💻

GitHub Copilot

Code Assistants AI that speeds coding, testing, and reviews

Paid ⭐⭐⭐⭐⭐ 4.6/5 💻 Code Assistants 🕒 Updated
Visit GitHub Copilot ↗ Official website
Quick Verdict

GitHub Copilot is an AI pair‑programmer that delivers context‑aware code completions, chat, and pull‑request assistance directly in your IDE and on GitHub.com. It’s built for professional developers and teams who want faster implementation, tests, and reviews with enterprise governance. Pricing starts at $10/month for individuals, is free for verified students, and scales to Business ($19) and Enterprise ($39) organization plans.

Best For
Professional developers and GitHub-centered engineering teams
Free Tier
Verified students and teachers; 30‑day individual trial
Starting Price
$10 per month for individual subscriptions
Standout
Native pull request summaries inside GitHub.com
IDE Support
VS Code, Visual Studio, JetBrains, Neovim
Data Controls
Business/Enterprise: no training on your code

GitHub Copilot is an AI pair programmer that suggests code and answers questions directly inside your IDE. It delivers context-aware line and block completions, natural-language-to-code, and chat that understands your repository to accelerate implementation and tests. A key differentiator is deep GitHub integration—PR summaries, inline explanations, and policy controls—built on enterprise-grade security. Ideal for professional developers, teams, and students looking to reduce boilerplate and ship faster, this Code Assistants AI works across VS Code, JetBrains, and Neovim. Pricing starts at $10/month for individuals, with free access for verified students and a 30‑day trial.

About GitHub Copilot

GitHub Copilot is an AI pair programmer embedded in your editor that helps you write, understand, and review code faster. Positioned as a premium Code Assistants AI for professional developers and teams, it turns natural language prompts and code context into relevant suggestions, explanations, and fixes. Copilot watches the files you have open, learns patterns from your project, and proposes multi-line completions, tests, and boilerplate so you can focus on architecture and edge cases. Deep GitHub integration brings the assistant to pull requests and issues, while IDE-native chat answers “why” and “how” questions without leaving your flow. The result is shorter feedback loops and fewer repetitive tasks.

In the editor, Copilot provides inline and block completions that adapt to your variable names, active frameworks, and coding style, often filling out entire functions or parameterized SQL in a single suggestion. Natural-language-to-code lets you describe intent in comments like “fetch top 10 orders by revenue” and receive idiomatic implementations in Python, JavaScript, Java, Go, and more. Copilot Chat augments this with repository-aware reasoning: ask it to explain a complex method, refactor a class, or fix a failing test and it cites relevant files, proposes diffs, and inserts changes for approval. On GitHub, Copilot summarizes pull requests, suggests review comments, and highlights risky patterns. Teams can enable duplication detection to block suggestions that closely match public code, and business controls ensure prompts and completions from private repos aren’t used to train the service. It also offers terminal and workspace commands, like generating tests, writing docs, and answering framework questions without leaving the IDE.

Pricing for GitHub Copilot is straightforward. Individuals pay $10 per month or $100 per year and get full IDE completions and Copilot Chat with no metered usage. Copilot Business is $19 per user/month and adds organization management, policy controls, seat provisioning, and advanced privacy options suited for teams. Copilot Enterprise is $39 per user/month and layers on enterprise compliance, SSO/SAML, audit logs, and deeper GitHub integration across pull requests and code review. Verified students and open‑source maintainers get Copilot free, and new users can start with a 30‑day trial to evaluate performance on their stack and workflows.

Developers who live in VS Code, JetBrains, or Neovim use Copilot to eliminate rote tasks and keep momentum. A full‑stack engineer ships features faster by generating API scaffolds, React components, and integration tests, then using chat to explain unfamiliar library code. A DevOps engineer accelerates automation by drafting Terraform, GitHub Actions, and Bash scripts, with Copilot highlighting risky commands before execution. Compared with Amazon CodeWhisperer, Copilot excels in GitHub/IDE integration and repo‑aware chat, while CodeWhisperer is stronger inside AWS workflows. For teams adopting AI coding, Copilot reduces review friction and raises consistency without forcing new tools or processes.

