How do their growth rates compare?

Polygres has a month-over-month growth rate of -52.3%; Riverside has +4.5%.

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. Riverside: Freemium. Freemium. A free tier allows 2 hours of multi-track recording at up to 720p with a watermark, plus unlimited single-track recording and basic editing. Paid plans are billed monthly or annually (annual saves up to 20%): Pro around $29/month, Grow around $39/month, and Webinar around $99/month. A Business tier with unlimited studios, async recording, SSO, and priority support is quoted through sales. Pro trials run 14 days.

What are alternatives to Polygres and Riverside?

Other tools in related categories include CapCut, Wondershare Filmora, Adobe Podcast, VEED.IO, Descript.

Polygres vs Riverside — Traffic (4.02K vs 4.07M), Pricing & Verdict

Open AI Tools

Polygres vs Riverside — Traffic (4.02K vs 4.07M), Pricing & Verdict | Open AI Tools

Side-by-Side Traffic & Specification Matrix

Dimension / Metric	Polygres	Riverside
Official Domain	polygres.com	riverside.com
Pricing Model	Freemium	Freemium
Monthly Visits	4.02K (4,018)	4.07M (4,069,413)
MoM Traffic Delta	-4.41K (-52.3%)	+176.99K (+4.5%)
Primary Market & Share	🌐 Global (—)	🇺🇸 United States (58.3%)
Direct Visits / mo	1.61K	2.75M
User Rating	4.1	4.6
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