pgvector vs Cassandra for enterprise: Which Should You Use?

pgvector and Cassandra solve different problems, and enterprise teams keep mixing them up.

pgvector is a PostgreSQL extension for vector similarity search, built around SQL, indexes like ivfflat and hnsw, and the rest of Postgres. Cassandra is a distributed wide-column database built for high write throughput, linear scale, and multi-node resilience. For enterprise: use pgvector unless you already know you need Cassandra’s distributed write architecture at massive scale.

Quick Comparison

When pgvector Wins

• •

  You already run PostgreSQL in production.

  This is the biggest win. Adding pgvector means you keep one database, one backup strategy, one auth model, one observability stack. You can store embeddings in a vector(1536) column and query them with <->, <=>, or <#> without introducing another system.

• •

  You need vector search plus relational filtering.

  Enterprise search rarely means “just nearest neighbors.” It usually means “find similar documents where tenant_id = ?, status = 'approved', region IN (...), and created_at > ?.” PostgreSQL handles that cleanly with SQL; Cassandra makes that painful or impossible without precomputed tables.

• •

  You want transactional consistency around metadata.

  If your embedding rows need to stay aligned with documents, permissions, audit fields, or workflow states, Postgres gives you ACID transactions. That matters when a retrieval pipeline feeds regulated workflows like claims triage or policy servicing.

• •

  You need fast time-to-value for RAG.

  pgvector supports practical production patterns quickly:

  • •create an embedding column
  • •build an hnsw index for better recall/latency tradeoffs
  • •filter with normal SQL
  • •join against business tables

  That gets you to a working enterprise retrieval layer without building a distributed data platform first.

Example:

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE knowledge_chunks (
  id bigserial PRIMARY KEY,
  tenant_id bigint NOT NULL,
  doc_id bigint NOT NULL,
  content text NOT NULL,
  embedding vector(1536) NOT NULL
);

CREATE INDEX ON knowledge_chunks USING hnsw (embedding vector_cosine_ops);

When Cassandra Wins

• •

  Your workload is write-heavy at extreme scale.

  Cassandra is built for sustained ingestion across many nodes. If you are writing millions of events per second across regions and care more about availability than ad hoc querying, Cassandra is the right hammer.

• •

  Your access pattern is known ahead of time.

  Cassandra rewards disciplined data modeling. If every query is shaped around partition keys like (tenant_id, day_bucket) and clustering columns like (event_time), it performs very well. If you want flexible filtering later, it will punish you.

• •

  You need multi-node resilience as a first-class requirement.

  Cassandra’s replication model is designed for distributed uptime. With tunable consistency levels such as ONE, QUORUM, and LOCAL_QUORUM, you can shape durability and latency behavior across regions.

• •

  You are storing operational event data rather than semantic search data.

  Cassandra shines for:

  • •clickstream ingestion
  • •IoT telemetry
  • •session state
  • •audit/event logs
  • •high-volume append-only records

  It is not the first choice for embedding similarity search unless your team is prepared to build a lot around it.

Example:

CREATE TABLE events_by_tenant_day (
  tenant_id text,
  day date,
  event_time timestamp,
  event_type text,
  payload text,
  PRIMARY KEY ((tenant_id, day), event_time)
) WITH CLUSTERING ORDER BY (event_time DESC);

For enterprise Specifically

Pick pgvector if your goal is enterprise AI search, document retrieval, or any workflow where vectors live next to business data. It gives you simpler operations, stronger transactional guarantees, and faster delivery.

Pick Cassandra only when the core problem is distributed ingestion at huge scale with fixed query shapes. For most enterprise teams building AI systems today, Cassandra adds complexity without solving the actual problem.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.