What makes GitHub Copilot different

Three capabilities that set GitHub Copilot apart from its nearest competitors.

  • First‑party GitHub PR features: on GitHub.com, Copilot generates pull‑request summaries and explanations inline, tying suggestions directly to diffs, commits, and review workflows.
  • Enterprise data controls include seat-level policy management, SSO/SCIM, a public‑code filter, and "no training on your code" guarantees for Business and Enterprise plans.
  • Broad, maintained IDE coverage—VS Code, Visual Studio, JetBrains, and Neovim—plus repository‑aware Copilot Chat that references open files and symbols for grounded answers.

Is GitHub Copilot right for you?

✅ Best for
  • Full‑stack engineers who need fast, context‑aware completions and refactors
  • Team leads who need instant pull‑request summaries and explanations
  • Test engineers who need quick, runnable unit test scaffolds
  • Students learning languages who need inline explanations and examples
❌ Skip it if
  • Skip if you need fully offline, self‑hosted inference with no external network access.
  • Skip if you require deterministic license‑attributed code generation with guaranteed provenance for every suggestion.

GitHub Copilot for your role

Which tier and workflow actually fits depends on how you work. Here's the specific recommendation by role.

Solopreneur

Buy if you code daily and want faster boilerplate and tests without leaving your editor.

Top use: Generate idiomatic CRUD endpoints and unit tests from docstrings and TODO comments in VS Code.
Best tier: Copilot Individual
Agency / SMB

Buy for consistent velocity across mixed stacks with seat-based controls and policy settings.

Top use: Speed up feature spikes, PR summaries, and cross-repo refactors with Copilot Chat grounded in the workspace.
Best tier: Copilot Business
Enterprise

Buy for policy, SSO/SCIM, and GitHub-native governance that scales across regulated teams.

Top use: Repository-aware chat for triage and secure PR summaries with organization-wide policy and auditability.
Best tier: Copilot Enterprise

✅ Pros

  • High-quality multi-line suggestions reduce typing and boilerplate by 20–40% in popular languages
  • Tight IDE and GitHub integration, including repo-aware chat and PR summaries
  • Robust enterprise features: SSO/SAML, audit logs, seat management, and privacy controls

❌ Cons

  • May produce insecure or incorrect code; human review and tests remain essential
  • Quality can dip on niche languages, unconventional frameworks, or sparse codebases

GitHub Copilot Pricing Plans

Current tiers and what you get at each price point. Verified against the vendor's pricing page.

Plan Price What you get Best for
Students & Teachers Free Verified through GitHub Education; personal use; includes Chat and completions Students and educators needing no‑cost access
Copilot Individual $10/month or $100/year Single user; IDE plugins and Chat; 30‑day trial available included Solo developers seeking personal productivity boost
Copilot Business $19/user/month Org seats; policy controls, SSO/SCIM; no training on your code Teams needing governance, billing, and security controls
Copilot Enterprise $39/user/month Adds GitHub.com chat, PR summaries, inner‑loop context, advanced compliance features Enterprises standardizing Copilot across GitHub workflows
💰 ROI snapshot

Scenario: Team of 5 developers generating boilerplate, tests, and PR summaries (~25 hours saved/month)
GitHub Copilot: $95/month (5 seats × $19 Copilot Business) · Manual equivalent: $3,125/month (25 hours × $125/hr US contract developer) · You save: $3,030/month (~97% vs. equivalent contractor time)

Caveat: Savings depend on code review diligence and stack fit; Copilot can hallucinate or suggest insecure patterns that require oversight.

GitHub Copilot Technical Specs

The numbers that matter — context limits, quotas, and what the tool actually supports.

