Locofy vs Vellum
Compare coding AI Tools
Design-to-code platform that converts Figma or Penpot designs into production-ready React, Next.js, React Native, Flutter, Vue and more with AI assisted tagging and layout.
Vellum is an AI agent building platform that combines a prompt playground, evaluation tools, and hosted agent apps so teams can iterate on LLM workflows with debugging and knowledge base support, starting with a free tier and upgrading for more credits.
Feature Tags Comparison
Key Features
- Figma and Penpot plugins to map layers variants and interactions
- AI assisted semantic tagging grouping and layout constraints
- Exports for React Next.js React Native Flutter Vue HTML/CSS
- Design tokens breakpoints and responsive controls
- Component reuse and code sync with GitHub integration
- State props and events mapped from design for real behavior
- Free and Pro plans: Pricing starts at $0 with 50 credits and Pro at $25 with 200 builder credits so solo builders can scale testing
- Prompt playground: Compare models side by side and iterate prompts systematically instead of relying on subjective testing
- Evaluations framework: Run repeatable quality tests at scale to detect regressions and track improvements across prompt versions
- Hosted agent apps: Share working agents with teammates through hosted apps for demos
- reviews
- and stakeholder feedback cycles
Use Cases
- Design handoff where engineers start from generated code not redlines
- Greenfield apps bootstrapped with consistent components and tokens
- Mobile apps with React Native or Flutter scaffolds from the same design
- Landing pages and sites that go live faster with clean HTML/CSS
- Design system rollouts where components map to code libraries
- Rapid prototyping with interactive exports for stakeholder testing
- Agent prototyping: Build an agent by chatting with AI then refine logic with low code steps and controlled prompt versions
- Prompt iteration: Compare LLM outputs side by side and select prompts that improve accuracy and reduce unwanted variation
- Regression testing: Run evaluations on a saved dataset before release to catch quality drops after model or prompt changes
- RAG apps: Attach a knowledge base and test retrieval behavior with representative questions and strict document scope rules
- Stakeholder demos: Publish hosted agent apps so product and compliance reviewers can test behavior without local setup steps
- Model selection: Evaluate providers and self hosted options with the same tasks to choose the best cost and latency mix for production
Perfect For
front-end engineers designers tech leads and agencies who want reliable design-to-code with framework choices and AI assistance
product managers, ML engineers, software engineers, data scientists, AI platform teams, prompt engineers, QA and reliability teams, startups building LLM features, teams shipping agent workflows
Capabilities
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