# How to Vibe Code Your Own Baserow (and Stop Paying for It) > The open-source no-code database and application builder - Site: https://baserow.io - Category: Developer Tools - Platforms: Web app, Docker container - Verdict: **Serious undertaking** (42/100 vibecodeable) - Estimated effort: 6+ weeks of focused development ## Verdict Build a personal read-only grid clone in a weekend, but keep paying if you need dynamic multi-tenant database migrations, real-time WebSockets, and field-type plugins. Baserow is an enterprise-grade open-source platform built over six years by a dedicated team using Django, PostgreSQL, and Vue. Vibecoding a personal single-user clone of the core database grid and basic field types is achievable, but building the dynamic meta-programming layer that translates UI-defined database columns into real PostgreSQL table alterations on the fly is an architectural trap. AI coding tools will hallucinate schema management logic and break WebSocket state synchronization during real-time concurrency tests. If you want a spreadsheet database for personal use, use an existing tool or deploy the official Docker container. ### What you can't replicate - Enterprise compliance certifications (SOC 2 Type II, HIPAA, GDPR) - Six years of edge-case hardening on dynamic DDL migrations - Extensive ecosystem of native third-party integrations and plugins ## What it does An open-source, no-code database and application builder designed as an alternative to Airtable, featuring collaborative grids, galleries, kanban boards, application builders, and automated workflows. ### Core features - Dynamic schema builder with custom fields (text, number, date, select, relations) - Spreadsheet-like grid, gallery, and kanban board views - Dynamic data filtering, sorting, and grouping engine - Real-time collaborative cell editing and presence indicators - Visual No-Code Application Builder for custom portals - Automation engine with triggers and actions (webhooks, email) ## The business ### Pricing - Free: $0 - Premium: $10/mo - Advanced: $18/mo - Enterprise: Custom ### Funding $5.16M raised. - Seed Round (July 2022): €5M led by Inkef Investors: Inkef, firstminute capital, Job van der Voort, Martin Henk Founded 2019. Team size: 15-21. ## The hard parts - Dynamic schema-to-database mapping (translating user-created fields into live PostgreSQL DDL operations safely) - Real-time collaborative sync with conflict resolution using WebSockets - Performant formula evaluation and cross-table lookup/rollup query engine - Extensible architecture for custom field types and UI plugins ## How to vibe code Baserow ### Prerequisites - Docker (free): Required to run local PostgreSQL and Redis instances for development - Python (free): Backend runtime environment for Django and Django REST Framework - Node.js (free): Frontend build tooling for the React or Vue interface - GitHub (free): Source code management and version control ### Recommended AI tools - Claude Code: Best-in-class agent for multi-file backend and frontend scaffolding across a complex Django/React codebase - Cursor: Ideal for fine-tuning the dense spreadsheet UI, keyboard shortcuts, and cell navigation ### Stack - Frontend: Next.js with Tailwind CSS and TanStack Table - Backend: Django REST Framework - Database: PostgreSQL - Auth: better-auth - Payments: None (Personal use clone) - Other: Redis for Django Channels and WebSockets, Celery for background automation tasks ### Hosting - Fly.io (Running the full Django backend container, PostgreSQL, and Redis together via Docker Compose): ~$7-15/mo - Vercel (Hosting the Next.js frontend interface with zero-config edge deployments): $0/mo ### Build guide 1. **Initialize Django Project & Core Database Models** — Set up a Django project with Django REST Framework and PostgreSQL. Define models for Workspaces, Databases, Tables, and Fields using a JSON metadata structure to store user-defined column configurations. ``` Initialize a Django project named 'baserow_clone' with Django REST Framework and PostgreSQL configured in settings.py using environment variables. Create core models: Workspace, Database, Table, and Field. The Field model must include a 'text_sub_type' or 'field_type' string field and a 'options' JSONField to store custom configurations like select dropdown choices or number decimal places. Include standard UUID primary keys, timestamps, and