GitHub Copilot Review

Introduction

As AI tools become embedded in the software development lifecycle, a central question emerges for engineering teams: Is GitHub Copilot the indispensable AI pair programmer for modern development? This review aims to answer that by examining its role not just as a code completion tool, but as a deeply integrated component of the GitHub ecosystem. Having evaluated numerous AI coding assistants, I’ve found that Copilot’s value is heavily tied to its ubiquity and the specific workflow it enables.

What sets GitHub Copilot apart is its position as the industry standard, deeply woven into the environments developers already use. Its native integration with GitHub, VS Code, and Visual Studio means it doesn’t feel like a bolt-on feature but a natural extension of the coding environment. This is reinforced by its enterprise-grade features, including code indemnification and native Pull Request support, which address critical security and collaboration concerns for teams.

In this review, we will dissect the tool’s core capabilities, from its Next Edit Predictions that anticipate your cursor moves to its Workspace feature for natural language planning across repositories. We’ll also explore its multi-model access, which allows users to leverage different AI models for varied tasks. Alongside its features, we’ll provide a balanced look at the user experience, performance realities, and pricing structure. Ultimately, this analysis will help you determine if GitHub Copilot is the right fit for your specific development needs and team workflow.

What is GitHub Copilot?

GitHub Copilot is an AI-powered code completion tool that functions as a virtual pair programmer, deeply integrated into popular development environments like VS Code and Visual Studio. It suggests whole lines, functions, and blocks of code in real-time as you type, aiming to accelerate development and reduce repetitive tasks. Unlike simple autocomplete, Copilot leverages a vast dataset of public code to provide context-aware suggestions that align with your project’s style and logic. This positions it as a proactive assistant rather than a reactive tool, fundamentally changing how developers interact with their editor.

The Engine Behind the AI Pair Programmer

Developed by GitHub, a subsidiary of Microsoft, Copilot is built on a foundation of billions of lines of public code from GitHub repositories. Initially powered by OpenAI’s models, it has evolved into a multi-model system, now offering access to advanced models like GPT-4, Claude, and Gemini within the GitHub Copilot Chat interface. This allows users to select different models for varied tasks, from code generation to natural language explanations. The tool’s integration with GitHub’s ecosystem provides a unique advantage, as it understands the context of your entire codebase and repository history. This deep integration is central to features like Workspace, which enables natural language planning for complex, multi-file tasks and repository-level refactoring.

Market Positioning and Target Audience

In the crowded landscape of AI coding assistants, GitHub Copilot is positioned as the industry standard and ubiquitous tool for enterprise development. It targets both individual developers seeking productivity boosts and engineering teams requiring scalable, secure solutions. Its key differentiators include code indemnification, which protects enterprises from legal risks related to AI-generated code, and native Pull Request support, which streamlines code review by integrating AI suggestions directly into collaboration workflows. Furthermore, features like Next Edit Predictions anticipate your cursor moves and next logical edits, creating a fluid and anticipatory coding experience. This suite of enterprise-grade features makes it a compelling choice for organizations that prioritize security, collaboration, and integration within the existing GitHub workflow.

Ultimately, GitHub Copilot aims to be more than a plugin; it seeks to be an indispensable component of the modern software development lifecycle. By embedding itself directly into the tools developers use daily, it reduces friction and enhances focus on higher-level problem-solving. As we delve deeper into this review, we will explore how these capabilities translate into real-world performance, the user experience trade-offs, and the practical considerations for adopting this AI pair programmer in your workflow.

Key Features and Capabilities

GitHub Copilot’s core functionality is built around intelligent code completion, offering suggestions that range from single lines to entire function blocks. As you type, it analyzes the context of your code, comments, and project structure to provide relevant completions. This goes beyond traditional autocomplete by generating code that fits the specific logic of your application. For many developers, this means a significant reduction in time spent writing boilerplate or recalling syntax. However, the quality of suggestions can vary, particularly with highly specialized or proprietary algorithms where the model has less training data. The tool is designed to act as a proactive assistant, but its effectiveness is ultimately guided by the clarity of your existing code and prompts.

Beyond line completions, the Chat interface provides a conversational way to interact with your codebase. You can ask Copilot to explain a complex function, suggest fixes for an error, or generate documentation. This feature is integrated directly into the editor, allowing you to get explanations without leaving your workflow. Similarly, the Command Line Interface (CLI) extends Copilot’s assistance to the terminal, where it can help with shell commands, git operations, and scripting. These interfaces transform Copilot from a simple autocomplete tool into a more versatile assistant capable of handling a broader range of development tasks.