Platforms VS Code, Visual Studio 2022/2022+, JetBrains IDEs, Neovim, GitHub.com (PRs, web editor), GitHub Codespaces
Supported languages 20+ languages, including Python, JavaScript/TypeScript, Java, C#, C/C++, Go, Rust, Ruby, PHP, SQL, Bash, Markdown
API availability No public Copilot API; available via IDE extensions and GitHub.com integrations
File format support Source code files, Markdown, JSON, YAML, Dockerfiles, configuration files, unit test files
Team seats Seat-based licensing via GitHub organizations; SSO/SCIM provisioning; no seat minimum
Context window Not published
Rate limits / quotas Not published

Best Use Cases

  • Backend Engineer using it to reduce API boilerplate ~35% and ship endpoints faster
  • Frontend Developer using it to generate unit tests 2x faster and cut regressions
  • DevOps Engineer using it to author CI/CD scripts 40% quicker with fewer errors

Integrations

VS Code JetBrains IDEs Neovim GitHub Actions Azure DevOps

How to Use GitHub Copilot

  1. 1
    Install extension and authenticate with GitHub
    In VS Code, open Extensions (Ctrl+Shift+X), search "GitHub Copilot", click Install. When prompted, "Sign in to GitHub" and authorize the OAuth app. Confirm your plan or start the 30‑day trial. You should see ghost text suggestions appear in supported languages as you type.
  2. 2
    Enable suggestions and configure language targets
    In VS Code, go to Settings > Extensions > GitHub Copilot and ensure "Enable" and "Inline Suggest" are on. In JetBrains, Settings > Tools > GitHub Copilot, enable "Suggest code completions" and "Enable chat". Choose languages to prioritize. This ensures real-time completions and chat context in your active workspace.
  3. 3
    Generate code with comments and chat
    Create a file, add a directive comment like // build an Express REST endpoint with input validation. Pause to preview the ghost text and press Tab to accept. Open the Copilot Chat panel (View > GitHub Copilot > Chat), reference files with #filename and use /tests to scaffold unit tests for changed files.
  4. 4
    Summarize and review a pull request
    On GitHub.com, open your Pull Request, expand the Copilot side panel, and click "Generate summary". Ask follow‑ups like "Explain changes in commit abc123". Use inline "Ask Copilot" on diffs for explanations or suggested fixes. Post the generated summary as a comment to accelerate reviewer onboarding and discussion.

Sample output from GitHub Copilot

What you actually get — a representative prompt and response.

Prompt
Write async Python fetch_json with retries, jitter, timeout using aiohttp and asyncio.
Output
Use aiohttp.ClientSession with ClientTimeout(total=timeout). Loop retries; on failure, if not last attempt, sleep min(2**i + random.random(), 10) for exponential backoff with jitter. GET the URL, call r.raise_for_status(), then return await r.json().

Ready-to-Use Prompts for GitHub Copilot

Copy these into GitHub Copilot as-is. Each targets a different high-value workflow.

