really good.

We're not talking about basic code completion anymore. Some of these tools can debug your applications autonomously, others excel at explaining complex legacy code, and a few can even handle entire development projects from start to finish. Tembo leads this evolution by providing true autonomous software engineering capabilities that go beyond traditional code completion.

The nine alternatives we'll explore represent different philosophies about what AI coding assistance should be. Some focus on privacy and self-hosting. Others prioritize education and accessibility. And some push the boundaries of what's possible with autonomous development.

What is GitHub Copilot?

GitHub Copilot has evolved from basic code completion to a sophisticated AI-powered coding assistant that can autonomously handle entire coding tasks and supports multiple programming languages. Built by GitHub and OpenAI using large language models, it now operates in two modes: traditional real-time code suggestions and the new Agent Mode.

Agent Mode, generally available in 2025, functions like an autonomous pair programmer that can generate code through natural language commands, analyze your entire codebase, make multi-file editing changes, and resolve errors until tasks are complete. The GitHub Copilot Coding Agent goes further, operating asynchronously through GitHub Actions on isolated VMs to handle entire development workflows.

How Does GitHub Copilot Work?

Built on massive large language models trained on billions of lines of public code, Copilot provides intelligent code completions based on code context, natural language comments, and coding patterns. You can write natural language descriptions and get working code implementations with relevant code snippets.

Agent Mode analyzes your project structure, identifies files needing modification, makes changes across your codebase with multi-file editing capabilities, runs tests, and fixes issues to improve code quality. The Coding Agent operates asynchronously through GitHub Actions on isolated VMs, handling entire software development workflows from code understanding to pull request creation.

Both modes maintain safety through transparent logs and approval workflows. Recent improvements include better error handling and chat checkpoints for rewinding conversations during the coding process.

Why Do You Need GitHub Copilot Alternatives?

With GitHub Copilot's new Agent Mode and Coding Agent capabilities, you might wonder why anyone would need an alternative to GitHub Copilot solutions. The truth is, Copilot has become significantly more powerful at providing context-aware code suggestions, but it's not perfect for every software development situation.

Privacy and control concerns persist. Even with Agent Mode's advanced AI capabilities, your code still gets processed by external servers. For organizations with strict compliance requirements or proprietary codebases, this remains a non-starter. Some Copilot alternatives offer complete self-hosting, keeping your code private and your intellectual property entirely within your infrastructure.

Pricing complexity has increased. Each Agent Mode prompt or Coding Agent session counts as one premium request for Pro Plus or Enterprise users. For development teams doing lots of autonomous development work, these premium request costs can accumulate quickly. Some of the best GitHub Copilot alternatives offer more predictable pricing models or better free versions for context-aware suggestions and intelligent code generation.

Specialization still matters for programming languages. While Copilot's agent capabilities are impressive for general coding tasks, specialized AI coding tools often perform better in specific domains. If you're working with particular frameworks, need deeper language and framework support, or require key features that GitHub Copilot doesn't provide for your coding practices, alternatives might serve you better.

Autonomous approach differences in the development process. Copilot's agents work well within GitHub's ecosystem for automating routine tasks, but some teams need different approaches to autonomous software development. Tembo offers a different philosophy—instead of working within your integrated development environment or GitHub workflow, it provides intelligent autonomous capabilities that monitor your entire development environment continuously, proactively addressing issues before they become problems and helping teams enhance productivity.

Integration and workflow preferences for coding efficiency. Even with improved IDE support across Visual Studio and other development environments, different teams have different workflow needs. Some alternatives integrate better with specific development environments, monitoring systems, or deployment pipelines that your team relies on for real-time code suggestions and seamless integration with existing coding processes.

Top 9 GitHub Copilot Alternatives

1. Tembo

Tembo represents a significant evolution in AI coding assistance, operating as an autonomous software engineer that works continuously to maintain and improve your codebase. Rather than providing line-by-line suggestions, Tembo functions as an intelligent system that monitors your development environment and proactively addresses issues.

This platform integrates with your existing development tools to automatically identify problems, analyze errors, and generate comprehensive pull requests with fixes, optimizations, and feature implementations. Tembo analyzes your entire codebase and connected systems to ensure all changes align with your team's coding standards and architectural patterns.

