pgvector vs MongoDB for real-time apps: Which Should You Use?

pgvector and MongoDB solve different problems, and that matters a lot in real-time systems. pgvector is a vector search extension for PostgreSQL; MongoDB is a document database with vector search built into Atlas and strong operational tooling around change streams and flexible schemas. For most real-time apps, I’d pick MongoDB when the app is event-heavy and user-facing, and pgvector when you already run PostgreSQL and need vector search without adding another datastore.

Quick Comparison

When pgvector Wins

• •

  You need vector search next to transactional data

  If your app already stores users, orders, tickets, or sessions in PostgreSQL, pgvector keeps everything in one place. That means one transaction boundary when you write metadata plus embeddings.

• •

  Your real-time queries are mostly relational filters plus similarity

  Example: “Find the 20 most similar support cases for this customer segment created in the last 24 hours.” In Postgres you can do that cleanly with SQL joins plus ORDER BY embedding <-> query_embedding LIMIT 20.

• •

  You want simpler ops than running two databases

  A lot of teams fail by splitting data between Postgres for OLTP and another engine for vectors too early. With pgvector you add HNSW or IVFFLAT indexes to the same system and keep backups, auth, migrations, and observability in one stack.

• •

  You care about strong consistency more than horizontal write scale

  Real-time recommendation lookups are often read-heavy but still need fresh writes to be visible immediately after commit. PostgreSQL’s transaction model makes that behavior predictable.

Example pattern

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  tenant_id bigint NOT NULL,
  title text NOT NULL,
  body text NOT NULL,
  embedding vector(1536),
  created_at timestamptz DEFAULT now()
);

CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops);
CREATE INDEX ON documents (tenant_id, created_at DESC);

That setup is boring in the best way: one table for metadata and embeddings, one query path for retrieval.

When MongoDB Wins

• •

  Your app changes shape constantly

  Real-time products evolve fast: new event types, new profile fields, new message payloads. MongoDB’s document model handles that without forcing migrations every time product adds a field.

• •

  You need high-volume live updates

  Chat apps, activity feeds, collaboration tools, notification systems — these are MongoDB’s home turf. Change streams give you a clean way to react to inserts and updates as they happen.

• •

  You want managed scale without becoming a database tuning team

  With Atlas you get replica sets, sharding options, monitoring hooks, backup workflows, and built-in search features under one roof. That matters when your app needs to stay live while traffic spikes.

• •

  You’re already using Atlas Search or Vector Search

  If your retrieval stack includes $search, $vectorSearch, or hybrid ranking in Atlas Search pipelines then MongoDB gives you a single operational surface for both documents and semantic lookup.

Example pattern

db.products.aggregate([
  {
    $vectorSearch: {
      index: "product_vectors",
      path: "embedding",
      queryVector: queryEmbedding,
      numCandidates: 100,
      limit: 10
    }
  },
  {
    $match: { tenantId: ObjectId("..."), status: "active" }
  }
])

For live applications where schema drift is normal and retrieval sits inside an event-driven workflow, this is a practical fit.

For real-time apps Specifically

Pick MongoDB if your real-time app is fundamentally about live user activity: chats, feeds, notifications, collaboration state, rapidly changing JSON payloads. Its document model plus change streams make it easier to keep up with constant writes and evolving schemas.

Pick pgvector if your real-time app is mostly PostgreSQL already and vector search is an added capability rather than the core datastore choice. In that case PG stays simpler operationally and gives you lower-friction transactional consistency with similarity search attached.

Keep learning

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By Cyprian Aarons, AI Consultant at Topiax.