Why to choose PostgreSQL over MongoDB in 2026
Source: Dev.to
Introduction
For a long time, MongoDB was the default choice when applications needed flexibility or scale. The argument was simple: SQL databases are rigid, NoSQL databases scale better. That argument no longer holds true in 2026. Modern PostgreSQL supports structured schemas, semi‑structured data, vector search, advanced indexing, and complex queries, all in one system. In many real production systems, using PostgreSQL alone avoids the need for MongoDB entirely.
Data Consistency Matters
PostgreSQL enforces:
- Strong data types
- Primary and foreign keys
- Constraints and relationships
This prevents invalid data from entering the system in the first place.
Real‑World Example: Merlin
In one of our systems (Merlin, an AI‑driven real‑estate search platform used by 60K+ active users), PostgreSQL was the source of truth for:
- Properties
- Canonical locations
- Images
- Metadata relationships
With millions of records and daily ingestion of ~120K properties, schema enforcement helped catch errors early instead of letting bad data silently propagate.
JSONB: Flexible Yet Structured
A common reason for choosing MongoDB is storing flexible or evolving data. PostgreSQL already solves this with JSONB. You can store:
- Dynamic attributes
- Search filters
- Derived metadata
- Optional fields
…directly inside relational tables.
Search Backend Implementation
- Core entities (property, location, image) were modeled relationally.
- Flexible attributes and filters were stored using JSON/JSONB.
- GIN indexes were used to query JSON fields efficiently.
This allowed us to:
- Avoid unnecessary JOINs on hot paths
- Keep strong consistency for critical data
- Avoid running a separate NoSQL database just for flexibility
JOINs and Performance
JOINs are often blamed for performance issues. In our system:
- Complex JOINs were used where correctness mattered.
- JSONB and caching were used where read performance mattered.
- Redis JSON was used for fast hybrid search paths.
This approach reduced query complexity without duplicating core data everywhere.
Normalization vs. Denormalization
MongoDB often pushes developers to denormalize aggressively, leading to:
- Data duplication
- Hard‑to‑maintain update logic
- Eventual consistency bugs
PostgreSQL gives you more control: normalize first, optimize selectively.
SQL’s Cognitive Advantage
SQL allows you to:
- Express complex relationships clearly
- Debug queries easily
- Optimize step by step
- Reuse logic across services
When latency increased in our early search system, we didn’t rewrite the data model; we changed the architecture:
- Relational filtering → PostgreSQL
- Vector similarity search → pgVector / Redis
- Keyword and fuzzy matching → Elasticsearch
Result: query latency dropped from ~5 minutes to ~10 seconds while SQL remained readable and maintainable. MongoDB would require aggregation pipelines with multiple stages ($lookup, $unwind, $group), which become harder to reason about as complexity grows.
AI Workloads and Vector Search
There’s a common assumption that AI workloads require NoSQL databases. In reality:
- PostgreSQL + pgVector handled 1.2M+ image embeddings.
- Vector similarity was separated from relational queries.
- Multi‑stage retrieval (keyword → vector → re‑ranking) improved both performance and relevance.
PostgreSQL acted as:
- The source of truth
- The filter engine
- The relational backbone
Specialized systems handled specialized workloads without replacing PostgreSQL.
Architecture Example: Location Search
We stored canonical locations in PostgreSQL and used Elasticsearch only for fuzzy user‑input matching. User‑entered locations were mapped to canonical IDs, which were then used for downstream queries. This eliminated:
- Repeated database calls
- Duplicate location representations
- Inconsistent location data
Relational constraints ensured correctness; MongoDB would push this logic into application code instead.
Reducing Stack Complexity
Every additional database increases:
- Infrastructure cost
- Monitoring overhead
- Consistency challenges
- Developer cognitive load
In our backend, PostgreSQL remained the primary database. Redis and Elasticsearch were supporting systems, not sources of truth. MongoDB was never required.
PostgreSQL in 2026: Feature Summary
PostgreSQL is no longer “just a relational database”. It provides:
- Strong schemas for production safety
- JSONB for flexible data
- Advanced indexing (GIN, BRIN, etc.)
- Vector search support via pgVector
- Powerful SQL for complex queries
For many real‑world systems, PostgreSQL can replace MongoDB entirely while reducing risk, cost, and long‑term maintenance.
Conclusion
MongoDB still has valid use cases, but it should be a deliberate choice and not a default one. In 2026, reducing stack complexity often outweighs adding another database optimized for a narrow use case. PostgreSQL’s evolving ecosystem makes it a compelling, all‑in‑one solution for modern applications.