Standout Features: Anticipating Your Next Move

A key differentiator is Next Edit Predictions, a feature that aims to anticipate your logical next steps. Instead of just completing the current line, Copilot can suggest the subsequent line or block of code that follows your intended action. For example, after writing a function declaration, it might predict the opening brace and the first line inside the function. This creates a more fluid coding experience, reducing the cognitive load of context switching. While this feature is powerful for routine patterns, its predictions may not always align with unique architectural decisions, requiring you to actively guide or accept its suggestions.

Another powerful capability is the Workspace feature, which allows for natural language planning across an entire repository. By describing a high-level goal in plain English—such as “add user authentication to the API”—Copilot can generate a plan and suggest the necessary files and code changes. This is particularly useful for tackling larger tasks that span multiple files. It bridges the gap between a vague idea and actionable code, though the output still requires careful review to ensure it aligns with project standards and security practices. This feature underscores Copilot’s evolution from a code assistant to a project planning tool.

Multi-Model Access and Deep Integration

To cater to diverse needs, GitHub Copilot offers multi-model access, allowing users to leverage different AI models for varied tasks. This includes options like GPT-4, Claude, and Gemini, each with its own strengths in reasoning, code generation, or language understanding. The ability to switch models means you can choose the best fit for a specific job, whether it’s generating creative solutions or adhering to strict coding standards. This flexibility is a significant advantage, as it lets you tailor the AI’s behavior to your project’s requirements without being locked into a single model’s limitations.

The tool’s integration is arguably its most significant strength. It is natively embedded in Visual Studio Code, Visual Studio, and JetBrains IDEs, and has deep ties to the GitHub platform itself. This includes native Pull Request support, where Copilot can summarize changes and suggest code reviews directly within the PR interface. For enterprises, features like code suggestions filtering and indemnification provide a layer of security and legal protection, addressing a major concern for organizations adopting AI-generated code. This level of integration makes Copilot feel less like an add-on and more like a fundamental part of the development ecosystem.

Security and Enterprise Readiness

Security is a critical consideration for any development tool, and GitHub Copilot addresses this with several enterprise-grade features. The code indemnification policy is a major draw for companies, offering protection against copyright infringement claims related to AI-generated code. This reduces legal risk and makes it easier for organizations to adopt the tool at scale. Additionally, the platform includes options for code suggestions filtering, which can be configured to block suggestions that match public code, further mitigating potential licensing issues.

For teams, the integration with GitHub enhances collaboration and workflow. The ability to use Copilot directly within the Pull Request process means AI assistance can be part of the code review cycle, helping to maintain quality and consistency. However, it’s important to note that while these features add a layer of security, the generated code should still be audited by developers. No AI tool is infallible, and Copilot’s suggestions are based on patterns from existing code, which may include vulnerabilities or outdated practices. Therefore, a robust human review process remains essential, even with enterprise protections in place.

User Experience

How Seamless is the Integration?

The most immediate impression of GitHub Copilot is its deep, native integration into development environments like VS Code and Visual Studio. For developers already using these tools, the learning curve is virtually non-existent. Suggestions appear as ghost text directly in your editor, and accepting them is as simple as pressing Tab. This seamless approach means you don’t have to switch contexts or learn a new interface; Copilot feels like an enhanced version of your existing editor. The conversational chat interface, accessible via a sidebar or command palette, further reinforces this by making AI assistance a natural part of the coding workflow rather than a disruptive separate tool.

This level of integration is a significant advantage over many standalone AI tools that require copying and pasting code between windows. However, it also means the experience is heavily dependent on the host IDE. While the core autocomplete functionality is consistent, some of the more advanced features, like Next Edit Predictions and Workspace planning, have nuances in their implementation across different IDEs. For instance, the fluidity of cursor move predictions might feel more polished in VS Code compared to other supported editors, though the difference is generally minor for most users.

Navigating the Learning Curve

For basic use—accepting line completions and using chat for simple queries—GitHub Copilot is remarkably easy to start using. Most developers can extract immediate value from its core autocomplete feature within minutes of installation. The tool effectively reduces the cognitive load of remembering syntax or recalling standard library functions. However, mastering its more advanced capabilities, such as the Workspace feature for natural language planning across an entire repository, requires a period of adaptation. This feature allows you to describe a high-level task and have Copilot generate a plan and code, but crafting effective prompts that lead to reliable results is a skill that develops with practice.

