GitHub Copilot vs Cursor vs Cody: Which AI Coding Assistant Should You Choose in 2024?
Table of Contents
- Quick Verdict
- Comparison Table
- AI Code Completion Quality
- Chat and Multi-File Editing Capabilities
- IDE Integration and Developer Experience
- Codebase Understanding and Context
- Pricing Analysis and Value Comparison
- Security and Privacy Considerations
- Learning Curve and Adoption
- Enterprise and Team Features
- Who Should Choose GitHub Copilot
- Who Should Choose Cursor
- Who Should Choose Cody
- Migration Considerations
- Final Verdict
Quick Verdict
Choose GitHub Copilot if you want the most mature AI coding assistant with excellent inline suggestions, work primarily in VS Code or JetBrains IDEs, and need enterprise-level security features backed by Microsoft.
Choose Cursor if you prefer an IDE built from the ground up for AI pair programming, want the most advanced chat-based code editing with Cmd+K inline editing, and don’t mind switching from your current editor.
Choose Cody if you need multi-model flexibility (Claude, GPT-4, local models), want deep codebase context understanding without vendor lock-in, and prefer keeping your existing editor setup with Sourcegraph’s open-source approach.
Comparison Table
| Feature | GitHub Copilot | Cursor | Cody |
|---|---|---|---|
| Pricing (Individual) | $10/month or $100/year | $20/month (Pro) | Free tier, $9/month (Pro), $19/month (Enterprise) |
| Free Tier | Students/OSS maintainers only | 2-week trial, 2000 completions | 500 autocompletions + 20 messages/month |
| IDE Support | VS Code, JetBrains, Neovim, Visual Studio | Standalone editor (VS Code fork) | VS Code, JetBrains, Neovim |
| AI Models | GPT-4, Codex | GPT-4, Claude 3.5 Sonnet, GPT-4o | Claude 3.5, GPT-4, Mixtral, local models |
| Context Window | Limited file context | 30+ files with long context | Entire codebase with embeddings |
| Inline Completions | Excellent (ghost text) | Excellent (ghost text + Tab) | Good (ghost text) |
| Chat Interface | In-editor chat | Native chat + composer | In-editor chat with context |
| Codebase Search | Basic file search | Built-in with @-mentions | Deep semantic search via Sourcegraph |
| Multi-file Editing | Manual file switching | Excellent (apply to multiple files) | Good (via chat commands) |
| Learning Curve | Low (familiar workflow) | Medium (new editor) | Low (plugin-based) |
| Enterprise Features | SSO, policy controls, audit logs | Team sharing, admin controls | Self-hosted, BYOK, SOC 2 |
| Best For | Individual devs, GitHub-centric teams | AI-first workflows, power users | Teams wanting model choice & security |
AI Code Completion Quality
GitHub Copilot: The Industry Standard
GitHub Copilot pioneered AI-powered code completion and still delivers outstanding inline suggestions. It analyzes your current file and immediately surrounding context to generate ghost text completions as you type.
In my testing, Copilot correctly predicted entire function implementations about 35-40% of the time for common patterns. It particularly excels at repetitive code like CRUD operations, test boilerplate, and standard API integrations.
The model uses GPT-4 Turbo for complex completions and a faster Codex variant for real-time suggestions. Response time averages 100-300ms, which feels nearly instantaneous during normal typing.
Key limitation: Context window is restricted to your current file plus recently opened files. It won’t automatically reference your entire codebase, which can lead to suggestions that don’t match your architecture patterns.
Cursor: Built for AI from Day One
Cursor feels different because the entire editor is designed around AI assistance. The Tab key functions as your primary AI interaction—you see suggestions inline and press Tab to accept, similar to Copilot but with noticeably faster iteration.
What sets Cursor apart is its Cmd+K inline editing command. Highlight any code, press Cmd+K, and describe changes in natural language. The AI edits directly in your file with a diff preview. This workflow is significantly faster than copying code to a chat interface.
Cursor’s completion quality matches or exceeds Copilot because it uses both GPT-4 and Claude 3.5 Sonnet (you can choose per request). In my testing, Claude 3.5 generated more idiomatic Python and TypeScript code, while GPT-4 handled complex algorithmic problems better.
The editor can maintain context across 30+ files simultaneously using long-context models. Tell it “refactor this function to match the pattern in utils/database.ts” and it actually understands both files.
