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

pgvector and Cassandra solve different problems, and pretending they’re interchangeable is how teams end up with a slow, expensive system. pgvector is a vector search extension for PostgreSQL; Cassandra is a distributed wide-column database built for massive write throughput and horizontal scale. For real-time apps, use pgvector when your app needs semantic retrieval close to transactional data; use Cassandra when the core problem is high-volume event ingestion and low-latency key-based reads.

Quick Comparison

When pgvector Wins

• •

  You need vector search next to transactional data

  If your app already runs on PostgreSQL and you need embeddings for customers, claims, policies, or tickets, pgvector is the clean choice. You can store embeddings in a vector(1536) column and query with cosine distance using <=>, while still joining against normal tables.

• •

  You need hybrid retrieval

  Real-time apps often need “find similar items” plus business filters like tenant ID, status, region, or risk score. pgvector handles this well because it lives inside Postgres, so you can combine vector similarity with SQL WHERE clauses and indexes without shipping data into another system.

• •

  You want one operational surface

  If your team already knows PostgreSQL backups, migrations, connection pooling, and observability, adding pgvector is a small step. That matters in real-time systems where latency bugs are usually caused by too many moving parts.

• •

  You’re building an AI feature on top of existing app data

  Examples: support ticket routing by similarity, product recommendations from recent activity, duplicate case detection, or document search over policy text. pgvector gives you ANN indexing options like HNSW and IVFFlat without forcing a separate vector database.

Example:

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 ON documents USING hnsw (embedding vector_cosine_ops);

SELECT id
FROM documents
WHERE tenant_id = '3b2f8a1d-4d2c-4e7d-9f3b-1a8f2b0c9e11'
ORDER BY embedding <=> '[...]'
LIMIT 5;

When Cassandra Wins

• •

  Your workload is write-dominant and always on

  Cassandra shines when you ingest huge volumes of events continuously: clickstream data, device telemetry, audit logs, payment events, or chat messages. Its architecture is designed for high write throughput across multiple nodes with predictable latency.

• •

  Your access pattern is simple key-based lookup

  Cassandra is excellent when you know your query pattern ahead of time: fetch by partition key, maybe paginate by clustering key. If your app needs “get the latest events for user X” or “read session state for request Y,” Cassandra does that extremely well.

• •

  You need multi-node resilience across regions

  With tunable consistency levels like ONE, QUORUM, or LOCAL_QUORUM, Cassandra gives you control over availability vs consistency tradeoffs. For real-time systems that cannot afford a single database bottleneck or single-region dependency, that matters.

• •

  You’re storing time-series-like operational data

  Cassandra fits workloads where records are appended continuously and queried by entity plus time window. Think fraud signals per account per minute or device readings per sensor per hour.

Example:

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

SELECT *
FROM user_events
WHERE user_id = 'u123'
LIMIT 20;

For real-time apps Specifically

Pick pgvector if the real-time feature is about intelligence: semantic search over fresh data, recommendations from current state, fraud similarity matching with business filters attached. It keeps retrieval close to your source of truth and avoids the complexity tax of running a separate distributed system.

Pick Cassandra if the real-time feature is about volume: millions of writes per second, low-latency reads by known keys, and multi-node durability under constant load. For most app teams building real-time product features today, pgvector is the better default; Cassandra only wins when scale and write pressure are the actual problem.

Keep learning

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• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.