Chroma vs PG Vector
Compare deployment options, cost efficiency, and features to choose the right vector database for your application. If you want to compare these models on your data, try Agentset.
Database Comparison
Chroma takes the lead.
Both Chroma and PG Vector are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why Chroma:
- Chroma is more cost-effective
- Chroma has 1 more strengths
Vector Databases Are Just One Piece of RAG
Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no vector database to operate.
Trusted by teams building production RAG applications
Chroma
Chroma is a lightweight, local-first vector database designed for fast prototyping and flexible on-device or self-hosted RAG workflows. It supports efficient in-memory and SPANN search modes, making it ideal for local experimentation and small to medium RAG systems.
PG Vector
PG Vector is a popular Postgres extension that adds vector search capabilities directly inside a traditional relational database. It is ideal for teams that want to keep embeddings, metadata, and application data in one system without operating a separate vector database.
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with fully managed vector storage and retrieval. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Feature Comparison
Infrastructure & Technical Details
| Feature | Chroma | PG Vector |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud | Self-Hosted, Managed Cloud |
| Cost | Free (local), Chroma Cloud starts at $0 with $5 free credits | Free (extension), infra cost only |
| License | Apache 2.0 | PostgreSQL |
| Index Types | HNSW, SPANN | Flat, IVFFlat, HNSW |
| Cloud Providers | Any | AWS RDS, Azure Postgres, GCP AlloyDB, Supabase, Timescale |
| Regional Flexibility | medium | high |
| Strengths | 10 | 9 |
| Weaknesses | 7 | 7 |