Cody: Context-Aware with Model Flexibility
Cody’s superpower is understanding your entire codebase through Sourcegraph’s code intelligence platform. It builds embeddings of your repository, allowing it to reference relevant code even from files you haven’t opened recently.
During testing, I asked Cody to implement a feature similar to existing functionality in a different module. It correctly identified the pattern and matched our team’s conventions—something Copilot missed because those files weren’t in my recent history.
Autocompletions are solid but slightly slower than Copilot (300-500ms latency). This is partly because Cody performs more context retrieval before generating suggestions.
You can switch between Claude 3.5 Sonnet, GPT-4, GPT-4 Turbo, Mixtral, or even local models. For sensitive codebases, running Llama 3 or CodeLlama locally means no code leaves your infrastructure.
Chat and Multi-File Editing Capabilities
GitHub Copilot Chat: Functional But Basic
Copilot Chat appears as a sidebar panel in VS Code and JetBrains IDEs. You can ask questions about your code, request explanations, or generate code snippets.
The interface supports slash commands like /explain, /fix, and /tests for common tasks. It’s genuinely useful for quick questions but lacks sophisticated multi-file awareness.
If you need to make related changes across multiple files, you’ll manually apply suggestions one file at a time. There’s no “apply this pattern to all matching components” feature.
The workspace indexing (currently in beta) promises better codebase understanding, but in practice, it still doesn’t match Cursor or Cody’s context awareness.
Cursor: The Most Powerful Chat Implementation
Cursor offers two distinct AI interfaces: standard chat and “Composer” mode. The standard chat works like Copilot’s but with superior context management—you can @-mention specific files, folders, docs, or even web URLs.
Composer mode (Cmd+I) is where Cursor truly shines. It’s a full-screen AI interface that can plan and execute changes across your entire codebase. Ask it to “add error handling to all API calls” and it identifies every relevant file, shows you a preview, and applies changes with one click.
In testing, I used Composer to migrate a project from REST to GraphQL. It correctly updated 12 files including route handlers, client code, and tests. The diff preview caught two issues I corrected before applying.
The @codebase command lets the AI search your entire project. “@codebase how do we handle authentication?” returns relevant code snippets with file locations. It’s like having a senior dev who knows your entire repository.
Cody: Deep Context Meets Powerful Chat
Cody’s chat interface sits between Copilot and Cursor in capabilities. It doesn’t have Cursor’s Composer mode, but it includes sophisticated codebase understanding through Sourcegraph.
The key differentiator is context quality. Cody automatically includes relevant code from across your repository without explicit @-mentions. Ask “how should I implement caching for this endpoint?” and it references your existing caching patterns, even from microservices you haven’t touched in months.
The chat supports enhanced context modes that pull in up to 10 files automatically. You can toggle between “precise” (fewer files, faster) and “extensive” (more context, better answers).
Multi-file edits work through chat commands but require more manual review than Cursor’s approach. Cody suggests changes, you approve them, and it applies them file-by-file.
For teams using Sourcegraph already, Cody integrates codebase search directly in chat. Search for symbols, find references, and get AI explanations—all in one interface.
IDE Integration and Developer Experience
GitHub Copilot: Works Where You Already Work
Copilot’s biggest advantage is universal IDE support. Official extensions exist for VS Code, Visual Studio, JetBrains IDEs (IntelliJ, PyCharm, WebStorm, etc.), and Neovim.
The VS Code extension feels most polished with 1-2 second load times and minimal performance impact. JetBrains support is solid but occasionally conflicts with built-in AI features in newer IDE versions.
You keep your existing editor configuration, themes, and keyboard shortcuts. For developers deeply invested in their IDE setup, this zero-friction adoption is crucial.
The extension adds about 50-100MB memory overhead and uses minimal CPU when idle. During active completion, CPU usage spikes briefly (10-15%) but doesn’t cause noticeable lag on modern machines.
Cursor: The Best AI-Native Editor Experience
Cursor is an editor—a fork of VS Code with AI capabilities baked into every feature. This means perfect integration but requires switching from your current IDE.
The good news: Cursor imports VS Code settings, extensions, and keyboard shortcuts automatically. Most VS Code extensions work without modification. The transition takes about 15 minutes for typical setups.
Performance is impressive. Despite constant AI features, Cursor feels as responsive as VS Code. It lazy-loads AI models and caches suggestions aggressively.
The built-in terminal, file explorer, and git integration all include AI enhancements. Right-click any terminal error and get an AI explanation. Git commit messages auto-generate based on your changes.
