DEV Community

Cover image for Why to choose PostgreSQL over MongoDB in 2026
Suraj Sharma
Suraj Sharma

Posted on

Why to choose PostgreSQL over MongoDB in 2026

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.

This article explains why PostgreSQL is often a better choice than MongoDB today, based on real backend systems running at scale.

Structured schema (because it matters in production)

In production systems, data consistency matters more than initial flexibility.

PostgreSQL enforces:

  • Strong data types
  • Primary and foreign keys
  • Constraints and relationships

This prevents invalid data from entering the system in the first place.

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.

MongoDB’s schemaless model pushes validation to the application layer. This works initially, but as systems grow, different document shapes start appearing for the same logical entity. Debugging and migrating this later is painful.

JSONB for unstructured data (without losing relational guarantees)

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.

In our search backend:

  • 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

Instead of choosing either relational or document storage, PostgreSQL lets you combine both.

Minimize JOINs where needed (without denormalizing everything)

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.

MongoDB often pushes developers to denormalize aggressively, which leads to:

  • Data duplication
  • Hard-to-maintain update logic
  • Eventual consistency bugs

PostgreSQL gives you more control: normalize first, optimize selectively.

Complex SQL queries are an advantage

PostgreSQL’s biggest strength is still SQL.

SQL allows you to:

  • Express complex relationships clearly
  • Debug queries easily
  • Optimize step by step
  • Reuse logic across services

Early versions of our search system relied on complex SQL queries across multiple tables. When latency increased, we didn’t rewrite the data model but we changed the architecture.

We separated:

  • Relational filtering (PostgreSQL)
  • Vector similarity search (pgVector / Redis)
  • Keyword and fuzzy matching (Elasticsearch)

The result:

  • Query latency dropped from ~5 minutes to ~10 seconds
  • 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.

SQL scales better cognitively than aggregation pipelines.

PostgreSQL is AI-ready (without MongoDB)

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.

This architecture scaled to ~10K searches per day while keeping infrastructure costs under control.

Canonical data modeling works better in relational databases

Location search is a good example.

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.

Fewer systems, lower operational 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 and not sources of truth.

MongoDB was never required.

In 2026, reducing stack complexity is often more valuable than adding another database optimized for a narrow use case.

Conclusion

PostgreSQL in 2026 is no longer “just a relational database”.

It provides:

  • Strong schemas for production safety
  • JSONB for flexible data
  • Advanced indexing
  • Vector search support
  • Powerful SQL for complex queries

For many real-world systems, PostgreSQL can replace MongoDB entirely while reducing risk, cost, and long-term maintenance.

MongoDB still has valid use cases, but it should be a deliberate choice and not a default one.

Top comments (0)