Shakespeare vs. Cursor

If you're exploring AI-assisted development tools, you've probably heard of Cursor. It's a solid code editor for professional developers who want AI help while coding. But what if you're not a professional developer? Or what if you want more control over your privacy, payments, and AI providers?

Shakespeare was built for exactly this. Whether you're a seasoned dev or someone who's never touched code, Shakespeare gives you the freedom to build websites and apps through simple conversation with AI. We dug into Cursor's documentation, terms of service, and privacy policies to see how it stacks up.

Shakespeare vs. Cursor: Code Ownership

You own your code with both tools. Cursor's Terms of Service confirm this, and Shakespeare takes the same approach. Your code belongs to you, full stop.

Shakespeare vs. Cursor: Pricing

Cursor offers a free Hobby tier, but it's significantly limited. For full functionality, you'll need Pro ($20/mo), Pro+ ($60/mo), or Ultra ($200/mo). Teams start at $40/user/mo.

Shakespeare works differently. Bring your own API key, connect a free model on OpenRouter, or run a local LLM and you get full functionality for free. If you want to purchase credits, you only pay for what you use. No subscriptions required.

Shakespeare vs. Cursor: Payment Options

Shakespeare vs. Cursor: AI Provider Choice

Both tools let you connect to models from OpenAI, Anthropic, Google, and others. Both support bringing your own API keys. Shakespeare adds increased privacy through Maple AI, which uses end-to-end encryption. You're not locked into any single ecosystem.

Shakespeare vs. Cursor: Local LLM Support

Cursor doesn't natively support local LLMs. Workarounds exist, but setup is technical and uses tunneling, like ngrok. A non-technical user will need help to set this up with Cursor. But if you're already skilled enough to set up a local LLM, you should be able to figure out how to run a local LLM with Cursor.

Shakespeare supports local LLMs through Ollama with straightforward configuration. No tunneling required. You can also run everything fully offline.

Shakespeare vs. Cursor: Import, Export, and Git

Both tools connect to GitHub and GitLab. Cursor imports from VS Code; Shakespeare imports from Git repos or ZIP files. The difference: Shakespeare doesn't assume you already know Git. It guides you through version control, and you can create pull requests without leaving the app.

Shakespeare vs. Cursor: Deployment

You can deploy what you build in Cursor by using third-party hosting services like Vercel or Netlify. It works with any deployment provider that has a command line interface (CLI) or that offers CI/CD integration.

Shakespeare deploys for free from in-app using a free .wtf domain. Every project gets a public URL the moment you create it. No configuration, no waiting.

Shakespeare vs. Cursor: Nostr Support

Cursor has no built-in Nostr integration while Shakespeare is built on Nostr. Shakespeare intuitively builds on Nostr with its knowledge of NIPs, kinds, and handling relays. Cursor doesn't have that knowledge built in, so you'll have to consult an MCP.

Shakespeare vs. Cursor: Project Privacy

Cursor's Privacy Mode is available to all users but must be manually enabled. When it's off, Cursor may use your code to train models, and third-party providers may access your data. If you created your account after October 15, 2025, your prompts may also be shared with model providers. About half of Cursor users have Privacy Mode enabled.

Shakespeare's approach is simpler: privacy is always on. Your code stays yours. We don't train on your projects. We don't read your chats. There's no toggle because privacy isn't optional.

Shakespeare vs. Cursor: The Bottom Line

Cursor is a capable tool for professional developers comfortable with code editors and managing their own infrastructure. Shakespeare is for everyone, regardless of skill level. It's for developers who want more freedom as well as creators who've never written code. Anyone who wants to build without complexity, subscriptions, or privacy trade-offs.

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