Lovable vs Yao (2026): Which Vibe Coding App Is Better?
Both are popular AI app builders. Here is how Lovable and Yao stack up on price, strengths and weaknesses, and which one we would pick.
Lovable
Build full-stack web apps by chatting, with Supabase and one-click deploy
8.6 /10
Pricing: Free plan, paid from $25/mo
Pros
- Fastest path from idea to hosted app
- Backend, database and auth built in (Lovable Cloud, on Supabase)
- GitHub sync to hand over to developers
Cons
- Credits run out quickly while iterating
- Complex apps still need a developer
Yao
Self-hosted platform for managing AI agents and tasks across desktop, mobile and browser
6.5 /10
Pricing: Freemium
Pros
- Unified agent workspace reachable from desktop, mobile and browser against one backend
- Self-hosted/local-first option gives control over data and infrastructure
- Active project with a real commit history and community (8k stars, 4,200+ commits)
Cons
- Dual/non-standard licensing terms need review before commercial use
- Mobile client is explicitly beta-stage
Lovable vs Yao at a glance
| Lovable | Yao | |
|---|---|---|
| Editor score | 8.6 / 10 | 6.5 / 10 |
| Pricing model | Freemium | Freemium |
| Starting price | $25/mo | Free |
| Reader upvotes | 0 | 0 |
| Best for | app builder, no-code, web | self-hosted, task-board, multi-device |
| Agent Skills support | No | No |
| Subagents support | No | No |
| MCP support | Yes | No |
| AGENTS.md support | No | No |
| Plugins support | No | No |
| Hooks support | No | No |
| Rules support | No | No |
| Agent skills in this directory | 0 | 0 |
| Subagents & agents in this directory | 4 | 3 |
| MCP servers in this directory | 72 | 0 |
Our pick
Lovable edges it with a score of 8.6 versus 6.5. Lovable is the better fit if you value: fastest path from idea to hosted app, backend, database and auth built in (lovable cloud, on supabase). Choose Yao instead if you need: unified agent workspace reachable from desktop, mobile and browser against one backend, self-hosted/local-first option gives control over data and infrastructure.
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