pgvector vs ChromaDB: When to Extend PostgreSQL and When to Go Dedicated

Every developer building AI applications faces this question: should I add vector search to my existing PostgreSQL database, or spin up a dedicated vector database like ChromaDB?

The answer depends on what you're building. Let me break it down.

What's the Actual Difference?

pgvector is a PostgreSQL extension. You install it, and suddenly your existing database can store and query vectors. Your embeddings live right next to your user tables, your orders, your everything. One database, one connection string, one backup strategy.

ChromaDB is purpose-built for vectors from the ground up. It's designed specifically for AI workloads: storing embeddings, running similarity searches, and integrating with frameworks like LangChain and LlamaIndex out of the box.

Performance: The Numbers That Matter

Here's what benchmarks actually show:

ChromaDB wins on single-query speed. But when you throw real traffic at it, pgvector handles concurrent load significantly better. The gap widens with filtered queries, where pgvector's PostgreSQL foundation gives it a clear advantage.

For serious scale, pgvector with the pgvectorscale extension achieves 471 queries per second at 99% recall on 50 million vectors. That's 11x faster than Qdrant at the same recall level.

When pgvector Makes Sense

Choose pgvector if:

You already run PostgreSQL. Adding an extension is significantly simpler than operating another database. No new connection pools, no new monitoring, no new backup procedures.

You need hybrid queries. Joining vector similarity results with relational data is natural in SQL. "Find similar products that are in stock and under $50" is one query, not two systems stitched together.

Concurrent workloads matter. Production systems rarely run one query at a time. Under load, pgvector's performance advantage is substantial.

You want ACID guarantees. Your vectors participate in transactions. Rollbacks work. Foreign keys work. All the reliability PostgreSQL is known for.

# pgvector with Python
import psycopg2
from pgvector.psycopg2 import register_vector

conn = psycopg2.connect("postgresql://localhost/mydb")
register_vector(conn)

cur = conn.cursor()
cur.execute("""
    SELECT id, content, embedding <-> %s AS distance
    FROM documents
    WHERE category = 'technical'
    ORDER BY embedding <-> %s
    LIMIT 5
""", (query_embedding, query_embedding))

When ChromaDB Makes Sense

Choose ChromaDB if:

You're prototyping. Getting started takes seconds. No database setup, no extensions to configure. Just pip install chromadb and you're querying vectors.

You need built-in embeddings. ChromaDB integrates directly with HuggingFace, OpenAI, and Google embedding models. Generate and store embeddings in one step.

Pure vector workloads. If you're not joining with relational data, ChromaDB's focused API is cleaner than writing SQL.

Learning vector databases. The Python API feels intuitive. Great for understanding concepts before committing to production infrastructure.

# ChromaDB with Python
import chromadb

client = chromadb.Client()
collection = client.create_collection("docs")

collection.add(
    documents=["Document text here"],
    ids=["doc1"]
)

results = collection.query(
    query_texts=["similar content"],
    n_results=5
)

The Real Decision Framework

Here's how to think about it:

Scaling Considerations

Both solutions have limits. pgvector realistically handles 10-100 million vectors before performance degrades. ChromaDB's 2025 Rust rewrite improved things, but it's still optimized for prototyping rather than massive scale.

For truly large deployments, many teams prototype with ChromaDB or pgvector, then migrate to specialized solutions like Qdrant, Milvus, or Pinecone.

Deployment on Elestio

Both are available as fully managed services:

pgvector on Elestio: Deploy PostgreSQL with pgvector pre-installed. All the benefits of managed PostgreSQL, including automated backups, updates, and monitoring, with vector search ready to go.

ChromaDB on Elestio: Get a dedicated ChromaDB instance with persistent storage, accessible via the standard ChromaDB API. No infrastructure management.

Both start at $16/month on Netcup (2 CPU, 4GB RAM, 60GB NVMe), with the flexibility to scale as your vector workload grows.

Troubleshooting Common Issues

pgvector queries are slow: Create an HNSW index. The default sequential scan works for small datasets but doesn't scale.

CREATE INDEX ON documents
USING hnsw (embedding vector_l2_ops);

ChromaDB losing data on restart: You're using the ephemeral client. Switch to PersistentClient with a path:

client = chromadb.PersistentClient(path="/data/chroma")

Recall is poor in pgvector: Tune hnsw.ef_search. Higher values improve recall at the cost of speed.

ChromaDB memory issues: Large collections consume RAM. Consider chunking your data or upgrading your instance.

The Bottom Line

If you're already on PostgreSQL and building production systems, pgvector is probably your answer. One database, battle-tested reliability, excellent concurrent performance.

If you're prototyping, learning, or building something focused purely on embeddings, ChromaDB gets you moving faster with less friction.

Both are solid choices. Pick based on your stack, not hype.

Thanks for reading! See you in the next one.