The multi-model access adds another layer to this learning experience. While having the choice between models like GPT, Claude, and Gemini is powerful, it introduces a decision point: which model is best for this specific task? New users might need time to understand the general strengths of each model (e.g., one might be better for creative generation, another for adhering to strict patterns). This isn’t a barrier to entry, but rather an aspect of the tool that rewards exploration and experimentation to optimize your workflow.

Day-to-Day Workflow Impact

In daily use, GitHub Copilot often feels like a natural extension of the editor, especially for repetitive tasks like writing boilerplate code, generating test cases, or converting comments to code. It excels in accelerating routine work, allowing developers to focus more on architecture and complex problem-solving. The native Pull Request support is a standout feature for team workflows, as AI-assisted code can be reviewed and discussed directly within the collaboration context, streamlining the process.

However, this productivity boost comes with a crucial caveat: active review and editing of suggestions are mandatory. Copilot’s suggestions are probabilistic, based on patterns from its training data, and are not guaranteed to be correct, secure, or optimal. Developers must treat every suggestion as a starting point, not a final product. This is particularly important for business-logic-specific code or novel algorithms where the model might produce generic or inefficient solutions. The tool’s code indemnification for enterprise users provides a legal safety net, but it does not replace the developer’s responsibility for code quality and security. Ultimately, the most effective users are those who maintain a healthy skepticism, using Copilot to draft while they drive the final design and validation.

Key Takeaway: GitHub Copilot delivers a highly integrated and low-friction user experience that can significantly boost productivity for developers willing to engage actively. Its greatest value is realized when used as a powerful assistant that accelerates routine work, not as a replacement for critical thinking and code review.

Performance and Quality

GitHub Copilot’s performance is best understood through the lens of its core promise: to act as a proactive AI pair programmer. In practice, it delivers exceptionally well on this promise for a significant portion of development work. It excels at generating boilerplate code, common algorithms, and translating natural language comments into functional code snippets. For tasks like writing unit tests, implementing standard data structures, or converting API documentation into function calls, the tool often provides accurate and context-aware suggestions that save considerable time. This aligns strongly with the “industry standard” positioning, as it reliably accelerates the repetitive and less creative aspects of coding, allowing developers to focus on architectural and business logic.

However, this proficiency has clear boundaries. When faced with complex, novel logic or highly specialized architectural patterns, the model’s suggestions can become less reliable. The underlying training data, while vast, is drawn from public repositories, which may not cover proprietary or cutting-edge solutions. Consequently, Copilot might generate code that is syntactically correct but logically flawed for your specific use case, or it could “hallucinate” functions and APIs that don’t exist. This is where the “pair programmer” metaphor is most accurate—it’s a collaborator that requires a senior developer’s oversight to guide its output and correct its mistakes.

Key Takeaway: Copilot significantly outperforms basic autocomplete for routine tasks but requires developer expertise to vet its output for complex, novel, or security-critical code.

How Reliable and Consistent Is the Output?

The reliability of Copilot’s suggestions is highly contextual and directly tied to the quality of your existing code and comments. When working within a well-documented, mature codebase with clear patterns, the AI’s consistency improves markedly. It can seamlessly extend existing functions, follow naming conventions, and maintain coding style. This makes it a powerful tool for maintaining consistency across large projects. The integration with GitHub’s workflow, including native Pull Request support, further enhances reliability by allowing teams to incorporate AI suggestions into their standard review and collaboration processes.

Conversely, in sparse or ambiguous contexts—such as a new file with minimal comments or an unfamiliar project—the model’s outputs become more variable. It might offer generic or even incorrect code that doesn’t align with your project’s intent. Security is a critical consideration here; while GitHub offers code indemnification for enterprise plans, the AI itself is not a security tool. It can inadvertently suggest code with known vulnerabilities or outdated libraries. Therefore, the tool’s reliability is not absolute but conditional, functioning best as a productivity enhancer for developers who can critically evaluate each suggestion.

Key Takeaway: Expect high consistency in familiar, well-structured projects, but plan for variable output in new or poorly documented environments, always validating for security and correctness.

