Compare
Fei is for dev teams building real world products, not just MVP’s or short lived weekend projects
| Feature |  |  |
|---|---|---|
| Core Functionality | ||
| Code Generation Both tools can generate UI code from natural language prompts. AutonomyAI continues from there into deeper codebase integration. | ||
| Code Update AutonomyAI can modify real files in your repo while preserving structure and project standards. | Lovable does not support editing existing files in the codebase. | |
| Visual Output & Mocked Data Both platforms support visual previews and mock states. AutonomyAI emphasizes realistic multi-state simulation. | ||
| Inputs | ||
| Jira Integration AutonomyAI ties tasks to code, allowing structured delivery and traceability. | Lovable has no public support for task management or issue-tracking integration. | |
| Figma Integration Both platforms import designs from Figma. AutonomyAI maintains structural and interaction logic. | ||
| Codebase Integration | ||
| Codebase Integration AutonomyAI works directly in your repo with full version control support. | Lovable runs in its own cloud IDE. It does not sync directly with your existing repo structure. | |
| Custom Component Reuse AutonomyAI reuses your components for visual and structural consistency. | Lovable does not import or reuse your existing design system or component library. | |
| Component Reuse Across Screens AutonomyAI detects and reuses similar components across user flows. | Lovable does not track or reuse UI patterns across screens or projects. | |
| Custom Library Support AutonomyAI understands internal packages, utility functions, and hook usage. | Lovable does not ingest or model custom libraries or utilities. | |
| Guardrails from Project Configuration AutonomyAI respects your lint rules, TypeScript config, and project conventions out of the box. | Lovable does not align its output to your repo's existing rules or tooling. | |
| Enterprise | ||
| Built For Teams AutonomyAI supports collaboration, shared ownership, and delivery pipelines across team environments. | Lovable is optimized for solo builders and individual use—not team collaboration or review cycles. | |
| End-to-End Task Stitching AutonomyAI unifies prompt, design, task, and code into one cohesive workflow with traceability. | Lovable does not offer structured workflow stitching or task traceability across systems. | |
| Production-Grade Output AutonomyAI outputs clean, documented, and review-ready code intended for production deployment. | Lovable’s output is optimized for prototyping—not direct production use. | |
| Structured Work Summary AutonomyAI generates README-style summaries documenting what was done and what’s left to complete. | Lovable does not generate documentation, summaries, or structured meta around output. | |
Compare
Fei is for dev teams building real world products, not just MVP’s or short lived weekend projects
| Feature | |  |
|---|---|---|
| Core Functionality | ||
| Code Generation Both tools generate UI from natural language prompts. AutonomyAI builds into production-grade systems. | ||
| Code Update AutonomyAI works based off your actual codebase, delivering usable results not just the sandbox demoes. | V0 generates new code in isolation; it doesn't update existing files in real codebases. | |
| Visual Output & Mocked Data Both platforms offer visual previews. AutonomyAI adds data-state simulation and logic-aware mocking. | ||
| Inputs | ||
| Jira Integration AutonomyAI integrates with ticketing tools to link tasks with code changes. | V0 has no native integration with Jira or other task managers. | |
| Figma Integration Both tools support Figma import. AutonomyAI extends it with layout fidelity and reusable logic. | ||
| Codebase Integration | ||
| Codebase Integration AutonomyAI commits directly to your version-controlled repo. | V0 doesn’t operate inside your repo; output must be manually transferred. | |
| Custom Component Reuse AutonomyAI reuses your components and respects existing structure. | V0 does not map or reuse your component library; outputs are always fresh. | |
| Component Reuse Across Screens AutonomyAI identifies repeating UI logic and patterns. | V0 generates isolated screens without pattern linking or reuse. | |
| Custom Library Support AutonomyAI uses your internal helpers, hooks, and libraries during generation. | V0 doesn't understand or integrate with your custom code modules. | |
| Guardrails from Project Configuration AutonomyAI respects your config, including ESLint, Prettier, and TS rules. | V0 has no repo-aware linting or configuration alignment. | |
| Enterprise | ||
| Built For Teams AutonomyAI supports PR flow, handoffs, team collaboration, and role separation. | V0 is focused on individual creators or demos, not team workflows. | |
| End-to-End Task Stitching AutonomyAI connects prompts to code, designs, and tickets in one chain. | V0 does not link its output to task context or user stories. | |
| Production-Grade Output AutonomyAI generates reviewable, mergeable, and scalable code artifacts. | V0’s code is demo-friendly but requires rework before production use. | |
| Structured Work Summary AutonomyAI outputs a readable summary of changes and reasoning per task. | V0 doesn't document output or rationale for the code it generates. | |