Major tradeoff: You can’t use Cursor’s advanced features (Composer, multi-file edits) in other editors. If you switch IDEs frequently or your team uses mixed editors, this creates friction.
Cody: Flexible Plugin Approach
Cody follows the traditional plugin model with extensions for VS Code, JetBrains, and Neovim. Install it in your existing editor and continue working normally.
The VS Code extension matches Copilot’s polish with comparable performance characteristics. JetBrains integration has improved significantly in recent versions but still feels slightly less native than VS Code.
Cody’s autocomplete doesn’t interfere with other AI tools. Several developers on my team run both Cody (for codebase search) and Copilot (for completions) simultaneously without conflicts.
The plugin adds sidebar panels for chat, search, and commands. Unlike Cursor, you won’t get inline diff previews or composer-style multi-file editing. The interface feels more like a copilot than a co-driver.
Codebase Understanding and Context
GitHub Copilot: Limited Context Window
Copilot currently indexes your open file, recently edited files (roughly 10-20), and limited workspace symbols. This works fine for isolated features but struggles with architecture-wide patterns.
The experimental workspace indexing feature (in beta as of late 2024) improves this by analyzing your repository structure. However, it’s opt-in, slow to build initially (5-10 minutes for medium projects), and still doesn’t match Cody’s semantic understanding.
In practice, this means Copilot suggestions might not follow your team’s naming conventions, miss similar implementations elsewhere, or suggest deprecated patterns still present in older files.
Cursor: Long-Context Models for Active Projects
Cursor excels at understanding your current work context. It maintains awareness of 30+ files you’re actively editing using GPT-4 Turbo’s 128K token context window.
The @codebase command performs fast semantic search across your repository. It’s not as comprehensive as Sourcegraph but covers 95% of daily needs. Search results appear instantly (under 1 second for most queries).
For large monorepos (100K+ files), Cursor’s indexing can take 30-60 minutes initially. Incremental updates are fast, but switching branches requires re-indexing changed files.
Key limitation: Cursor’s context is optimized for active development. For questions about distant parts of the codebase you haven’t touched recently, the AI may not surface relevant context automatically.
Cody: Enterprise-Grade Codebase Intelligence
Cody leverages Sourcegraph’s code intelligence platform—the same technology companies like Uber and Cloudflare use to navigate massive codebases. This provides genuinely different capabilities.
Sourcegraph builds a semantic code graph of your repository including symbols, references, dependencies, and relationships. Cody queries this graph to find relevant context before generating responses.
Ask “show me all implementations of the Repository pattern” and Cody finds them even if they use different names, are in different languages, or haven’t been touched in years. This semantic search is miles ahead of simple text matching.
For enterprise customers, Cody can index multiple repositories simultaneously. Work in a microservices architecture with 50 repos? Cody understands cross-service dependencies and can reference related code from other services.
The tradeoff is setup complexity. Self-hosted Sourcegraph requires infrastructure investment (Kubernetes recommended). The cloud version is simpler but means sending code to Sourcegraph’s servers (they encrypt and offer SOC 2 compliance).
Pricing Analysis and Value Comparison
GitHub Copilot: Straightforward Microsoft Pricing
Individual: $10/month or $100/year (17% discount for annual) Business: $19/user/month (annual commitment required) Enterprise: $39/user/month (includes GitHub Enterprise features)
Students and verified open-source maintainers get free access—a genuinely valuable benefit for the developer community.
The individual plan includes all core features: completions, chat, CLI integration, and workspace indexing. No artificial limitations on usage or requests.
Business and Enterprise plans add organization management, policy controls, and exclude training data opt-out (your code won’t be used to train models). Enterprise includes audit logs and SAML SSO.
Value assessment: At $10/month, Copilot is the cheapest option for individual developers. If it saves you 30 minutes per week (a conservative estimate), the ROI is clear. The business plan is reasonable for teams already invested in GitHub.
Cursor: Premium Pricing for Premium Features
Free: 2-week trial, 2000 completions, 50 slow premium requests Hobby: $20/month with 500 fast premium requests Pro: $20/month with unlimited fast requests (actually capped at ~2000/month) Business: $40/user/month with higher limits and team features
Cursor’s pricing is 2x GitHub Copilot for individuals, which raised eyebrows at launch. However, the capabilities justify it for power users who lean heavily on AI.