Exceeding Expectations? The Pair Programmer Paradigm

For many developers, Copilot often exceeds initial expectations, particularly in speed and utility for daily tasks. The seamless integration into VS Code and Visual Studio means the AI feels like a natural extension of the editor, reducing friction and context-switching. Features like multi-model access (GPT, Claude, Gemini) allow you to tailor the AI’s behavior to specific tasks, which can lead to surprisingly creative or efficient solutions. The promise of a “pair programmer” is largely fulfilled in this regard—it’s a responsive assistant that accelerates your workflow.

However, it’s crucial to calibrate expectations: Copilot is not an autonomous coder. It requires active direction and review. The “pair programmer” metaphor implies a partnership where the human is the lead. You must provide clear context through comments and code structure, and you must meticulously review the AI’s output. For developers expecting a tool that can independently architect and build complex applications, Copilot will fall short. Its true value emerges when used as a collaborative tool that augments human intelligence, not replaces it. This reinforces the need for skilled oversight, ensuring that the AI’s speed doesn’t come at the cost of code quality or security.

Key Takeaway: Copilot excels at accelerating development when used as a guided assistant, but it firmly requires human oversight, affirming its role as a pair programmer rather than a replacement.

Pricing and Value

How is GitHub Copilot priced?

GitHub Copilot offers a tiered pricing model designed for different user groups. The most accessible entry point is the free tier, which is available for students, teachers, and maintainers of popular open-source repositories. This provides an excellent opportunity for learning and contribution without any financial commitment. For individual professionals, the Individual subscription is priced at approximately $10 per month, bringing AI assistance directly into your personal development workflow.

For teams and organizations, the Business tier is the primary offering, costing around $19 per user per month. This tier unlocks critical enterprise-grade features, including organization-wide policy management, indemnification for code, and the native Pull Request integration mentioned earlier. This structured approach means you’re not just paying for the AI model, but for the security, compliance, and collaborative features that make Copilot viable at scale in a professional environment.

Is the cost justified for your use case?

The value proposition of GitHub Copilot hinges on your role and productivity gains. For professional developers, especially those in full-time roles, the cost is often easily justified. The time saved on writing boilerplate code, implementing standard functions, and recalling syntax can quickly offset the subscription fee. The multi-model access (to Gemini, Claude, GPT) further enhances this value, allowing you to choose the best tool for complex tasks without needing separate subscriptions. For teams, the Business tier’s features—like Repo Context for understanding project structure and security indemnification—provide a layer of risk management that can be indispensable for enterprise development.

However, the value is less clear-cut for hobbyists or developers on a tight budget. While the free tier is generous for specific groups, the individual plan may feel like a luxury if coding is a secondary activity. The tool’s effectiveness also depends on active use; if you write code infrequently, the monthly cost may outweigh the benefits. The key is to evaluate your daily coding volume and the potential time savings against the subscription cost.

How does it compare to the market?

In the broader AI coding assistant market, GitHub Copilot sits at a premium price point. Standalone alternatives like Codeium or Tabnine often have free tiers with more generous limits or lower-cost individual plans. However, this price difference reflects Copilot’s unique deep integration within the GitHub ecosystem. Unlike many competitors that function as separate extensions, Copilot’s native integration into VS Code, Visual Studio, and the GitHub workflow itself—from chat to Pull Requests—creates a seamless experience that can be more productive than juggling multiple tools.

When comparing, consider the total feature set. Some competitors may excel at specific tasks (e.g., code explanation or specialized language support), but Copilot’s combination of multi-model access, workspace planning, and enterprise security is a bundled offering. For developers already entrenched in the GitHub/VS Code world, this integrated experience is a significant advantage that may justify the premium. For those using different editors or seeking only basic autocomplete, a lower-cost alternative might offer better value.

Final Value Assessment

Bottom Line: GitHub Copilot offers strong value for professional developers and engineering teams whose productivity gains will cover the cost. The Business tier is a compelling package for organizations needing security and integration. For students and open-source maintainers, the free tier is a fantastic resource. However, for casual coders or those not using the supported IDEs, the cost may not align with the benefit. We recommend leveraging the free tier or trial period to test its impact on your specific workflow before committing to a paid plan.

Pros and Cons

What GitHub Copilot Does Well

GitHub Copilot shines as an AI pair programmer, offering significant advantages for developers who integrate it into their workflow. Its deep integration into popular IDEs like VS Code and Visual Studio makes it feel like a natural extension of the coding environment. For repetitive tasks—like writing boilerplate code, generating unit tests, or implementing common algorithms—it can dramatically accelerate development speed. This is particularly valuable for experienced developers who want to maintain flow and for junior developers who can use it as a learning aid to see how patterns are implemented.

