pgvector vs Cassandra for AI agents: 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 multi-node availability. For AI agents, use pgvector unless you have a hard requirement for global scale, always-on writes, or an existing Cassandra platform you must fit into.

Quick Comparison

When pgvector Wins

Use pgvector when your agent needs retrieval that is tightly coupled to business data.

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  You need relational filters with vector search

  • •Example: “Find the 10 most similar policy clauses for this claim, but only from active policies in South Africa.”
  • •With pgvector you can do this in one SQL query using metadata columns plus embedding <-> $1.
• •

  You want one database for agent state and retrieval

  • •Store conversation history, tool outputs, user profile data, and embeddings in the same Postgres instance.
  • •That keeps joins simple and avoids syncing between systems.
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  You care about developer speed

  • •The API surface is tiny: CREATE EXTENSION vector, define a vector(1536) column, add an index like USING hnsw (embedding vector_cosine_ops).
  • •Your team already knows how to migrate tables, inspect queries, and tune indexes.
• •

  Your workload is read-heavy with moderate scale

  • •If you’re serving agent memory or RAG over thousands to tens of millions of rows, pgvector is usually enough.
  • •It gives you good latency without introducing distributed database complexity.

When Cassandra Wins

Use Cassandra when the problem is not really “vector search,” but “massive distributed ingestion with strict uptime.”

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  You have extreme write volume

  • •Think telemetry from millions of sessions, event streams from customer interactions, or agent traces at very high throughput.
  • •Cassandra handles append-heavy workloads better than a single Postgres node.
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  You need multi-region availability as a first-class requirement

  • •Cassandra’s replication model is built for always-on systems across datacenters.
  • •If your agent platform must keep accepting writes during regional failure, Cassandra has the edge.
• •

  Your access pattern is simple and predictable

  • •Cassandra shines when you know your partition key and query shape in advance.
  • •Example: fetch all events for tenant_id + conversation_id sorted by time.
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  You already run Cassandra at scale

  • •If your platform team has the tooling, alerting, repair processes, and expertise already in place, adding another system may be worse than using what exists.
  • •In that case Cassandra can store agent transcripts or embeddings as part of a broader operational pipeline.

For AI agents Specifically

Pick pgvector unless you are solving infrastructure-scale ingestion problems first and AI retrieval second. Most AI agents need semantic search over documents plus metadata filters plus some transactional state; PostgreSQL with pgvector does that cleanly in one place.

Cassandra is the wrong default for agent memory because it does not give you native vector-first ergonomics like <-> distance operators or HNSW/IVFFlat indexing out of the box in the way pgvector does. If your team starts with Cassandra here, you’ll end up bolting on extra systems just to get basic retrieval behavior back.

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.