How do their growth rates compare?

Polygres has a month-over-month growth rate of -52.3%; Zilliz has -16.4%.

How does pricing compare?

Polygres: Freemium. Polygres follows a freemium model with a free tier for getting started and paid managed plans for production workloads. Exact tiers are published on the company's pricing page, so check there for current numbers before budgeting. Zilliz: Freemium, Paid, Free, Contact for Pricing. Zilliz follows a freemium model. There is a free tier for getting started with managed Milvus, flexible paid plans including a Business Critical tier, and enterprise options priced by contact. The website provides a cost calculator and a public list-price page detailing every billing item, so teams can estimate spend before committing.

What are alternatives to Polygres and Zilliz?

Other tools in related categories include Exa, You.com, Algolia, Pinecone, Jina AI.

Polygres vs Zilliz — Traffic (4.02K vs 108.89K), Pricing & Verdict

Open AI Tools

Polygres vs Zilliz — Traffic (4.02K vs 108.89K), Pricing & Verdict | Open AI Tools

Side-by-Side Traffic & Specification Matrix

Dimension / Metric	Polygres	Zilliz
Official Domain	polygres.com	zilliz.com
Pricing Model	Freemium	Freemium, Paid, Free, Contact for Pricing
Monthly Visits	4.02K (4,018)	108.89K (108,886)
MoM Traffic Delta	-4.41K (-52.3%)	-21.29K (-16.4%)
Primary Market & Share	🌐 Global (—)	🇺🇸 United States (14.7%)
Direct Visits / mo	1.61K	36.84K
User Rating	4.1	4.7
Supported Platforms	Website	Website

Polygres

Graph, vector and text retrieval for AI agents on your Postgres

Unified retrieval API

Query graph, vector, full-text and hybrid retrieval through one consistent interface instead of stitching multiple systems together.

Graph traversal on Postgres

The open-source pgGraph engine follows relationships across your records, enabling multi-hop and shortest-path retrieval without moving data.

Vector and hybrid search

Semantic search over embeddings combines with sparse and full-text matching so queries that mix meaning and exact terms still work.

Developer toolkit

A dashboard playground, CLI for imports and configuration, and a typed Python SDK make it straightforward to prototype and then ship.

Strengths & Pros

+Keeps your existing PostgreSQL instead of forcing a migration to a specialised vector store
+Combines four retrieval modes in one platform, which reduces the number of moving parts in a RAG stack
+Core engines pgGraph and pgContext are open source, so the internals are inspectable and self-hostable
+Familiar Postgres tooling, SQL knowledge and connection patterns carry over for most teams