The tool’s enterprise-grade features are a major strength for organizations. It includes code indemnification, which provides legal protection for businesses using the generated code. Its native Pull Request support streamlines collaboration, and the Workspace feature allows for natural language planning across entire repositories. Furthermore, the multi-model access (to models like Gemini, Claude, and GPT) offers flexibility and the potential for higher-quality suggestions tailored to different coding contexts.

Key Limitations and Considerations

Despite its power, GitHub Copilot is not a flawless solution and requires careful management. The AI can sometimes generate insecure or incorrect code, including code with potential vulnerabilities or subtle bugs. This necessitates that developers constantly review and test all suggestions, treating the output as a starting point rather than a finished product. Relying on it without scrutiny can introduce risks into your codebase.

The financial cost is another consideration. While the individual subscription is priced for professionals, the cost adds up for teams on the Business or Enterprise plans. For developers on a tight budget or those who code infrequently, justifying the monthly expense can be challenging. Additionally, there’s a valid concern that over-reliance on AI suggestions might reduce deep problem-solving practice for some users, potentially impacting the development of core coding skills over time.

Summary of Trade-offs

In essence, GitHub Copilot offers a powerful productivity boost with robust enterprise features, but it demands an active, critical approach from its users. It excels at accelerating routine work and supporting team collaboration, yet it cannot replace the developer’s judgment and expertise. The decision to adopt it should be based on a clear assessment of your coding volume, budget, and willingness to maintain a rigorous review process for all AI-generated code.

Who Should Use GitHub Copilot?

Who Benefits Most from GitHub Copilot?

GitHub Copilot is designed to be a productivity multiplier, and it delivers the most value to specific user profiles. Professional software developers are the primary target. If you spend your days writing code, especially in languages like JavaScript, Python, or Go, Copilot’s ability to autocomplete entire functions and suggest complex logic can save hours of repetitive typing. This is particularly impactful for engineers in enterprise teams already using the GitHub ecosystem. The deep integration with pull requests and the security indemnification offered in higher tiers make it a low-friction, compliant addition to a company’s toolchain.

The tool is also a boon for developers exploring new libraries or APIs. Instead of constantly consulting documentation, you can write a natural language comment describing what you need—like // fetch user data from the API and parse the JSON—and Copilot can generate a starting point. This accelerates the learning curve for unfamiliar frameworks. However, this strength assumes you have a foundational understanding of programming concepts. Copilot is a coding assistant, not a coding instructor. It can suggest patterns, but it won’t teach you the underlying principles from scratch.

What Are the Ideal Use Case Scenarios?

In practice, Copilot excels at specific, well-defined tasks that follow recognizable patterns. Its Next Edit Predictions feature, which anticipates your next cursor move, is invaluable when working on repetitive structures like setting up API endpoints or creating similar UI components. For teams, the Workspace feature allows you to use natural language to plan changes across a repository, such as “add input validation to all user forms,” providing a high-level starting point for complex refactors.

Common, high-value scenarios include:

• Generating boilerplate code: Quickly scaffolding configuration files, class structures, or standard project setups.
• Writing unit tests: Creating test cases for existing functions, which can be tedious but is critical for code quality.
• Converting comments to functions: Translating a descriptive comment into a functional implementation, which helps maintain focus and flow.
• Exploring new APIs: Getting instant code samples for how to call a new service or library, reducing context-switching.

For these tasks, Copilot acts as a force multiplier, allowing you to focus on the unique logic of your application rather than the boilerplate.

Who Might Want to Look Elsewhere?

While powerful, GitHub Copilot is not a universal solution. Solo hobbyists on a budget may find the $10/month Individual subscription hard to justify if coding is an occasional pastime. The free tier is generous for students and open-source maintainers, but casual developers might be better served by free, open-source alternatives like Codeium or StarCoder, which offer similar AI completion features without the cost.

Developers working in highly specialized or proprietary domains (e.g., niche scientific computing, legacy systems with unique languages) may also find Copilot’s suggestions less helpful. Since the model is trained on vast public code, its suggestions are strongest for mainstream, well-documented technologies. If your work involves obscure languages or heavily customized frameworks, the AI’s accuracy may drop significantly, requiring more manual correction than time saved.