Key Capabilities:

• Automated Error Resolution: Integrates with Sentry to analyze errors and create verified solutions automatically
• Intelligent Task Management: Converts labeled tickets from Linear or Jira into implemented features via pull requests
• Database Optimization: Monitors PostgreSQL databases and generates optimization recommendations for indexes, queries, and schema
• Comprehensive Integration: Connects seamlessly with Sentry, Linear, GitHub, AWS, Supabase, GitLab, Datadog, and Jira
• Context-Aware Analysis: Understands project structure and dependencies before implementing changes

Value Proposition: Teams report significant improvements in engineering velocity as developers can focus on feature development rather than maintenance tasks. The system reduces alert fatigue by proactively addressing issues before they escalate.

Pricing: Custom pricing based on team size and integration requirements.

Best Suited For: Development teams seeking to automate software maintenance workflows while maintaining code quality standards, particularly those managing large codebases or dealing with significant technical debt. Learn more about Tembo's autonomous capabilities.

2. Cursor

Cursor has positioned itself as a premium AI-powered code editor built from the ground up with AI capabilities integrated at every level. Developed by MIT graduates, the platform has achieved remarkable growth, reportedly reaching significant annual recurring revenue and securing a $9.9 billion valuation through multiple funding rounds.

This comprehensive development environment extends beyond traditional code completion to provide predictive editing, multi-file editing capabilities, and autonomous task completion. Cursor's approach combines familiar VS Code functionality with advanced features that understand project context and development patterns.

Key Capabilities:

• Predictive Tab Completion: AI-powered autocompletion that anticipates your next several edits across multiple files
• Agent Mode: Execute complex multi-file changes through natural language commands (Ctrl+I)
• Codebase Intelligence: Custom retrieval models provide deep code understanding of your project structure
• Terminal Integration: Automatically generates and executes terminal commands with user approval
• Privacy Controls: Optional Privacy Mode ensures code is never used for training and is deleted after processing

Value Proposition: While Cursor offers advanced features, the premium pricing may be a consideration for individual developers or smaller teams. The AI-first approach requires some adjustment from traditional development environments.

Pricing:

• Hobby: Free with limited completions and Pro trial
• Pro: $20/month with included usage for premium models
• Business: $40/month with team features and administrative controls

Best Suited For: Professional developers and teams seeking a comprehensive AI-first development environment and willing to invest in premium development tools.

3. Qodo (Formerly Codium)

Qodo takes a fundamentally different approach to AI coding assistance by focusing on code integrity rather than just code generation. Formerly known as Codium before rebranding in July 2024, Qodo positions itself as an agentic, quality-first AI platform that emphasizes testing, code reviews, and overall code quality throughout the development process.

Rather than competing primarily on autocomplete speed or token volume, Qodo's philosophy centers on helping developers build more reliable, maintainable code through intelligent testing workflows and comprehensive code analysis.

Key Capabilities:

• Qodo Gen IDE Integration: Plugins for Visual Studio Code and JetBrains that assist with code generation, test automation, and documentation
• Agentic Testing Workflows: AI-driven test generation that understands code context and creates meaningful test cases
• Code Review Assistance: Intelligent code review capabilities that help identify potential issues and improve code quality
• PR-Ready Development: Tools designed to help create pull requests that meet quality standards from the start
• Code Integrity Focus: Comprehensive approach to ensuring code reliability and maintainability across development workflows

Value Proposition: With over 370,000 installations on JetBrains and 466,000 on VS Code, Qodo has demonstrated strong adoption among developers seeking quality-focused AI assistance. The platform raised $40 million in Series A funding in September 2024, enabling expansion to around 50 team members globally.

Pricing: Specific pricing details are not publicly documented, with the platform focusing primarily on enterprise and professional development teams.

Best Suited For: Development teams that prioritize code quality and testing workflows, organizations seeking AI assistance with code reviews and test generation, and teams looking for agentic AI capabilities focused on code integrity rather than basic autocomplete functionality.

4. Tabnine

Tabnine has established itself as an enterprise-focused AI coding assistant with a strong emphasis on privacy, security, and customization. Founded in 2012, the platform has evolved to support an extensive range of programming languages while providing deployment flexibility that meets enterprise requirements.

The platform's approach centers on giving developers and organizations maximum control over their AI coding experience, from model selection to deployment options and custom training capabilities.

