PG Vector vs Elasticsearch
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
PG Vector takes the lead.
Both PG Vector and Elasticsearch are powerful vector databases designed for efficient similarity search and storage. However, their deployment options and features differ in important ways.
Why PG Vector:
- PG Vector ranks higher overall
- Elasticsearch offers more deployment options
- PG Vector is more cost-effective
- PG Vector has more permissive licensing
- Elasticsearch has 3 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
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.
Elasticsearch
Elasticsearch is one of Europe's most widely deployed open-source search engines that includes native vector database capabilities. It combines dense vector search with traditional full-text BM25 keyword search for powerful hybrid retrieval, making it ideal for RAG applications that need both semantic and lexical search capabilities.
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 | PG Vector | Elasticsearch |
|---|---|---|
| Deployment | Self-Hosted, Managed Cloud | Self-Hosted, Managed Cloud, Serverless |
| Cost | Free (extension), infra cost only | Serverless: usage-based (ECU); Hosted: starts ~$95/month; Self-hosted: free (infra cost only) |
| License | PostgreSQL | AGPL v3 / SSPL / Elastic License 2.0 |
| Index Types | Flat, IVFFlat, HNSW | HNSW, int8_hnsw, int4_hnsw, bbq_hnsw, Flat |
| Cloud Providers | AWS RDS, Azure Postgres, GCP AlloyDB, Supabase, Timescale | AWS, Azure, GCP, Alibaba Cloud |
| Regional Flexibility | high | high |
| Strengths | 9 | 12 |
| Weaknesses | 7 | 8 |