Compare
Fei is for dev teams building real world products, not just MVP’s or short lived weekend projects
| Feature | |  |
|---|---|---|
| Core Functionality | ||
| Code Generation Both platforms allow natural language to code workflows. AutonomyAI is tailored to UI components; Replit focuses on general-purpose coding via Ghostwriter and Agent. | ||
| Code Update AutonomyAI updates real files in your repo. Replit Agent can modify code but lacks structured, config-aware updates. | ||
| Visual Output & Mocked Data Both platforms support previews. AutonomyAI simulates multi-state UI; Replit emphasizes deployment and live view. | ||
| Inputs | ||
| Jira Integration AutonomyAI links tasks to code for delivery traceability. Replit has no native Jira support. | ||
| Figma Integration AutonomyAI imports structured UI from Figma. Replit does not offer design file ingestion. | ||
| Codebase Integration | ||
| Codebase Integration AutonomyAI works inside your repo. Replit uses its own cloud-based environment with GitHub syncing. | ||
| Custom Component Reuse AutonomyAI reuses and references your design system. Replit doesn’t support reusable UI components out of the box. | ||
| Component Reuse Across Screens AutonomyAI maps UI patterns across flows. Replit doesn’t detect or leverage screen-level UI reuse. | ||
| Custom Library Support AutonomyAI supports hooks and shared logic. Replit treats all code as project-local unless manually managed. | ||
| Guardrails from Project Configuration AutonomyAI respects linter and build settings. Replit doesn’t enforce config-based safety rules across runs. | ||
| Enterprise | ||
| Built For Teams AutonomyAI enables role-based workflows and team collaboration. Replit Teams offers multi-user support but lacks deeper enterprise workflows. | ||
| End-to-End Task Stitching AutonomyAI connects design, task, and implementation. Replit Agent can scaffold projects but lacks structured traceability. | ||
| Production-Grade Output AutonomyAI outputs clean, deployable code. Replit can deploy code but has had incidents where AI suggestions caused critical errors. | ||
| Structured Work Summary AutonomyAI documents what was done and why. Replit Agent doesn’t produce structured logs or summaries per run. | ||
Compare
Fei is for dev teams building real world products, not just MVP’s or short lived weekend projects
| Feature | |  |
|---|---|---|
| Core Functionality | ||
| Code Generation Both tools generate UI from natural language prompts. AutonomyAI extends the process into your actual codebase. | ||
| Code Update AutonomyAI edits live repo files with full structural awareness. | Base44 lacks persistent context and doesn’t reliably update existing files in structured repos. | |
| Visual Output & Mocked Data Both platforms support visual previews. AutonomyAI adds dynamic state modeling. | ||
| Inputs | ||
| Jira Integration AutonomyAI connects tickets to code delivery. | Base44 lacks built-in Jira or issue tracker integration, requiring manual context stitching. | |
| Figma Integration Both tools import from Figma. AutonomyAI preserves interaction logic and naming conventions. | ||
| Codebase Integration | ||
| Codebase Integration AutonomyAI reads your and writes based on your existing infra and components | Base44 outputs must be copied manually; no direct codebase sync. | |
| Custom Component Reuse AutonomyAI references and reuses your existing components. | Base44 lacks awareness of your existing component library. | |
| Component Reuse Across Screens AutonomyAI remembers design patterns and applies them throughout your app. | Base44 treats every screen as a fresh task without context reuse. | |
| Custom Library Support AutonomyAI understands and applies your internal utilities and shared libraries. | Base44 doesn’t reuse or reference custom libraries from your codebase. | |
| Guardrails from Project Configuration AutonomyAI adheres to your ESLint, TSConfig, and naming rules. | Base44 does not follow your repo’s configuration or code quality rules. | |
| Enterprise | ||
| Built For Teams AutonomyAI is designed for collaborative work environments. | Base44 was built for solo makers; lacks multi-user support. | |
| End-to-End Task Stitching AutonomyAI merges prompt, design, planning, and code in a single workflow. | Base44 doesn’t connect prompt-to-code to task or design context. | |
| Production-Grade Output AutonomyAI delivers merge-ready, linted, testable code. | Base44 outputs raw, often untested code intended for demo use. | |
| Structured Work Summary AutonomyAI provides a clear, human-readable log of what was done. | Base44 does not output explanations, rationales, or logs alongside code. | |
AutonomyAI is creating a real impact when it comes to managing your R&D. The focus on delivering production grade results is helping companies move faster and more efficiently"
AutonomyAI helped us move faster as a business and increase our production velocity in a very competitive market”
AutonomyAI’s leadership team’s vision represents a fundamental shift in how organizations use AI, which will soon become the new standard.”
Using AutonomyAI helped me focus on the main things in my workflow and accelerate the front end development”
Real code for real products, faster than ever