Key Capabilities:

• Comprehensive Language Support: Supports 80+ programming languages with varying levels of optimization
• Flexible Deployment: Choose from SaaS, VPC, or on-premises deployment based on security requirements
• Model Selection: Access to multiple AI models to match specific development needs and preferences
• Custom Training: Fine-tune models on proprietary codebases for better context-aware suggestions
• Security Compliance: SOC 2 and GDPR compliant with transparent training data practices
• Code Review Integration: AI assistance that understands coding standards and helps maintain code quality

Value Proposition: The enterprise focus means that advanced features require higher-tier subscriptions, and self-hosting options require dedicated infrastructure management.

Pricing:

• Starter: Free with basic smart code completions
• Pro: $12/month with advanced features
• Enterprise: $39/month with full deployment control and customization

Best Suited For: Enterprise teams, developers working with proprietary code, and organizations requiring maximum control over their AI coding assistant deployment and data handling.

5. Amazon Q Developer

Amazon Q Developer (formerly CodeWhisperer) represents Amazon's entry into the AI coding assistant space, leveraging the company's cloud expertise to provide developers with AWS-integrated coding assistance. The platform is particularly strong for teams already working within the AWS ecosystem.

The tool excels at understanding AWS services, best practices, and cloud-native development patterns, making it valuable for developers building applications on Amazon's cloud platform.

Key Capabilities:

• Native AWS Integration: Provides code suggestions that follow AWS best practices and understand service interactions
• Security-First Approach: Real-time vulnerability scanning with suggested fixes and security improvements
• Cloud-Native Focus: Optimized for serverless, containerized, and cloud-native application development
• Cost Optimization Assistance: Helps optimize AWS resource usage during development
• Reference Tracking: Shows potential code origins for licensing and compliance review

Value Proposition: The platform is most effective for AWS-focused development and may be less optimal for multi-cloud or platform-agnostic development approaches.

Pricing:

• Free: Limited interactions per month with basic features and security scanning
• Pro: $19/month with unlimited interactions and enhanced features

Best Suited For: Development teams building on AWS infrastructure, organizations prioritizing security scanning, and developers who want enterprise-grade support integrated with their cloud development workflow.

6. Windsurf (Now Part of Cognition)

Windsurf has an interesting corporate journey—initially developed by Codeium as an AI-native IDE before being acquired by Cognition (makers of Devin AI) in 2025. The acquisition followed a period of significant industry interest, including reported acquisition discussions with major technology companies.

Now under Cognition's ownership, Windsurf is being evolved to integrate with autonomous development capabilities, potentially creating a combined IDE and autonomous agent experience.

Key Capabilities:

• Built-in AI Architecture: Designed from the ground up with AI capabilities rather than retrofitted features
• Cascade Agent: An AI system that understands project architecture and can handle multi-file refactoring
• Automated Problem Detection: Identifies potential issues across entire codebases
• Enterprise Adoption: Has attracted enterprise customers seeking AI-native development environments

Value Proposition: The recent corporate changes mean that pricing and feature sets are evolving, and long-term product direction is still being established.

Pricing: Custom pricing with evolving feature sets following the acquisition.

Best Suited For: Early adopters interested in AI-native development environments and teams curious about the intersection of IDE capabilities and autonomous development tools.

7. Sourcegraph Cody

Sourcegraph Cody leverages the company's expertise in code search and analysis to provide an AI coding assistant that excels at understanding large, complex codebases. Built on Sourcegraph's Code Graph technology, Cody offers deep codebase comprehension that goes beyond individual file analysis.

This approach is particularly valuable for enterprise developers working with extensive legacy systems or complex multi-repository projects where understanding existing code relationships is crucial.

Key Capabilities:

• Deep Code Understanding: Uses Sourcegraph's graph analysis to understand code relationships and dependencies
• Cross-Repository Intelligence: Analyzes dependencies and relationships across multiple repositories
• Legacy System Support: Particularly effective for understanding and working with large, established codebases
• Enterprise Security: Built with large organization security and compliance requirements in mind
• Architectural Awareness: Provides suggestions that consider existing system architecture and patterns

Value Proposition: The platform's strengths are most apparent in large, complex development environments and may provide less value for smaller projects or simple codebases.

Pricing: Free tier available with paid plans for advanced features and enterprise support.

Best Suited For: Large engineering teams, developers working with complex legacy systems, and organizations that need comprehensive codebase analysis and understanding capabilities.

