pgvector vs Cassandra for production AI: Which Should You Use?

pgvector is a vector extension for PostgreSQL. Cassandra is a distributed wide-column database built for high write throughput and horizontal scale. If you are building production AI and need vector search plus normal application data in one place, start with pgvector; if you need massive write volume across regions with predictable availability, Cassandra is the database.

Quick Comparison

When pgvector Wins

• •

  You need vector search inside an existing PostgreSQL stack.

  • •If your app already runs on Postgres, adding pgvector is the least risky path.
  • •You keep transactions, foreign keys, row-level security, and all your existing tooling.
• •

  You need hybrid retrieval with real business filters.

  • •This is where pgvector is strong: filter by tenant, status, region, or document type first.
  • •Example pattern:

    SELECT id, content
    FROM chunks
    WHERE tenant_id = $1
      AND published = true
    ORDER BY embedding <-> $2
    LIMIT 5;
    

  • •That query shape matters in production AI because retrieval almost never depends on vectors alone.
• •

  You want simpler operations and faster delivery.

  • •One database means fewer moving parts.
  • •Your team can use standard Postgres backups, replicas, monitoring, migrations, and connection pooling instead of introducing a second datastore just for embeddings.
• •

  You care about exact SQL semantics around AI metadata.

  • •Storing chunk metadata next to embeddings is cleaner than splitting state across systems.
  • •For many RAG systems, that beats chasing distributed scale you do not actually need.

When Cassandra Wins

• •

  Your workload is write-heavy at extreme scale.

  • •Cassandra is built for ingesting large volumes of events without central bottlenecks.
  • •If you are storing millions of user actions per minute across clusters, it fits the problem better than a relational store.
• •

  You need multi-region availability as a first-class requirement.

  • •Cassandra’s replication model is designed for always-on systems spread across datacenters.
  • •If your business requirement is “keep writing even during regional failures,” Cassandra has the edge.
• •

  Your access pattern is fixed and known in advance.

  • •Cassandra performs best when you model tables around specific queries using partition keys and clustering columns.
  • •If your AI system mainly writes telemetry or feature events that are later consumed by another pipeline, that model works well.
• •

  You are building infrastructure around AI rather than retrieval itself.

  • •Think feature stores, event logs, session state capture, or online personalization signals.
  • •Cassandra shines as the durable operational store feeding downstream ML or ranking systems.

For production AI Specifically

Use pgvector unless you have a very clear distributed-systems reason not to. Most production AI apps need semantic retrieval plus filters over application data; pgvector gives you that in one database with SQL, transactions, and mature ops.

Choose Cassandra only when your primary problem is massive distributed ingestion or multi-region availability at scale. If your main job is answering AI queries over documents or records, Cassandra adds complexity without giving you a better retrieval primitive than pgvector’s <-> operator and ANN indexes like hnsw or ivfflat.

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.