# How to Vibe Code Your Own Kaiber (and Stop Paying for It) > AI creative studio and video generation platform for musicians and visual artists - Site: https://kaiber.ai - Category: AI Video Generation Platform - Verdict: **Serious undertaking** (35/100 vibecodeable) - Estimated effort: 6-8 weeks of part-time work ## Verdict Build a personal subset with a local web stack and fal.ai, but replicate the full cloud GPU orchestration pipeline only if you have months to spare. Replicating Kaiber's core loop—prompting a diffusion model and syncing output frames to an uploaded audio track—is achievable for a solo developer by leveraging serverless GPU APIs like fal.ai. However, building an infinite node canvas like Superstudio, writing robust backend queue workers for async video rendering, and implementing precise audio-reactive DSP parsing require handling complex async state management and infrastructure plumbing that will consume weeks of frustrating debugging. ### What you can't replicate - Proprietary fine-tuned custom model weights and stylistic pipelines - Enterprise audio processing infrastructure scaled for millions of users - Artist network effects and high-profile industry partnerships ## What it does An infinite node-based workspace and suite of generative AI tools for audio-reactive video creation, text-to-video, and frame-by-frame visual sequencing. ### Core features - Infinite node-based canvas for chaining video, image, and audio assets - Audio-reactive beat synchronization and transient detection - Text-to-video and image-to-video inference pipelines (Stable Diffusion / AnimateDiff / ControlNet) - Timeline video editor for trimming and sequencing frames - Asynchronous GPU queueing and job orchestration - Credit-based usage accounting system ## The business ### Pricing - Starter: $10/mo — Basic credit allotment for hobbyist video generation. - Creator: $29/mo — Full feature tier for independent musicians and artists. - Pro: $99/mo — High-volume generation for professional production work. Founded 2022. Team size: 16 to 24 employees. ## The hard parts - Asynchronous GPU inference orchestration for heavy diffusion workloads without HTTP timeouts - Digital signal processing (DSP) audio analysis to map beats to precise generation frames - Infinite responsive node graph canvas architecture with real-time state synchronization - Managing variable inference costs and queue degradation under high load ## How to vibe code Kaiber ### Prerequisites - Node.js (free): Required runtime for the Next.js frontend and build tools - GitHub (free): Source control and deployment pipeline integration - fal.ai Account (Pay-per-generation (~$10-20 starting credits)): Provides hosted serverless GPU inference for AnimateDiff and Stable Diffusion pipelines ### Recommended AI tools - Claude Code: Best-in-class multi-file agentic coding tool for scaffolding the node canvas and API integration layer - Cursor: Ideal for iterative UI work on the React Flow node canvas and video timeline editor ### Stack - Frontend: Next.js - Backend: Next.js API Routes / Server Actions - Database: Turso - Auth: better-auth - Payments: none - Other: React Flow, fal.ai API, librosa (Python microservice for audio beat analysis), Vercel AI SDK ### Hosting - Vercel (Hosting the Next.js frontend and serverless API endpoints): $0/mo (Hobby Tier) - Fly.io (Hosting the lightweight Python DSP microservice for audio beat extraction): ~$3-5/mo ### Build guide 1. **Scaffold Next.js App and Database Schema** — Initialize the Next.js project with Tailwind CSS, configure Turso for SQLite storage, and set up better-auth for single-user authentication. ``` Initialize a Next.js project using App Router and Tailwind CSS. Configure Turso (libSQL) as the database using Drizzle ORM. Set up better-auth with email/password authentication. Create database schemas for users, projects, nodes, and generations tracking job status (pending, processing, completed, failed). Ensure all environment variables are documented in a .env.example file. ``` 2. **Build the Node-Based Canvas Workspace** — Implement a node graph workspace using React Flow where users can create prompt, audio, and generation nodes and connect them together. ``` Install React Flow and build an infinite canvas workspace page in Next.js. Create custom node types for 'Prompt Node', 'Audio Node', and 'Generation Node'. Implement drag-and-drop node creation, connection edge handlers, and local state persistence to Turso so users can save and load canvas graphs. ``` 3. **Deploy Audio Beat Analysis Microservice** — Create a Python microservice using FastAPI and librosa to analyze uploaded audio files and extract transient beat timestamps. ``` Create a FastAPI microservice in Python packaged with Docker to handle audio analysis. Implement an endpoint that accepts an audio file upload, processes it using librosa to detect downbeats and transient peak timestamps, and returns a structured JSON array of timestamp markers. Deploy this service to Fly.io with a persistent volume for temporary file storage. ``` 4. **Integrate fal.ai Video Generation Pipeline** — Connect backend server actions to fal.ai endpoints for Stable Diffusion and AnimateDiff inference, passing prompt parameters and audio beat cues. ``` Write a server-side service in Next.js that communicates with the fal.ai API to trigger AnimateDiff and Stable Diffusion video generations. Implement an asynchronous polling mechanism to check job statuses and update the Turso database when video frames are rendered. Handle API errors gracefully and store generated video URLs in Turso. ``` 5. **Build the AI Video Editor Timeline** — Construct a timeline-based UI component allowing users to preview, trim, sequence, and arrange generated video clips and audio tracks together. ``` Build a timeline editor component in React for the frontend workspace. Allow users to arrange generated video clips chronologically alongside an audio waveform track. Implement video playback controls, track trimming, and a final export mechanism that stitches video clips together using client-side WebCodecs or server-side FFmpeg. ``` 6. **Polish UI, Error Handling, and Credit Tracking** — Implement a credit accounting ledger, polish dark-mode studio aesthetics, and add comprehensive error boundaries and toast notifications. ``` Add a credit tracking ledger to the Turso database and deduct credits per generation run based on video length. Implement a dark-mode creative studio UI theme across all canvas and editor components using Tailwind CSS. Add toast notifications for generation progress and error handling for failed API requests. ``` ### Cost vs paying **Starting costs (one-time):** - fal.ai API starting credits: $15.00 - Custom domain (optional): $12.00/yr - Total: ~$27.00 one-time **Ongoing costs (monthly):** - Vercel Hosting: $0/mo - Fly.io Microservice VM: ~$3.00/mo - fal.ai Generation Usage: ~$10.00/mo (pay-per-gen) - Total: ~$13.00/mo - Paying for the SaaS instead: $29/mo (Creator Plan) - Build time: 35-50 hours - AI tool credits: $20 (Claude Pro / Cursor) - Break-even: Never (built for personal creative use and learning) ## Sources - [Kaiber Plans & Pricing Breakdown](https://kaiber.ai/pricing) - [Business Wire - Kaiber Funding & Superstudio Launch Announcement](https://www.businesswire.com) - [GetLatka - Kaiber Revenue, Valuation & Team Size Data](https://getlatka.com)