Generate Express CRUD Endpoint
Create a simple REST CRUD endpoint quickly
You are GitHub Copilot acting as a senior backend engineer. Task: generate a single-file Express.js CRUD endpoint (GET, POST, PUT, DELETE) for a resource named "items". Constraints: use TypeScript, ES modules (import/export), async/await, simple in-memory store (no DB), input validation for POST/PUT (id as UUID string, name non-empty). Output format: return a comment with the filename (src/routes/items.ts) then the complete TypeScript code block only. Example route: POST /items accepts { name: string } and returns created item with id.
Expected output: One TypeScript file contents for src/routes/items.ts implementing Express CRUD endpoints with validation.
Pro tip: Ask Copilot to also generate a minimal router unit test file if you plan to plug it into existing test suites.
React Component Test Generator
Create unit tests for React functional components
You are GitHub Copilot acting as a frontend test engineer. Task: generate Jest + React Testing Library unit tests for a functional React component named "UserCard". Constraints: use TypeScript test (.test.tsx), mock props and simulate user events, cover rendering, accessibility role assertions, and one error state. Output format: provide a filename comment (tests/UserCard.test.tsx) then the complete test file code only. Example prop: { id: 'u1', name: 'Alice', onSelect: () => void } and simulate clicking a select button to assert onSelect called.
Expected output: A single TypeScript test file tests/UserCard.test.tsx with several Jest/RTL tests covering render, events, and error state.
Pro tip: If the component imports hooks, include jest.mock stubs to avoid unrelated network calls or side effects.
Node Monorepo CI Workflow
Author CI workflow for Node monorepo builds
You are GitHub Copilot acting as a DevOps engineer. Task: create a GitHub Actions workflow for a Node.js monorepo using Yarn workspaces. Constraints: YAML format, run on push to main and PRs, matrix Node versions [14,16,18], install with cache, run lint, build, and test for changed packages only, and upload cache/artifacts. Output format: provide workflow filename comment (.github/workflows/ci.yml) then the full YAML workflow. Include a short section (3 lines) after the YAML explaining how changed-package detection works and one environment variable to override parallelism.
Expected output: A .github/workflows/ci.yml YAML file implementing matrix builds, caching, changed-package detection, and a 3-line explanation.
Pro tip: Set up caching keys that include yarn.lock/lockfile hash to avoid stale dependency caches across branches.
Refactor Callbacks To Async/Await
Convert callback codebase to async/await safely
You are GitHub Copilot acting as a refactoring engineer. Task: convert a callback-based function named "fetchUserAndPosts" into async/await across its module. Constraints: present a file-by-file plan, include exact code diffs (unified format) for up to three files, preserve behavior and error handling, add TypeScript types where missing. Output format: list changed filenames, then for each file show a unified diff snippet (--- old +++ new lines) with full updated function implementations. Example: original uses callbacks like fetchUser(id, cb) and fetchPosts(userId, cb).
Expected output: A plan plus unified diff snippets for each changed file showing callback-to-async/await conversions with types.
Pro tip: Ask Copilot to run through potential breaking changes: event loop behavior, returned promises, and places where callers must now await the function.
PR Security Review And Fixes
Audit PR diffs for security vulnerabilities and fixes
You are GitHub Copilot acting as a senior application security engineer. Task: analyze a provided PR diff (paste below) and produce a security review with prioritized findings, concrete remediation code snippets, and CI policy updates. Constraints: identify OWASP-relevant issues, insecure dependency changes, sensitive data leaks, and missing auth checks; assign severity (Critical/High/Medium/Low); for each critical/high issue give a one-paragraph exploit scenario and a patch code snippet; propose a GitHub Actions policy that blocks merges until automated checks pass. Output format: JSON with fields: findings[], patches[], ci_policy (YAML string), and summary (3-sentence). Paste PR diff into the prompt when using.
Expected output: A JSON object containing findings array, patch snippets, CI policy YAML string, and a concise summary.
Pro tip: When pasting large diffs, include only the changed files most likely to affect security (auth, input handling, dependency updates) to get focused results quickly.
Property-Based Tests With fast-check
Generate property-based tests for complex functions
You are GitHub Copilot acting as a senior QA engineer. Task: produce property-based tests using fast-check for a pure function named "mergeUserProfiles(a,b)" that merges two user profile objects. Constraints: include generator definitions for profiles (optional fields, nested address, email format), shrinkers or minimal counterexample guidance, three property tests (commutativity where applicable, idempotence with identical profiles, and stability with null fields), and a failing example demonstrating a discovered bug. Output format: provide filenames and full code blocks for generators (tests/generators.ts) and tests (tests/mergeUserProfiles.prop.test.ts), plus one paragraph explaining how to run them and interpret counterexamples.
Expected output: Two test files (generators.ts and mergeUserProfiles.prop.test.ts) containing fast-check generators and three property tests with a failing example.
Pro tip: Seed the failing run with a fixed RNG seed included in the test output so CI can reproduce any flaky counterexample deterministically.

