pgvector vs Cassandra for batch processing: Which Should You Use?

pgvector and Cassandra solve different problems, and that matters a lot for batch jobs. pgvector is a PostgreSQL extension for vector similarity search; Cassandra is a distributed wide-column database built for high write throughput and horizontal scale.

For batch processing, use pgvector when the batch job needs SQL, joins, transactions, and vector search in the same place. Use Cassandra only when the batch job is mostly massive writes, time-series style reads, or fan-out processing across huge datasets.

Quick Comparison

When pgvector Wins

• •

  Your batch job needs vector search plus relational logic

  • •Example: score 5 million customer records against a set of embeddings, then filter by region, product line, or risk tier.
  • •With pgvector you can do this in one query using ORDER BY embedding <-> $1 LIMIT 50 alongside normal SQL filters.
• •

  You want one system for embeddings and business data

  • •If your batch pipeline enriches records from multiple tables, Postgres keeps joins cheap compared to moving data out to another system.
  • •This matters when you need reproducible batches with transaction boundaries.
• •

  You need simple operational overhead

  • •A single Postgres cluster with pgvector is easier to run than a Cassandra ring.
  • •Backup/restore, migrations, access control, and observability are all familiar Postgres problems.
• •

  Your batch size is large but not absurdly distributed

  • •pgvector handles serious workloads well when you tune the index type:
    • •HNSW for better recall/latency tradeoffs
    • •IVFFlat for faster build times on large static datasets
  • •If your job runs nightly or hourly on millions of rows, pgvector is usually enough.

When Cassandra Wins

• •

  Your batch job is dominated by writes

  • •If you're ingesting billions of events or checkpoint rows per run, Cassandra is the right tool.
  • •Its write path is built for append-heavy workloads with low-latency inserts.
• •

  You need linear scale across many nodes

  • •Cassandra shines when one machine or one Postgres primary becomes the bottleneck.
  • •Batch pipelines that shard naturally by tenant_id or device_id fit well with Cassandra partitioning.
• •

  Your access pattern is known and simple

  • •Cassandra works when each query hits a specific partition key or a narrow range of clustering keys.
  • •Example: process all events for customer_id = X during a backfill window.
• •

  You care more about throughput than query flexibility

  • •Batch jobs that just read raw rows, transform them offline, and write results back do not benefit from SQL joins.
  • •In that case Cassandra’s denormalized model is fine and often faster at scale.

For batch processing Specifically

Pick pgvector unless your batch job is basically a distributed ingestion engine. Batch processing usually needs filtering, grouping, deduplication, enrichment, and occasional human debugging; PostgreSQL handles all of that cleanly while pgvector adds similarity search without introducing a second data model.

Cassandra only wins when the batch workload is so large that horizontal write scaling matters more than query flexibility. If you're deciding today for most AI-adjacent batch pipelines — embeddings generation, candidate retrieval prep, reranking datasets — pgvector is the better default by a wide margin.

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.