Postgres JSONB vs MongoDB for App Data

For most app backends, Postgres with JSONB columns gives you the document flexibility people pick MongoDB for, plus relational joins, foreign keys and SQL in the same database. MongoDB is the better fit when nearly all your data is self-contained documents, when your team already thinks in its query language, or when you need built-in sharding across many machines from the start. The real Postgres JSONB vs MongoDB question is less about speed and more about which data model matches your app.

This article compares the two on the things that decide real projects: data modeling, querying, indexes, consistency, operations and cost. There is no benchmark here. Performance depends on your documents, indexes and hardware, so measure your own workload before trusting anyone's numbers, including ours.

#Quick comparison

MongoDB facts are from the MongoDB limits page and transactions docs as of November 2026.

#How the data models differ

#MongoDB: documents first

In MongoDB, an order with its line items, shipping address and status history is naturally one document. You read it in one round trip and update it atomically. MongoDB's own documentation recommends this: an operation on a single document is atomic, and embedding related data reduces the need for multi-document transactions. The trade-off appears when data is shared. A customer who places 200 orders either lives inside every order (and must be updated in 200 places) or in a separate collection that you join with $lookup.

#Postgres: tables first, documents where useful

In Postgres the same order is usually a row in orders with columns for the fields you filter on, a foreign key to customers, and line items in their own table. Where the shape really varies, such as product attributes that differ per category, a jsonb column holds it:

create table public.customers (
  id uuid primary key default gen_random_uuid(),
  email text not null unique
);

create table public.products (
  id bigint generated always as identity primary key,
  name text not null,
  category text not null,
  attrs jsonb not null default '{}'      -- varies per category
);

create table public.orders (
  id bigint generated always as identity primary key,
  customer_id uuid not null references public.customers on delete restrict,
  status text not null check (status in ('pending', 'paid', 'shipped')),
  shipping jsonb,                          -- free-form address
  created_at timestamptz not null default now()
);

You keep the guarantees of a relational schema (types, not null, foreign keys, unique emails) and still store messy data without migrations. The JSONB vs JSON guide explains why jsonb is the type to use.

#Querying

The same question in both:

Find paid orders from Germany that include product A-1.

MongoDB, with embedded items:

db.orders.find({
  status: 'paid',
  'customer.country': 'DE',
  'items.sku': 'A-1',
})

Postgres, with the whole document in a jsonb column of a table orders_doc (id bigint, data jsonb):

select id
from public.orders_doc
where data @> '{"status": "paid", "customer": {"country": "DE"}, "items": [{"sku": "A-1"}]}';

Both are concise. The difference shows in the next question: revenue per customer segment, joined with a marketing table, for the last 90 days. In Postgres that is a normal join and group by, with window functions and CTEs if you need them. In MongoDB it is an aggregation pipeline with $match, $lookup and $group stages. Both work; which one your team writes faster is a fair tie-breaker.

For the full set of JSONB operators, JSON_TABLE and update functions, see Querying JSONB in Postgres.

#Indexes

Both databases index nested fields and arrays.

• MongoDB indexes specific fields, including fields inside arrays (multikey indexes). You choose the fields up front.
• Postgres has two approaches. A GIN index covers every key in the document, so ad hoc containment and jsonpath queries are indexed without knowing the keys in advance. A B-tree expression index on one key is smaller and supports ranges and sorting.

create index products_attrs on public.products using gin (attrs jsonb_path_ops);
create index orders_country on public.orders_doc ((data #>> '{customer,country}'));

One real weakness on the Postgres side: the planner keeps statistics on columns, not on keys inside JSON. Filters on JSON keys can get poor row estimates. The fix is to promote frequently filtered keys to real or generated columns.

#Consistency and transactions

In Postgres every statement runs in a transaction, and a transaction can touch any number of rows and tables. Transferring stock between two warehouses, writing an order and its line items, or decrementing a balance and inserting a ledger entry are all ordinary begin ... commit blocks. Constraints and foreign keys are checked by the database, so bad data cannot slip in from a buggy client.

