Lovable is one of the newest AI coding assistants on the market, and it’s already turning heads for how quickly it helps developers build real, working features.

If you’re tired of vague AI suggestions or spending hours on boilerplate tasks, Lovable might be exactly what you’ve been waiting for.

In this review, I’ll walk you through my hands-on experience with Lovable, including its pricing, core features, strengths, and limitations.

I’ll also show you how it stacks up against similar tools like GitHub Copilot, Cursor, and Cody, so you can decide whether it’s the right AI pair programmer for your workflow.

Why you can trust this Lovable AI review

I’ve tested Lovable on two real-world projects — one small, one enterprise-level. I also compared its capabilities side by side with GitHub Copilot and Cursor.

What follows is based on hands-on usage, not just demo videos or product pages.

Quick Summary of Lovable AI

• Rating: 4.6 out of 5
• Best for: Developers who want to ship full features, not just code snippets
• Pricing: Starts at $20 per month
• Top features:
  • GitHub integration
  • Full pull request generation
  • Repo-wide code understanding
• Limitations:
  • Doesn’t work outside GitHub
  • Struggles with large or complex monorepos

What I Like

✔️ Lovable can understand your entire GitHub repo and generate full pull requests with real feature implementations
✔️ It provides clear step-by-step explanations before shipping code, which helps with review and trust
✔️ There’s no IDE plugin required — you can work directly from a clean browser interface
✔️ It supports frontend, backend, and infrastructure tasks across multiple languages

What I Dislike

❌ It doesn’t yet support GitLab, Bitbucket, or non-GitHub workflows
❌ Some generated code needed manual tweaks or cleanup
❌ Limited ability to handle very large or legacy repos
❌ No offline mode or local deployment options available

My Experience With Lovable

Lovable is a browser-based AI tool that integrates with GitHub and helps you build entire features, end to end.

Instead of writing a single function or line of code, you can ask it to “add login functionality” or “create a new payment webhook,” and it will generate the full code, update the necessary files, and submit a working pull request.

Getting Started

The setup process was simple and took less than five minutes. Here’s how it went:

• I signed in with my GitHub account
• Selected a repo to test with
• Lovable indexed the codebase in under a minute
• I typed a request like “Add password reset flow”
• Within two minutes, it proposed a plan and submitted a pull request

It was a refreshingly quick onboarding experience. I didn’t need to install a browser extension, VS Code plugin, or deal with long loading times.

The tool is entirely browser-based, and all updates are tracked through GitHub.

Features and Functionality

Lovable goes beyond traditional autocomplete or code suggestion tools. Here’s a breakdown of its most important features and how they performed during testing.

Repo-Aware AI

One of Lovable’s standout features is its repo context awareness. It doesn’t just guess at your file structure — it reads and understands your actual project.

This makes its responses far more relevant than generic ChatGPT prompts or autocomplete tools.

You can ask it things like:

• “Add pagination to the user list page”
• “Create a webhook for Stripe events”
• “Convert all class components to functional components”

It knows where your routes are defined, what frameworks you’re using, and how to stay consistent with your architecture.

Full Pull Requests

This is where Lovable shines. It builds the actual feature you request and submits a full pull request with:

• Code changes
• Test cases
• Updated configs
• README or changelog edits
• A description of the work and why it was done

I appreciated that it didn’t just dump code into a single file — it followed best practices, split logic into components or services, and even suggested naming conventions based on the rest of the codebase.

Real-World Requests I Tested

Task	Success?	Notes
Add forgot password flow	✅	Included backend route, frontend update, and token logic
Add dark mode toggle	✅	Worked well, but needed minor CSS adjustments
Create webhook for Stripe	✅	Integrated cleanly with existing payments module
Generate E2E tests	⚠️ Partial	Needed manual tweaks for test runner setup
Migrate legacy class components	✅	Converted React class components to hooks
Handle file uploads with S3	✅	Created config and handled uploads properly

How It Compares to Other Tools

There are a growing number of AI dev tools on the market, so how does Lovable hold up against the competition?

Lovable AI vs GitHub Copilot

Feature	Lovable AI	GitHub Copilot
Repo Context	✅ Full understanding	❌ Local file only
IDE Integration	❌ Browser-only	✅ Full VS Code plugin
Pull Requests	✅ Built-in	❌ Not supported
Feature Implementation	✅ End-to-end	⚠️ Code snippets only
Supported Languages	✅ Multiple (JS, TS, Go, Python, etc.)	✅ Similar

While Copilot is great for fast autocomplete and inline help, it’s not aware of your entire repo. Lovable is better suited for project-level tasks where multiple files and components are involved.