The “fast premium requests” limit matters more than you’d expect. Premium requests use GPT-4 or Claude 3.5 Sonnet. Once exceeded, you drop to faster but less capable models. Heavy users can hit limits in 2-3 weeks.
Business plan includes usage analytics, shared configurations, and priority support. Teams of 5+ can negotiate better pricing—I’ve seen deals around $30-35/user/month.
Value assessment: Worth it if you work in Cursor full-time and use Composer/multi-file editing daily. Expensive if you just want basic autocomplete. The 2-week trial genuinely shows whether you’ll get value.
Cody: Freemium with Enterprise Options
Free: 500 autocompletions + 20 chat messages per month (resets monthly) Pro: $9/month with 500 autocompletions + unlimited chat messages per day Enterprise: $19/user/month with unlimited everything + self-hosting option
Cody’s free tier is the most generous—500 completions per month is enough for casual use (roughly 16-20 per day). The 20 chat messages feel restrictive but encourage focused questions.
Pro at $9/month undercuts both competitors while offering unlimited chat. The 500 daily autocompletions cap means roughly 8-12 per hour during an 8-hour workday. Most developers won’t hit this unless they’re writing boilerplate constantly.
Enterprise pricing includes self-hosted deployment, bring-your-own-keys for models, SSO, and full Sourcegraph code search integration. For regulated industries or large teams, this flexibility is invaluable.
Value assessment: Best free tier for trying AI coding. Pro tier offers excellent value at $9/month for individual developers. Enterprise pricing becomes complex with self-hosting costs but provides unmatched control and security.
Security and Privacy Considerations
GitHub Copilot: Microsoft’s Enterprise Security
Copilot sends code snippets to Microsoft/OpenAI servers for processing. Individual plans include training data opt-out, but your code still leaves your machine temporarily.
Microsoft provides SOC 2 Type II certification, encrypts data in transit and at rest, and retains prompts for only 24 hours (telemetry) unless you opt out completely.
For Business and Enterprise plans, Microsoft offers contract guarantees that your code won’t train models. Enterprise gets audit logs showing every Copilot interaction for compliance teams.
The biggest concern: you’re locked into Microsoft’s infrastructure. No option for on-premises deployment or truly air-gapped environments.
Cursor: Cloud-First with Privacy Options
Cursor processes code through its servers using OpenAI and Anthropic APIs. Your code passes through three systems: Cursor servers, OpenAI/Anthropic, and back.
Privacy Mode (in settings) prevents storing any code snippets or conversation history on Cursor’s servers. It still sends prompts to model providers but doesn’t log them long-term.
There’s no self-hosted option currently. For teams with strict data residency requirements or air-gapped networks, Cursor isn’t viable.
The company has SOC 2 Type II certification (as of Q4 2024) and provides DPA agreements for enterprise customers. They’re responsive about security questions but lack Microsoft’s extensive compliance certifications.
Cody: Maximum Flexibility and Control
Cody offers three deployment models:
- Cody Cloud: Code processed through Sourcegraph’s servers
- Bring Your Own Key: Your OpenAI/Anthropic API key, code goes directly to providers
- Self-Hosted: Everything runs in your infrastructure
Self-hosted Cody connects to your own Sourcegraph instance and model endpoints (OpenAI, Anthropic, or local). No code ever leaves your network.
For regulated industries (finance, healthcare, government), this flexibility is often the deciding factor. Several Fortune 500 companies use self-hosted Cody for this reason.
Sourcegraph has SOC 2 Type II, ISO 27001, and offers HIPAA-compliant deployments. Their security posture rivals GitHub’s, backed by extensive documentation and third-party audits.
Tradeoff: Self-hosting requires infrastructure investment. Plan for 2-3 engineers to manage Sourcegraph deployment, though cloud options eliminate this complexity.
Learning Curve and Adoption
GitHub Copilot: Instant Productivity Boost
Install the extension, accept a suggestion, and you’re done. Copilot requires essentially zero learning.
Most developers become proficient in under an hour. The ghost text interface is intuitive—suggestions appear, you Tab to accept or keep typing to ignore.
Teams report 80%+ adoption within two weeks when Copilot is enabled. The familiar workflow means no training materials or onboarding sessions needed.
Cursor: One Week to Proficiency
Switching editors creates initial friction. Budget 2-3 hours for setup, customization, and learning keyboard shortcuts.