MongoDB supports multi-document transactions on replica sets (feature compatibility 4.0 and later) and sharded clusters (4.2 and later). Its documentation notes that distributed transactions usually cost more than single-document writes and should not replace good schema design. If your data embeds cleanly, you rarely need them. If it does not, you will use them often.

#Schema and validation

MongoDB lets you attach a $jsonSchema validator to a collection, with validation levels that control how existing documents are treated. Postgres checks column types always, and you can add rules to JSON columns:

alter table public.products
  add constraint attrs_object check (jsonb_typeof(attrs) = 'object'),
  add constraint weight_positive check (
    not (attrs ? 'weight_g') or (attrs ->> 'weight_g')::numeric > 0
  );

For full JSON Schema validation inside Postgres, the pg_jsonschema extension exists and is available on StitchDB (extensions list).

#Scaling

MongoDB has sharding in the server: you pick a shard key and the cluster spreads data across shards. If you know you will need to write terabytes across many machines, that is a genuine advantage.

Postgres scales a single primary vertically and reads horizontally with replicas. For most apps that is plenty for a long time; a well-indexed Postgres on one server handles a lot of traffic. Sharding Postgres is possible with extensions or by splitting tenants across databases, but it is more work than in MongoDB.

#Where each one is the better choice

Pick MongoDB when:

• Your data is mostly independent documents read and written as a whole (content, catalogs, event payloads, device state).
• You need built-in horizontal write scaling early.
• Your team knows the MongoDB query language and tooling well.
• You rely on MongoDB-specific features in Atlas that you already use.

Pick Postgres with JSONB when:

• Your data has relationships: users, teams, orders, invoices, permissions.
• You want constraints and foreign keys enforced by the database.
• You need reporting with joins and aggregates without exporting data elsewhere.
• You want one database for relational data, documents, full-text search and vectors (pgvector).

Many teams that start on MongoDB "for flexibility" end up modeling relations by hand. Many teams that start on Postgres end up with a few jsonb columns for the parts that really vary. Both outcomes are fine. Mixing the two models inside Postgres is usually less work than keeping two databases in sync.

#What StitchDB adds on top of Postgres

StitchDB gives each project its own Postgres database with the extras an app backend needs, so the comparison with MongoDB Atlas becomes a comparison of whole backends:

• Auto-generated REST and GraphQL APIs over your tables, with contains filters for JSON columns (Filtering, GraphQL).
• Row Level Security on every request, including Realtime and Storage (RLS docs).
• Auth, Storage, Realtime and Edge Functions in the same project.
• Read replicas on Pro and Scale (read replicas).

Trade-offs to be honest about: StitchDB regions are in the EU only (Germany and Finland), and it is a younger platform than MongoDB Atlas. If you need multi-region writes or global clusters, look at MongoDB Atlas or a distributed SQL database. For a deeper side-by-side, see StitchDB vs MongoDB Atlas.

Moving documents over is usually a small script: export each collection as JSON, create a table with an id and a data jsonb column, insert, then promote the keys you filter on into columns over time.

create table public.events_import (
  id text primary key,       -- MongoDB _id as text
  data jsonb not null
);
create index on public.events_import using gin (data jsonb_path_ops);

#FAQ

#Is Postgres JSONB faster than MongoDB?

There is no general answer. It depends on document size, indexes, read and write mix and hardware. Both can be fast for document lookups with the right index. Measure your own queries on realistic data before deciding.

#Can Postgres replace MongoDB completely?

For most app workloads, yes: jsonb stores and indexes documents, and you gain joins and constraints. MongoDB remains a better fit for workloads that need built-in sharding or are almost entirely self-contained documents.

#Does Postgres have a document size limit like MongoDB's 16 MiB?

Postgres values are limited by its general field size limits, which are far above 16 MiB. In practice, keep documents small in both databases: large documents are slow to rewrite on every update.

#Can I use the REST API with JSONB columns?

Yes. On StitchDB, JSON columns support the contains, containedBy and overlaps filters through the REST API and SDK, and you can expose complex JSON queries as SQL functions called with rpc().

Try documents and relations in one Postgres database: create a free StitchDB project.