foreign key relationships. Write comprehensive pytest unit tests verifying workspace creation, table nesting under databases, and dynamic field definition storage. ``` 2. **Build Dynamic Row Storage and CRUD API** — Implement a generic row storage architecture where user table data is safely ingested, validated against field metadata, and exposed through a high-performance REST API. ``` Implement a dynamic row storage architecture in Django for our no-code database. Create a 'Row' model linked to a Table, storing cell values in a flexible JSONField where keys correspond to Field UUIDs. Write a custom DRF Serializer and Viewset that validates incoming row mutations against the parent Table's defined Field schemas (e.g., ensuring number fields contain valid numbers, date fields match ISO formats). Add API endpoints for bulk row creation, filtering, sorting, and pagination. Write integration tests covering invalid type submissions and multi-field filtering. ``` 3. **Scaffold Next.js Spreadsheet Grid Interface** — Create a responsive frontend spreadsheet grid using Next.js, Tailwind CSS, and TanStack Table that mimics a collaborative data table. ``` Scaffold a Next.js frontend dashboard featuring a spreadsheet grid view using TanStack Table. Implement virtualized row scrolling, inline cell editing with optimistic UI updates, column header menus for sorting and filtering, and a toolbar to add new text, number, or selection fields. Ensure keyboard navigation (arrow keys moving between cells) functions smoothly. Connect the frontend grid state to the Django REST API endpoints created in previous steps with SWR or React Query for data fetching. ``` 4. **Implement Real-time WebSockets with Django Channels** — Add real-time collaborative editing support using Django Channels and Redis, broadcasting cell mutations to connected clients instantly. ``` Configure Django Channels and Redis layer in the backend to support real-time collaborative cell editing. Create a WebSocket consumer that accepts connections authenticated per workspace/table room. When a client updates a cell value via REST or WebSocket, broadcast the row update event to all other connected clients in the same table room. On the Next.js frontend, establish a WebSocket connection, handle live incoming cell update payloads, and flash modified cells to highlight live collaboration. Write tests verifying multi-client broadcast messages. ``` 5. **Add Kanban and Gallery View Alternatives** — Extend the frontend view engine to support card-based Kanban boards and visual Gallery cards alongside the primary grid view. ``` Extend the Next.js frontend to support multiple view tabs per table: Grid, Kanban Board, and Gallery. For the Kanban view, allow the user to select a single-select or status field as the grouping column, rendering drag-and-drop columns where cards update their group field upon dragging. For the Gallery view, render cover images and primary text attributes in a responsive card grid. Ensure view configurations are saved persistently in the database per table. ``` 6. **Implement Automation Engine and Background Tasks** — Set up Celery with Redis to execute automated workflows triggered by row creation or field updates, supporting webhook and email actions. ``` Implement an automation engine using Celery and Redis in the Django backend. Define models for Workflows, Triggers (e.g., 'Row Created'), and Actions (e.g., 'Send Webhook' or 'Send Email via Resend'). Create a signal handler or polling service that intercepts table mutations, evaluates active triggers against row payloads, and dispatches asynchronous Celery tasks to execute actions. Provide a simple API and frontend configuration modal to set up and inspect automation execution logs. ``` ### Cost vs paying **Starting costs (one-time):** - Custom domain (optional): $12/yr - AI coding tool subscription: $20 - Total: ~$32 one-time **Ongoing costs (monthly):** - Fly.io backend & database hosting: ~$10/mo - Total: ~$10/mo - Paying for the SaaS instead: $10/user/mo (Premium) - Build time: 60-90 hours - AI tool credits: $20 (1 month Claude Pro) - Break-even: 1 month of Premium vs hosting costs ## Sources - [Baserow Official Website](https://baserow.io) - [Baserow GitHub Repository](https://github.com/baserow/baserow) - [Baserow Pricing & Features](https://baserow.io/pricing) - [Baserow Documentation](https://baserow.io/docs)