Finally, some developers prefer to avoid AI tools for ethical or learning reasons. If your goal is to deeply understand every line of code you write or if you have concerns about data privacy and code ownership, a traditional development workflow might be preferable. The key takeaway is that Copilot is most effective when you treat it as a collaborative tool, not an autonomous agent. It requires active scrutiny and domain knowledge to be used safely and effectively. For those who prefer a fully manual, AI-free coding experience, the tool’s benefits may not align with their philosophy.

Final Verdict

After extensive analysis, GitHub Copilot stands as a mature and powerful AI assistant that can significantly boost developer productivity, but its value is heavily dependent on the user’s skill, context, and workflow. It is not a set-and-forget solution; it is a tool that demands active collaboration and critical oversight. For the right user, it is a transformative addition to the development environment, but for others, it may introduce more complexity than it resolves.

Who Should (and Shouldn’t) Use GitHub Copilot

The decision to adopt Copilot hinges on your specific role and needs. Its strengths are most pronounced for certain profiles:

• Best For: Professional developers and engineering teams already embedded in the GitHub and VS Code ecosystem. If you regularly work with popular languages (like JavaScript, Python, or Go) and value deep IDE integration, Copilot’s native feel and features like Next Edit Predictions and Workspace planning will feel like a natural extension of your workflow. The Business tier is particularly compelling for enterprises needing security and compliance, thanks to its indemnification and admin controls.

• Proceed with Caution: Hobbyists, students on a budget, or developers who write code infrequently may find the cost difficult to justify, even with the free tier. Furthermore, if your priority is to understand every line of code from first principles or if you have strict data privacy concerns, the AI-assisted model may clash with your workflow philosophy.

The Bottom Line: A Powerful Tool with a Critical Caveat

Ultimately, GitHub Copilot earns a high rating for its best-in-class integration, robust feature set, and enterprise readiness. It delivers on the promise of an AI pair programmer by accelerating routine tasks and reducing context switching. However, this rating is balanced against legitimate concerns about code quality, cost, and potential over-reliance. The AI can generate insecure or incorrect code, making your own expertise and review process non-negotiable.

Our final recommendation is a clear “yes” for professional developers and teams whose productivity gains will offset the subscription cost. For this audience, the investment is justified. For others, we strongly advise leveraging the free tier or trial period to test its impact on your specific workflow before committing. The key to success is to approach Copilot as a collaborative tool, not a replacement for your own judgment. Use it to accelerate the mundane, but always own the final code.

Frequently Asked Questions

What is GitHub Copilot and how does it work?

GitHub Copilot is an AI-powered code completion tool that acts as a pair programmer. It integrates directly into popular IDEs like VS Code and Visual Studio, analyzing the context of your code and comments to suggest entire lines or functions in real-time. It’s powered by models like GPT-4 and Claude, trained on a vast corpus of public code, to help developers write code faster and with less context switching.

Is GitHub Copilot worth the cost for individual developers?

The value depends on your workflow. For developers who frequently write boilerplate code, explore new libraries, or work on repetitive tasks, Copilot can significantly boost productivity and reduce time spent searching for syntax. However, for developers who primarily work in highly specialized or proprietary codebases, the subscription cost may not justify the benefits. Many find the free trial useful for evaluating its impact on their specific projects.

How does GitHub Copilot handle code security and privacy?

GitHub Copilot offers a code indemnification policy for its Business and Enterprise plans, providing legal protection if code suggestions are accused of infringing on open-source licenses. For privacy, individual suggestions are not stored by GitHub, but data from Business/Enterprise plans can be used for service improvement unless opted out. It’s crucial to review suggested code carefully, as AI can sometimes generate insecure or inefficient code, and always follow your organization’s security policies.

Can GitHub Copilot generate code for any programming language?

GitHub Copilot supports a wide range of programming languages and frameworks, including Python, JavaScript, TypeScript, Go, Java, and C++. Its effectiveness varies by language; it often performs best with languages that have abundant public code in its training data (like JavaScript and Python). For newer or less common languages, suggestions may be less reliable. It’s best used as an assistant for common tasks rather than a complete solution for complex, niche problems.

What’s the difference between GitHub Copilot Individual and Business/Enterprise plans?

The Individual plan is for solo developers and includes core code completion features. The Business plan adds organization-wide policy management, secure data handling, and admin controls for teams. The Enterprise plan includes all Business features plus advanced security, compliance certifications, and dedicated support. Both paid tiers offer the indemnification policy, while the Individual plan does not. Pricing is per user per month, with volume discounts available for larger teams.