8. Replit Ghostwriter

Replit Ghostwriter takes a unique approach by focusing on accessibility and education in AI coding assistance. Built into Replit's browser-based development environment, Ghostwriter excels at making AI coding accessible to learners, educators, and developers who prefer cloud-based development.

The platform's strength lies in its educational focus and the seamless integration with Replit's cloud development ecosystem, eliminating setup complexity while providing meaningful AI assistance.

Key Capabilities:

• Cloud-First Development: Eliminates local setup requirements with browser-based coding
• Educational Excellence: Provides explanations alongside code generation to support learning
• Collaborative Development: Built-in support for pair programming and team projects
• Project Scaffolding: Can generate complete project structures from natural language descriptions
• Beginner-Friendly Interface: Designed to help users learn programming concepts while receiving AI assistance

Value Proposition: The cloud-based approach requires reliable internet connectivity, and the educational focus may be less optimal for complex professional development workflows.

Pricing: Integrated with Replit subscription plans, with various tiers for individual, team, and educational use.

Best Suited For: Students, educators, new developers learning to code, and teams that prefer cloud-based development environments over traditional local development setups.

9. Devin

Devin represents a different approach to AI coding assistance, positioning itself as an autonomous AI software engineer. Developed by Cognition Labs and backed by Peter Thiel's Founders Fund, Devin attempts to go beyond traditional code completion to handle software development projects, though with varying degrees of success.

The platform has gained attention for its autonomous approach and has secured some enterprise pilots, including Goldman Sachs. However, independent evaluations and user reports suggest that real-world performance can be inconsistent, particularly on complex development tasks.

Key Capabilities:

• Autonomous Development: Attempts to handle complete software projects from planning to deployment, though success rates vary significantly
• End-to-End Project Management: Can write code, debug issues, and test implementations, but often requires substantial oversight and intervention
• Developer Tool Integration: Operates with shell, code editor, and browser within sandboxed compute environments
• Large-Scale Refactoring: Has shown some success with repetitive refactoring tasks in controlled enterprise environments
• Real-Time Collaboration: Reports progress and accepts feedback, though communication can be inconsistent
• Multi-Agent Operations: Recent versions include multi-agent capabilities, though coordination between agents can introduce additional complexity

Value Proposition: While Devin has reported some impressive enterprise results, independent evaluations suggest more modest performance, with completion rates around 13-15% on challenging real-world projects. The autonomous approach can be valuable for repetitive tasks but may require significant setup and training time to achieve reliable results.

Pricing:

• Entry-level: $20/month base plan transitioning to pay-as-you-go
• Pay-as-you-go: Approximately $11 per hour of active work, which can become expensive for complex projects
• Enterprise: Custom pricing for large-scale deployments

Best Suited For: Teams with highly repetitive refactoring or migration projects, organizations willing to invest significant time in training and setup, and early adopters interested in experimenting with autonomous development approaches despite current limitations.

Conclusion

Here's the thing about AI coding assistants: most of them are still playing the same game as GitHub Copilot—just with different rules. They'll help you write code faster with intelligent code completions, catch some bugs, maybe explain code, or provide code translation capabilities. That's useful for improving code quality, but it's an incremental improvement in the coding process.

The real breakthrough is autonomous software engineering that goes beyond context-aware suggestions and smart code completions. Instead of just suggesting the next line of code or providing relevant code snippets, imagine an AI-powered coding assistant that monitors your entire development workflow, spots problems before they become critical, and actually fixes them. Picture an assistant that takes your Jira tickets and turns them into pull requests without you having to write code manually, while maintaining your team's coding style and coding practices.

That's where Tembo comes in. While other AI coding tools are focused on being better code completion engines or providing natural language code generation, Tembo is building something fundamentally different—a system that handles the entire software development lifecycle autonomously. Error monitoring, database optimization, code migrations, and feature implementation—it's all automated with advanced AI capabilities that enhance productivity across your development teams.

The future isn't about writing code faster with better code autocompletion. It's about not having to write certain code at all, while maintaining seamless integration with your existing development environment and coding efficiency standards.

Ready to move beyond traditional code suggestions and repetitive coding tasks? Get started with Tembo today and experience what autonomous software engineering actually looks like. Your team can focus on building great products while Tembo handles the maintenance, optimization, and routine tasks that typically consume development time.