DBBridge analyzes your code, translates your schema through a canonical representation and migrates your data with checksum validation — from SQLite to PostgreSQL, from MySQL to MongoDB, from Oracle to Cassandra.
Common tools convert SQL strings and hope for the best. DBBridge parses your schema into a semantic Intermediate Representation (keys, constraints, relations, canonical types) and renders it into the target paradigm — tables become collections, FKs become graph relationships, indexes become partition keys.
Scans your code and flags every query that breaks on the target database.
Generates the transition plan with risks, limitations and a per-table strategy.
Applies schema and migrates data on the real target — with generated rollback.
Row-by-row content checksum. Not a row count: a proof of integrity.
Rewrites your code to the native driver and removes DBBridge. Zero lock-in.
SQLite ─┐ ┌─→ PostgreSQL
MySQL ──┤ ├─→ Snowflake
Oracle ─┼─→ Parser ─→ [ CANONICAL IR ] ─→ Renderer ─┼─→ MongoDB (docs)
SQL Server ┘ │ ├─→ Neo4j (graph)
▼ └─→ Cassandra / OpenSearch
Semantic planner + Validator (checksum)
This distinction is our public promise: everything the lib does today, it will do for free forever, under the MIT license. DBBridge Cloud exists for teams who want a UI, AI, collaboration and zero infrastructure — built on the same engine, hiding none of it.
Cloud's AI does one thing well: reconciling the migration plan — catching what mechanical translation misses. We use a single model optimized for this and publish the entire price formula, margin included. You are never charged for a call the AI did not answer.
Each AI request processes on average 10k input tokens (your schema + plan) and 2k output tokens (the advice). We pay the provider per token, add a flat 50% margin, done — that is the price. It is even available on a public API endpoint: GET /v1/billing/ai-pricing.
The opposite. DBBridge has a command called eject whose job is to remove DBBridge from your project: it rewrites your code to the target database's native driver and gets out of the way. The library is MIT — if we vanish tomorrow, your fork keeps working.
The engine is the same. The lib gives you everything via CLI/Python, free, on your machine — you operate it. Cloud gives you the same engine with a web UI, reconciliation AI, team projects, reports, API keys and managed jobs — we operate it. You choose how much of each you want.
Only if you ask (use_ai=true) — and what is sent is the plan/schema, not your table data. The data migration itself runs directly between source and target, with checksum validation. And the AI quota is only charged when the AI actually answered.
The Enterprise plan includes on-premises deployment of the full platform. And the library — which does all the migration — is always self-hosted by definition.
Yes, and it is validated against real instances — actual MongoDB, Neo4j, Cassandra and OpenSearch, not mocks. The public suite has 1,559 tests proving the round-trip with content checksums.
Start with the Cloud Free plan or run pip install dbbridgekit now.