Tier 2
Build MongoDB-style data layers with AI
Orbit maps MongoDB-style document requests to Postgres — flexible JSONB columns give document-like flexibility with relational integrity and RLS on top.
MongoDB AI Builder
A MongoDB-style request is modeled in Postgres on Orbit: JSONB columns preserve the document feel, while tables, indexes, and RLS give you a relational backbone with real security.
In plain English
MongoDB stores data in flexible documents. Orbit models document-style data in Postgres — same flexibility, plus relational integrity and real security.
How it works in Orbit
MongoDB's flexible documents are great for evolving schemas. Orbit maps document-style data needs onto Postgres with JSONB — flexible documents where you need them, relational power where you don't.
Flexible documents via JSONB
Sparse and changing fields fit naturally in Postgres JSONB columns. Orbit models your spec as documents when the shape is flexible, and structured tables when it isn't.
- JSONB for flexible shapes
- Indexed document queries
- Hybrid table + document model
Query flexibility
Postgres JSON operators cover the queries Mongo users rely on, from nested lookups to array membership, and they run alongside normal SQL joins.
- JSON path queries
- Indexed lookups
- Joins across documents and tables
Why Postgres instead
Mongo's operational tradeoffs — manual replication, eventual consistency — are handled for you by managed Postgres, while documents still work the way your product needs.
- Managed replication
- Strong consistency
- One database for all data
Spec flexible data
Describe records that change shape.
Generate documents
Orbit models them as JSONB on Postgres.
Query naturally
Use document operators and joins.
Scale managed
Rely on managed replication and backups.
The challenge
Try it yourself
Run this one on a competitor first — then on Orbit. Same prompt, and keep score.
Build
Try this one elsewhere first.
Store the same messy, nested data on both backends and try to query it. Compare who bends without breaking.
Research
Ask a research tool to go deep on one topic.
Then ask Orbit. Compare how many rounds it runs, how many sources it actually reads — we target 65+ — and whether every claim in the final report has a link attached. Bring a bigger coffee mug.
Chat
Ask a plain chatbot the same question.
One just talks. Orbit's agent actually searches the web, runs code, reads your files, and comes back with something done. Compare how many tabs you opened and how many are still open when you're finished.
Image & video
Generate the same image, then edit it.
Ask two tools for the same hero shot, then ask both to recolor it, fill in the background, and animate it. Compare who hands you a first draft and who keeps refining in the same conversation.
150+
models routed per task
1h
cloud sandbox sessions
25
tool calls per chat turn
8–14
deep research rounds
What you can do with MongoDB AI Builder on Orbit
Schema from your spec
Tables, indexes, and relations generated as idempotent SQL.
Baseline RLS
Row-level security policies created with every table.
Live data panel
Browse tables, run SQL, and manage migrations from Orbit Cloud.
Compare sibling stacks
Supabase AI Builder
Orbit generates fullstack apps with Supabase built in: schema, row-level security, auth, storage, and edge functions provisioned automatically, with the client configured via VITE_SUPABASE_URL and VITE_SUPABASE_SCHEMA.
Firebase AI Builder
Orbit maps Firebase-style realtime backends to Supabase — auth, realtime data, storage, and serverless functions with an equivalent developer experience and RLS-based security.
Appwrite AI Builder
Orbit generates apps with Appwrite-style backend features — auth, databases, storage, functions — on its provisioned Supabase backend, pre-secured with row-level security.
PocketBase AI Builder
Orbit generates apps with PocketBase-style simplicity — instant backend, auth, and file uploads — on Supabase, with RLS and a managed cloud panel instead of self-hosting.
Prisma AI Builder
Orbit turns Prisma-style schema requests into Supabase SQL — the data layer of your app generated as idempotent migrations with RLS policies, instead of an ORM you maintain.
Postgres AI Builder
Orbit generates fullstack apps on Postgres via Supabase — the relational database your spec describes, with RLS, realtime, and the ability to run SQL directly from the panel.
Explore more
Supabase AI Builder
Orbit generates fullstack apps with Supabase built in: schema, row-level security, auth, storage, and edge functions provisioned automatically, with the client configured via VITE_SUPABASE_URL and VITE_SUPABASE_SCHEMA.
Firebase AI Builder
Orbit maps Firebase-style realtime backends to Supabase — auth, realtime data, storage, and serverless functions with an equivalent developer experience and RLS-based security.
Appwrite AI Builder
Orbit generates apps with Appwrite-style backend features — auth, databases, storage, functions — on its provisioned Supabase backend, pre-secured with row-level security.
PocketBase AI Builder
Orbit generates apps with PocketBase-style simplicity — instant backend, auth, and file uploads — on Supabase, with RLS and a managed cloud panel instead of self-hosting.
Prisma AI Builder
Orbit turns Prisma-style schema requests into Supabase SQL — the data layer of your app generated as idempotent migrations with RLS policies, instead of an ORM you maintain.
Postgres AI Builder
Orbit generates fullstack apps on Postgres via Supabase — the relational database your spec describes, with RLS, realtime, and the ability to run SQL directly from the panel.
Frequently asked questions
Can Orbit handle document-style data?
Yes — JSONB in Postgres preserves the flexible, document-like storage you'd get from MongoDB, with the added benefits of SQL and RLS.
Do I lose MongoDB features on Postgres?
Common document patterns — nested data, arrays, flexible fields — work in JSONB. If you need a MongoDB server specifically, the app's data layer can be repointed.
Can I keep my MongoDB-style document model?
Orbit maps flexible, document-shaped data onto Postgres JSONB columns, so schemaless-looking documents still work. You keep the flexibility of MongoDB-style shapes with the safety and relational power of Postgres underneath.