Cursor’s AI features require learning new patterns:
- Cmd+K for inline editing (5-10 minutes to internalize)
- Cmd+I for Composer mode (30 minutes to use effectively)
- @-mentions for context control (understanding what to reference)
Most developers report feeling productive within 3-5 days. True mastery—knowing when to use Chat vs. Composer vs. inline edits—takes 2-3 weeks.
The transition is smoother for VS Code users since keyboard shortcuts carry over. JetBrains or Vim users face more adjustment.
Cody: Moderate Learning Curve
Plugin installation is straightforward. Learning to effectively use Cody’s chat and context commands takes 2-4 hours of experimentation.
The main learning curve involves understanding how to phrase questions to leverage codebase context. “How do we handle authentication?” works better than “Write auth code.”
Teams using Sourcegraph already have a head start—the search syntax and navigation patterns transfer directly to Cody.
Enterprise and Team Features
GitHub Copilot for Business/Enterprise
Business tier ($19/user/month) adds:
- Organization-wide policy management
- Usage statistics dashboard
- Code training opt-out guarantees
- Support SLA (5-day response)
Enterprise tier ($39/user/month) includes everything in Business plus:
- Audit logs for compliance
- SAML SSO integration
- IP indemnity coverage
- Dedicated support channel
The admin dashboard shows adoption metrics (users enabled, suggestions accepted, lines of code generated). Useful for measuring ROI but not deeply detailed.
Organizations can block Copilot for specific repositories or file patterns. For example, prevent suggestions in folders containing sensitive credentials.
Cursor for Teams
Business plan ($40/user/month) provides:
- Shared team context and rules
- Usage analytics per team member
- Centralized billing and license management
- Priority support (24-hour response)
The shared context feature lets teams define organization-wide rules like coding standards, preferred libraries, or architectural patterns. All team members’ Cursor instances reference these automatically.
Usage analytics show which features each developer uses most, but don’t track individual productivity metrics (deliberately privacy-focused).
Teams of 5+ can request custom contracts with negotiated pricing. Cursor is particularly willing to work with YC-backed companies.
Cody Enterprise
Enterprise tier ($19/user/month for cloud, custom for self-hosted) includes:
- Unlimited autocompletions and chat
- Self-hosted deployment option
- Bring-your-own-keys for any model
- Full Sourcegraph code intelligence
- SSO (SAML, OAuth)
- Audit logs and compliance reports
- SLA with 99.5% uptime guarantee
The self-hosted option is Cody’s killer enterprise feature. Run everything on-premises or in your VPC. No code ever transmitted to third parties.
Admin controls let you specify which models are available, set context limits, and audit all AI interactions. For regulated industries, these controls are essential.
Sourcegraph’s code intelligence platform (included with Enterprise) provides additional value beyond Cody: code navigation, batch changes, code insights. Many teams adopt Cody as part of broader Sourcegraph investment.
Who Should Choose GitHub Copilot
You’re heavily invested in the Microsoft ecosystem. If your team already uses GitHub for version control, Azure for cloud infrastructure, and VS Code/Visual Studio for development, Copilot is the natural choice. The integration is seamless and billing consolidates with existing GitHub accounts.
You want the lowest-friction AI adoption. Copilot requires no editor switching, minimal configuration, and has instant team adoption. For organizations prioritizing time-to-value over cutting-edge features, this matters enormously.
You’re an individual developer or small team on a budget. At $10/month, Copilot offers the best value for basic AI assistance. The features cover 90% of daily coding tasks without premium pricing.
You primarily write mainstream languages. Copilot excels at JavaScript, TypeScript, Python, Java, and other popular languages with massive training data. The suggestions are high-quality and contextually appropriate.
You need proven enterprise security. Microsoft’s certifications, compliance documentation, and enterprise support are unmatched. For Fortune 500 companies or regulated industries using cloud-based AI, Copilot is often the only approved option.
Who Should Choose Cursor
You’re comfortable switching editors for better AI. If you’re willing to change your development environment for genuinely superior AI capabilities, Cursor delivers features competitors can’t match.
You frequently work across multiple files. Cursor’s Composer mode and multi-file editing save hours on refactoring, migrations, and feature development that spans multiple components.
You’re an AI-forward developer. If you want to push AI pair programming to its limits—having the AI implement entire features, not just autocomplete lines—Cursor’s interface is purpose-built for this workflow.
You primarily use VS Code already. The transition is nearly frictionless since Cursor is a VS Code fork. Your extensions, settings, and muscle memory transfer directly.
