pgvector vs Cassandra for multi-agent systems: 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. For multi-agent systems, use pgvector unless you have a real, proven need for massive write fan-out across many nodes.

Quick Comparison

When pgvector Wins

Use pgvector when your multi-agent system needs memory plus retrieval, not just storage.

• •

  You need semantic memory tied to relational data

  • •Example: store agent messages as rows in messages, embeddings in a vector(1536) column, then join to users, sessions, or workflow state.
  • •PostgreSQL gives you one transaction boundary for the whole operation.
• •

  You want hybrid retrieval

  • •Combine text filters with vector similarity:

    SELECT id, content
    FROM agent_memory
    WHERE tenant_id = $1
      AND status = 'active'
    ORDER BY embedding <-> $2
    LIMIT 10;
    

  • •This matters when agents must search only within a tenant, project, or workflow slice.
• •

  You need transactional correctness

  • •Multi-agent systems often update task state, tool outputs, and memory together.
  • •With pgvector inside PostgreSQL, you can wrap writes in a single BEGIN ... COMMIT transaction and avoid split-brain behavior between memory and metadata.
• •

  Your team already runs Postgres

  • •If your stack already includes PostgreSQL for app data, adding pgvector is the cheapest path.
  • •You get one backup strategy, one auth model, one observability stack, and fewer moving parts.

What this looks like in practice

CREATE EXTENSION IF NOT EXISTS vector;

CREATE TABLE agent_memory (
  id bigserial PRIMARY KEY,
  tenant_id uuid NOT NULL,
  agent_id uuid NOT NULL,
  content text NOT NULL,
  embedding vector(1536) NOT NULL,
  created_at timestamptz DEFAULT now()
);

CREATE INDEX ON agent_memory USING hnsw (embedding vector_cosine_ops);

That setup is enough for most production RAG-style agent memory systems.

When Cassandra Wins

Use Cassandra when your multi-agent system is really an event ingestion platform with extreme write volume.

• •

  You ingest massive append-only telemetry

  • •Example: every tool call, token usage record, trace span, or agent heartbeat lands as a new row.
  • •Cassandra handles sustained writes very well if your partitioning strategy is correct.
• •

  You need multi-region availability with predictable latency

  • •Cassandra’s replication model is built for distributed deployments.
  • •If agents are running across regions and you care more about uptime than relational queries, Cassandra fits better.
• •

  Your access pattern is simple and known upfront

  • •Cassandra wants queries like:

    SELECT * FROM agent_events
    WHERE tenant_id = ?
      AND day_bucket = ?
      AND agent_id = ?;
    

  • •If you know exactly how the app reads data before you design the table, Cassandra works well.
• •

  You are storing operational logs rather than semantic memory

  • •Cassandra is good for durable event history.
  • •It is not where I would put embedding search unless you are pairing it with another retrieval layer anyway.

What this means operationally

Cassandra shines when the system must keep writing even under failure conditions or regional disruption.

But it punishes bad schema design hard:

• •no ad hoc joins
• •no flexible querying
• •no “we’ll figure out the access pattern later”

If your agents need discovery-style memory search across arbitrary context windows, Cassandra becomes awkward fast.

For multi-agent systems Specifically

Pick pgvector unless your primary problem is distributed event ingestion at very high scale. Multi-agent systems usually need semantic recall over messages, tool outputs, plans, tasks, and entity state; that maps directly to PostgreSQL plus pgvector.

Cassandra only becomes the right answer when the system behaves more like a telemetry pipeline than an intelligent application. For most agent platforms I’ve seen in production—especially in banking and insurance—pgvector gives you the right mix of retrieval quality, transactional safety, and operational simplicity.

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.