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Rivestack

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Managed pgvector on dedicated PostgreSQL with NVMe storage. 2,000 QPS at sub-4ms p50, from $35/month, migration help from Supabase, Neon, Pinecone, and self-hosted.

Rivestack screenshot

OSS

Rivestack

Added 1 June 2026

Overview

Rivestack provides managed pgvector on dedicated PostgreSQL servers with NVMe storage. It delivers 2,000 queries per second at under 4ms p50 latency starting at $35 per month. The service offers migration assistance from Supabase, Neon, Pinecone, and self-hosted deployments.

Best for

Best for
Teams needing a dedicated, high-performance managed pgvector database for production vector search and retrieval-augmented generation workloads.

Use cases

  • Scaling vector search for production applications
  • Migrating vector databases to dedicated PostgreSQL
  • Running high-performance RAG pipelines with pgvector

Notes

Rivestack provides managed pgvector on dedicated PostgreSQL servers with NVMe storage. It delivers 2,000 queries per second at under 4ms p50 latency starting at $35 per month. The service offers migration assistance from Supabase, Neon, Pinecone, and self-hosted deployments.

Use cases

  • Scaling vector search for production applications
  • Migrating vector databases to dedicated PostgreSQL
  • Running high-performance RAG pipelines with pgvector

Pros

  • Dedicated NVMe storage ensures low latency and consistent performance
  • Competitive pricing ($35/month) for a managed dedicated instance
  • Migration support reduces friction when switching from other vector platforms

Cons

  • Limited to the pgvector extension; no support for alternative vector index types
  • Dedicated instance may be overkill for small-scale or low-traffic projects
  • No managed scaling beyond a single dedicated server (likely vertical only)

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Dedicated NVMe storage ensures low latency and consistent performance
  • Competitive pricing ($35/month) for a managed dedicated instance
  • Migration support reduces friction when switching from other vector platforms

Cons

  • Limited to the pgvector extension; no support for alternative vector index types
  • Dedicated instance may be overkill for small-scale or low-traffic projects
  • No managed scaling beyond a single dedicated server (likely vertical only)
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