Migration · MongoDB → PostgreSQL
MongoDB to PostgreSQL Migration Guide
MongoDB is fine for some workloads. For most product backends, PostgreSQL with JSONB columns gives the schema flexibility plus the relational rigour, the indexing, the joins and the operational maturity that Mongo doesn't. Here's how to migrate without downtime.
Why teams migrate
- Cross-document joins are killing read latency
- Schema drift is making bugs hard to trace
- Transactions across collections are getting complex
- Ops cost on managed Mongo is materially higher than managed Postgres
- Specific features (full-text search, geospatial, vector, time-series) are easier on Postgres extensions
Our migration approach
Phase 1 — Schema design with JSONB where it earns its keep
We model the relational core in Postgres with JSONB columns for genuinely document-shaped fields. Best of both worlds.
Phase 2 — Dual-write or change-data-capture
We dual-write to Mongo and Postgres for a period, or use CDC to stream changes. Both stores stay in sync during cutover.
Phase 3 — Read-path cutover
Reads switch to Postgres first — lower-risk than writes. We compare results between stores to catch any model drift.
Phase 4 — Write-path cutover and Mongo retirement
Once read parity is confirmed, writes cut over. Mongo is decommissioned after a verification period.
Pitfalls we've seen
- Don't migrate just because Mongo is unfashionable. Make a real TCO and feature case first.
- Don't try to perfectly relational-ise everything. JSONB exists for a reason.
- Plan for index re-build time on large datasets. Postgres indexes are not Mongo indexes.
- Test the application's query patterns. Some Mongo aggregations need careful Postgres equivalents.
Pricing and timeline
Price range
$20,000 – $60,000
USD, fixed-cost after written scope
Timeline
10 – 18 weeks
Phased rollout from kickoff to legacy retirement
FAQ
Are you anti-MongoDB?
No. Mongo is a fine choice for some workloads — high write throughput on document-shaped data, specific aggregation patterns, certain real-time products. We just see more teams reaching for it without need than the reverse.
What about vector search?
pgvector on Postgres handles vector search well for most product use cases. If you're at billion-vector scale or need very specialised retrieval, a dedicated vector DB might still be right. Most apps are fine on Postgres.
Can we do this with no downtime?
Yes, via dual-write or CDC. The cutover window is usually under 5 minutes for the final switchover. We've done several migrations with zero customer-facing downtime.
Considering this migration?
We'll scope it phase-by-phase and share a fixed-cost proposal within 48 hours. See the related service below for our standard api & cloud services approach.