What is the primary use case for Rivestack?

It is designed as a managed PostgreSQL service tuned for vector workloads, specifically serving as a RAG backend that combines vector search and relational data in one box.

How does Rivestack handle embeddings?

It includes an auto-embeddings feature that converts inserted text into OpenAI vectors on the fly, though users can also bring their own embedding pipeline.

What are the pricing tiers for Rivestack?

Pricing starts with a free shared tier, includes a Solo plan at $29/month, and offers HA Cluster options starting at $49/node/month.

Is Rivestack a complete RAG framework?

No, it is infrastructure rather than a full RAG framework, so users must provide their own LLM for generation and can rely on the bundled OpenAI auto-embeddings or their own pipeline.

What are the limitations of Rivestack at launch?

It is currently limited to EU Central regions, caps at approximately 1M vectors on the Scale tier, and is not suitable for billion-scale vector search.

Rivestack

Managed Postgres with pgvector on dedicated NVMe, pitched as a cheaper RAG backend than Pinecone or Supabase.

Freemium· Free: $0/month · Solo: $29/month · HA Cluster: Starting at $49/node/monthRAGOpenAI embeddings (auto-embeddings)7.1 / 10

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In short

Rivestack provides managed Postgres with preconfigured pgvector and HNSW indexes on dedicated NVMe nodes. It is best for RAG teams seeking a cheaper alternative to Pinecone or Supabase by consolidating vector and relational data into one database.

Best for

Pick Rivestack if you want a cheap, fast managed pgvector host for a RAG app and prefer one Postgres over a separate vector DB.

Skip if

Skip it if you need billion-scale vector search, US/APAC-region nodes, or a managed RAG framework rather than raw infrastructure.

Rivestack is a managed PostgreSQL service tuned for vector workloads. It ships with pgvector + HNSW indexes preconfigured on dedicated NVMe nodes, an auto-embeddings feature that turns inserted text into OpenAI vectors on the fly, and a spreadsheet-style table editor for poking at rows without writing SQL. Because it's just Postgres under the hood, any standard driver (Python, Node, Go, Java, Rust, .NET) talks to it directly, and Terraform is supported for cluster provisioning.

The pitch is aimed squarely at RAG teams who've felt the bill from Pinecone, Supabase, or Neon and want to collapse their vector store and relational data into one box. Pricing starts at a free shared tier with no card required, climbs through a $15/mo Solo plan, and tops out around $99/mo for a Scale node rated for roughly 1M vectors at ~1,000 QPS and 3.7ms p50. There's a live RAG demo running against 30 days of Hacker News for sanity-checking the latency claims.

It is infrastructure, not an AI model, so you bring your own embedding pipeline (or lean on the bundled OpenAI auto-embeddings) and your own LLM for generation. Best treated as a drop-in pgvector host rather than a full RAG framework.

Editor's take

A no-nonsense managed Postgres play for the pgvector crowd that's been priced out of Pinecone. The dedicated-NVMe angle is the real differentiator, and the numbers they quote are credible for the price. Just remember you're buying a database, not a RAG stack.

— The AI Tool Bible editorial team

Pros

✅ Dedicated NVMe Postgres is genuinely fast for pgvector HNSW workloads
✅ Cheaper than Pinecone at small/medium scale
✅ One database for vectors and relational data, no sync layer
✅ Auto-embeddings on insert removes a pipeline step
✅ Standard Postgres wire protocol, works with any existing driver

Cons

⚠️ EU Central only at launch limits latency for US/APAC apps
⚠️ Tied to OpenAI for the auto-embeddings convenience feature
⚠️ Scale tier caps at ~1M vectors, not a fit for billion-scale corpora
⚠️ Younger service with thinner track record than Supabase or Neon

Use cases

rag-backendvector-searchsemantic-searchmanaged-postgresembeddings-storage

Frequently asked

What is the primary use case for Rivestack?: It is designed as a managed PostgreSQL service tuned for vector workloads, specifically serving as a RAG backend that combines vector search and relational data in one box.
How does Rivestack handle embeddings?: It includes an auto-embeddings feature that converts inserted text into OpenAI vectors on the fly, though users can also bring their own embedding pipeline.
What are the pricing tiers for Rivestack?: Pricing starts with a free shared tier, includes a Solo plan at $29/month, and offers HA Cluster options starting at $49/node/month.
Is Rivestack a complete RAG framework?: No, it is infrastructure rather than a full RAG framework, so users must provide their own LLM for generation and can rely on the bundled OpenAI auto-embeddings or their own pipeline.
What are the limitations of Rivestack at launch?: It is currently limited to EU Central regions, caps at approximately 1M vectors on the Scale tier, and is not suitable for billion-scale vector search.