GitHub Copilot vs Alternatives

Bottom line

Choose GitHub Copilot over Codeium if you need native GitHub.com pull‑request summaries, org‑wide policy controls, and SSO/SCIM governance tightly integrated with your repositories.

Head-to-head comparisons between GitHub Copilot and top alternatives:

Compare
GitHub Copilot vs ChatPDF
Read comparison →

Common Issues & Workarounds

Real pain points users report — and how to work around each.

⚠ Complaint
Inline completions can be confidently wrong on project-specific abstractions or less-common frameworks.
✓ Workaround
Open and pin key files, add short architecture notes, and use Copilot Chat to reference specific symbols/files before asking for generation.
⚠ Complaint
Latency and suggestion flicker occur in large TypeScript monorepos, especially in JetBrains.
✓ Workaround
Switch to manual trigger/accept-block mode, narrow the edited file scope, and offload multi-file changes to Copilot Chat.
⚠ Complaint
Suggestions may resemble public code or include insecure patterns (e.g., unsafe SQL, weak crypto).
✓ Workaround
Enable 'Block suggestions matching public code' and vulnerability filtering in settings; enforce linters/CodeQL and request parameterized, tested outputs in prompts.

Frequently Asked Questions

How much does GitHub Copilot cost?+
GitHub Copilot costs $10/month or $100/year for individuals. Copilot Business is $19 per user/month with organization controls and enhanced privacy. Copilot Enterprise is $39 per user/month and adds SSO/SAML, audit logs, and deeper GitHub integration across reviews and PRs. There’s no per-token metering; usage is included. Pricing is billed through GitHub, and you can switch between monthly and annual billing to save on long‑term use.
Is there a free version of GitHub Copilot?+
Copilot isn’t free for general use, but verified students and eligible open‑source maintainers can access it at no cost. Individuals can also try Copilot with a 30‑day free trial to gauge code quality, latency, and fit with their stack. After the trial, standard individual pricing applies. Organizations typically start on Copilot Business to evaluate policy controls and team management features before wider rollout.
How does GitHub Copilot compare to its top competitor?+
Against Amazon CodeWhisperer, Copilot stands out for GitHub-native features, repo‑aware chat, and excellent VS Code/JetBrains integration. CodeWhisperer shines in AWS-centric workflows and includes a security scan for AWS best practices. Copilot generally feels more contextually aware across diverse codebases and offers stronger PR assistance. If your team lives in GitHub and multi-language IDEs, Copilot fits better; if you’re deep in AWS tooling, CodeWhisperer may edge it.
What is GitHub Copilot best used for?+
Copilot excels at accelerating routine coding: generating scaffolds, writing unit tests, drafting SQL, refactoring methods, and explaining unfamiliar code in context. It reduces time spent on boilerplate and helps maintain momentum during feature work, bug fixes, and reviews. It’s especially effective in popular languages like JavaScript/TypeScript, Python, Java, and Go, where patterns are well learned. Use it to speed delivery while reserving human judgment for architecture, security, and edge cases.
How do I get started with GitHub Copilot?+
Sign in with your GitHub account, start a 30‑day trial or choose a plan, then install the Copilot extension for VS Code, JetBrains, or Neovim. Enable Copilot Chat, open a familiar repo, and begin with inline comments describing intent. Calibrate settings like suggestion verbosity and duplication detection. For teams, provision seats via GitHub, set organization policies, and pilot on a representative service to benchmark quality, latency, and review impact.
🔄

See All Alternatives

7 alternatives to GitHub Copilot — with pricing, pros/cons, and "best for" guidance.

Read comparison →

More Code Assistants Tools

Browse all Code Assistants tools →
💻
Tabnine
Context-aware code completions for teams and individual developers
Updated Apr 21, 2026
💻
Amazon CodeWhisperer
In-IDE code assistants for faster, AWS-aware development
Updated Apr 22, 2026
💻
Codeium
Production-ready code assistants for developers and engineering teams
Updated Apr 22, 2026