Lovable AI vs Cursor

Feature	Lovable AI	Cursor
Repo Context	✅ Full GitHub repo	✅ Local repo context
Pull Requests	✅ Auto-generated	✅ Manual submission
IDE Integration	❌ Browser-only	✅ VS Code plugin
Offline Support	❌ No	✅ Yes
Best For	Fast GitHub-based dev work	In-editor coding workflows

Cursor is a strong competitor with a more native IDE integration, but Lovable is cleaner for GitHub-first workflows.

Cursor shines when you want to stay inside VS Code, while Lovable is best for developers who prefer to work directly in GitHub.

Pricing: How Much Does Lovable AI Cost?

Lovable offers a flexible pricing structure, starting with a generous free plan and scaling up based on team needs.

What sets it apart is that pricing is shared across unlimited users, which makes it more cost-effective for collaborative teams compared to per-seat billing models.

Here’s a breakdown of Lovable’s current pricing plans:

Plan	Price (USD)	Best For	Key Features
Free	$0/month	Individuals or public projects	5 daily credits (up to 30/month), unlimited collaborators, public projects
Pro	$25/month (shared)	Small teams moving fast	Everything in Free, plus 100 monthly credits, credit rollovers, private projects, user roles, remove Lovable badge
Business	$50/month (shared)	Growing teams and departments	All Pro features, plus SSO, personal projects, opt-out of training data, design templates
Enterprise	Custom pricing	Large organizations	All Business features, plus onboarding support, custom design systems, group access controls, custom integrations

All plans are billed annually, and credits are what power your usage — whether that’s generating features, submitting pull requests, or making updates to your codebase.

Is It Worth It?

For solo developers or small teams, the Pro plan offers excellent value.

Unlike tools that charge per user, Lovable’s pricing is shared across your entire team, meaning even the $25/month plan can support multiple devs working together in real time.

The free plan is also robust enough to let you test the platform’s core features before upgrading.

Limitations of Lovable

No tool is perfect, and Lovable has its weak points. Here’s where I ran into trouble:

Large Repos

When I connected a monorepo with over 1,500 files, things slowed down. It took several minutes to index, and some requests timed out.

Smaller projects (under 300 files) worked great, but Lovable isn’t yet ideal for massive enterprise repos.

UI and UX

While the browser interface is clean, it’s fairly minimal. There’s no dark mode, no native GitHub PR viewer, and no settings to customize how the AI behaves.

It would be helpful to see:

• Confidence scores
• Suggested alternative implementations
• A history of previous conversations and PRs

Framework Gaps

Lovable works well with mainstream stacks (React, Express, Flask, Django), but it struggled with:

• Niche frameworks
• Custom build systems
• Proprietary middleware

This isn’t unexpected, but it means you’ll need to manually review and edit PRs in those cases.

Security and Privacy

Lovable connects via GitHub OAuth and doesn’t store your code outside of your own GitHub repo.

All code generation happens via their cloud-based AI, but the pull requests are written directly to your project, not their servers.

Here’s what they currently promise:

• No persistent storage of your code
• Encrypted connections at all times
• Optional repo deletion after session ends

I didn’t see any signs of telemetry or usage tracking within my repo, which was reassuring.

Still, I wouldn’t connect Lovable to sensitive client work or private enterprise code without clearing it with legal or compliance teams.

Use Cases and Who It’s Best For

Lovable is ideal for:

• Solo developers building MVPs
• Startup teams trying to ship faster
• Mid-level engineers who want to offload boilerplate
• Agencies building similar features across multiple projects

I don’t recommend it for:

• Large enterprises with compliance needs
• Codebases using non-GitHub source control
• Very design-heavy frontend projects

Final Verdict: Should You Use Lovable?

Lovable AI delivers on its promise: it helps you ship features, not just code snippets.

If you’re tired of AI tools that give you vague suggestions with no real understanding of your project, this is a tool worth trying.

It’s affordable, easy to use, and powerful enough to handle common feature requests without extra context.

While it’s still early in its development, it already offers more usable functionality than many of the better-known names in the AI dev space.

If you work in GitHub and want a sidekick that can actually build things — Lovable is one of the best tools available right now.