pgvector vs Cassandra for startups: Which Should You Use?

pgvector is a PostgreSQL extension for vector similarity search. Cassandra is a distributed wide-column database built for massive write throughput and horizontal scale. For startups, use pgvector first unless you already know you need Cassandra’s multi-node write architecture on day one.

Quick Comparison

When pgvector Wins

• •

  You want one database for product data and embeddings.
  This is the most common startup case. Keep users, documents, permissions, and vectors in PostgreSQL so your app logic stays simple.

• •

  You are building semantic search or RAG quickly.
  pgvector gives you the core primitives fast: CREATE EXTENSION vector, vector(n) columns, and similarity operators like <-> for Euclidean distance or <=> for cosine distance.

• •

  You need production-ready indexing without inventing a new stack.
  With ivfflat or hnsw, you can get decent ANN performance without introducing a separate vector database or a distributed system your team has to babysit.

• •

  Your team already knows SQL and Postgres tooling.
  That matters more than people admit. Migrations with Flyway or Liquibase, backups with standard Postgres tools, and ORM support all keep velocity high.

Example schema:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE documents (
  id bigserial PRIMARY KEY,
  tenant_id uuid NOT NULL,
  content text NOT NULL,
  embedding vector(1536) NOT NULL
);

CREATE INDEX documents_embedding_hnsw
ON documents USING hnsw (embedding vector_cosine_ops);

That is a normal app database with vector search attached. For a startup shipping features weekly, that is the right shape.

When Cassandra Wins

• •

  You have extreme write volume from day one.
  If your product is ingesting huge streams of events, logs, telemetry, or device writes across many nodes, Cassandra is built for that workload.

• •

  You need predictable horizontal scale across multiple regions.
  Cassandra’s replication model and tunable consistency make sense when uptime and geographic distribution matter more than relational convenience.

• •

  Your access patterns are simple but massive.
  Cassandra shines when queries are known ahead of time: partition by user/device/account ID, then read by time range or recent activity. It hates ad hoc querying.

• •

  You are operating at infrastructure scale already.
  If you have a platform team comfortable managing compaction strategy, partition sizing, repair jobs, replication factors, and consistency levels like QUORUM, Cassandra becomes a serious option.

Example table design:

CREATE TABLE events_by_account (
  account_id text,
  event_time timestamp,
  event_type text,
  payload text,
  PRIMARY KEY ((account_id), event_time)
) WITH CLUSTERING ORDER BY (event_time DESC);

That model is great when every query looks like “give me the latest events for this account.” It is terrible if you later decide you want flexible filtering across arbitrary fields.

For startups Specifically

Pick pgvector unless your core business is high-volume distributed writes or globally replicated event storage. Startups lose more time to operational complexity than to raw database limits in year one.

Cassandra is the right answer only when your workload forces it. If your app needs embeddings plus normal product data plus fast iteration, pgvector on PostgreSQL is the clear winner.

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By Cyprian Aarons, AI Consultant at Topiax.