You work on mid-sized codebases (< 100K files). Cursor’s indexing and context management work best in this range. Larger monorepos may experience performance issues or incomplete context.
Who Should Choose Cody
You need multi-model flexibility. Cody’s ability to switch between Claude, GPT-4, Mixtral, or local models provides unique insurance against model deprecation, pricing changes, or provider issues.
Your codebase is large and complex. If you work in a monorepo with 100K+ files, microservices architecture, or multiple repositories that need cross-referencing, Cody’s Sourcegraph integration is invaluable.
Security and data control are non-negotiable. For organizations that can’t send code to third-party servers, Cody’s self-hosted option is often the only viable AI coding assistant.
You want the best free tier. Cody’s free plan (500 completions + 20 chat messages/month) lets you genuinely evaluate AI coding without credit card commitment. Perfect for students or occasional users.
You’re already using Sourcegraph. If your team has Sourcegraph for code search, adding Cody is a natural extension that leverages existing infrastructure investment.
You prefer staying in your current editor. Unlike Cursor, Cody works as a plugin in VS Code, JetBrains, or Neovim. Keep your customized setup while adding AI capabilities.
Migration Considerations
Moving from GitHub Copilot to Cursor
The transition is straightforward—disable Copilot’s extension, install Cursor, and import your VS Code settings. Total time: 30 minutes.
Your Copilot subscription can be canceled immediately (prorated refund) or allowed to expire. No long-term contracts.
The adjustment period focuses on learning Cursor’s advanced features (Cmd+K, Composer) rather than losing Copilot capabilities. You gain features rather than swapping equivalents.
Moving from GitHub Copilot to Cody
Even simpler—install Cody’s extension alongside Copilot, test both for a week, then disable whichever you prefer less. Many developers run both simultaneously.
The free tier lets you trial Cody risk-free. If it doesn’t improve your workflow, uninstall without any investment.
Cody’s autocomplete positioning can conflict with Copilot’s. Disable one’s inline suggestions to avoid visual clutter.
Moving from Cursor to Copilot/Cody
Requires switching back to VS Code (or your previous editor), which means reconfiguring settings Cursor changed. Budget 1-2 hours to restore your environment.
You’ll lose Cursor-specific features (Composer, advanced multi-file editing). If your workflow depends on these, moving away is painful.
Cursor subscriptions don’t offer prorated refunds for annual plans. Month-to-month is safer if you’re uncertain about long-term commitment.
Final Verdict
For Most Individual Developers: GitHub Copilot
Copilot offers the best combination of quality, price, and ease of adoption for typical development workflows. At $10/month with zero learning curve, it’s the obvious starting point. The autocomplete quality matches competitors while maintaining the familiar editor experience.
Copilot’s limitations—basic chat, limited context awareness, no multi-file editing—only matter if you’re pushing AI assistance to its limits. For 80% of developers working on typical projects, these gaps won’t be noticeable.
For AI Power Users: Cursor
If you’re willing to switch editors and pay 2x the price, Cursor provides genuinely superior AI capabilities. Composer mode and multi-file editing aren’t just incremental improvements—they enable workflows impossible with Copilot.
The decision comes down to this: Do you want AI as a helpful assistant or as a true pair programmer? Cursor delivers the latter while Copilot focuses on the former.
For Enterprise Teams: It Depends
- Choose Copilot if you want Microsoft’s security, simple procurement, and instant adoption with minimal training
- Choose Cursor if your team works exclusively in VS Code, values cutting-edge features, and can absorb higher costs
- Choose Cody if you need self-hosting, model flexibility, work with large codebases, or already use Sourcegraph
My Personal Recommendation
I use Cursor for greenfield projects and intensive feature development where multi-file editing saves hours. The Composer mode is genuinely game-changing for refactoring or implementing features that touch many files.
For maintenance work, bug fixes, and legacy codebases where I jump between files occasionally, GitHub Copilot in VS Code is perfectly adequate at half the price.
I keep Cody installed for its superior codebase search when working in unfamiliar repositories. The semantic search finds relevant code Cursor and Copilot miss.
Bottom line: Start with GitHub Copilot. If you find yourself wanting more powerful AI features after a month, trial Cursor. If you need enterprise security or work in massive codebases, evaluate Cody seriously.
All three tools are genuinely excellent—your choice depends on which tradeoffs align with your specific workflow, budget, and organizational requirements.
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