pinecone-alternative
The Pinecone alternative
that gives you SQL back.
Your vectors and application data, together in Postgres. Search with pgvector, SQL joins, and filters from $29/month.
SQL
joins, filters, transactions, with your vectors
~2 ms
p50 · recall@10 0.90+ · 250k × 1536d
$29/mo
flat price, no read/write unit metering
EU
Hosted in Europe · GDPR-ready
Every benchmark is measured with pgvector-bench and reproducible. We never publish invented QPS.
why teams leave Pinecone for pgvector
A dedicated vector store earns its keep at massive scale. Below that, it is mostly overhead.
Your vectors live next to your data
Keep embeddings and the rows they describe in one database. No separate data store to synchronize.
The pgvector guide →Real filtering, not metadata workarounds
Combine vector similarity with SQL joins, filters, permissions, and transactions.
PostgreSQL for AI →A flat bill instead of usage metering
Fixed monthly pricing per node, from $29. Query volume does not change the bill.
Postgres vs vector databases →rivestack vs pinecone
An honest comparison: different architectures for different scales.
| rivestack | pinecone | |
|---|---|---|
| What it is | Managed Postgres with tuned pgvector | Dedicated vector database service |
| Data model | Vectors as columns next to relational data | Vectors + metadata, separate from your DB |
| Query language | SQL: joins, filters, transactions | API calls with metadata filters |
| Pricing model | $29/mo flat · HA from $49/node | Usage-based (reads, writes, storage) |
| Index types | HNSW + IVFFlat (pgvector) | Proprietary serverless index |
| Hybrid search | Vector + full-text (pg_trgm, tsvector) in one query | Sparse-dense vectors |
| Data sync | None needed, one database | Your pipeline keeps two stores in sync |
| Scale ceiling | Tens of millions of vectors per node | Effectively unlimited (billions+) |
| Best fit | RAG and search where data already lives in SQL | Massive-scale pure vector workloads |
Pinecone suits large, dedicated vector workloads. Rivestack keeps vectors beside relational data for RAG and search applications.
migrating from Pinecone
For most workloads it is an afternoon, and we help, free.
Send us your workload
Send your vector count, dimensions, traffic, and current spend. We check fit and cost within about 48 hours.
Export vectors, import to pgvector
Export vectors and metadata, load a PostgreSQL table, and build an index for your recall target.
Replace API calls with SQL
Replace vector API calls with SQL, including the joins and filters your app needs.
Want the full white-glove version? See the Switch offer, where we plan and run the migration with you, or email rescue@rivestack.io with your vector count and dimensions for a free review.
flat pricing, unlimited queries
One price per node. No read units, no write units, no per-vector storage fees.
Your own single-tenant VM for small production apps. Never deleted.
- +1 vCPU · 2 GB RAM
- +30 GB NVMe storage
- +Daily backups + 7d PITR
- +500 pooled connections
- +Never deleted
Production dedicated PostgreSQL with HA-ready architecture.
- +2 vCPU · 4 GB RAM per node
- +55 GB NVMe storage
- +HA-ready (Patroni failover)
- +Daily backups + 14d PITR
- +Monitoring dashboard
More compute and storage for larger workloads.
- +4 vCPU · 8 GB RAM per node
- +135 GB NVMe storage
- +Automatic failover (2+ nodes)
- +Priority support
High-performance dedicated PostgreSQL for demanding workloads.
- +8 vCPU · 16 GB RAM per node
- +295 GB NVMe storage
- +Custom PostgreSQL config
- +Priority support
pinecone alternative FAQ
What teams ask before consolidating their vector stack into Postgres.
Teams often want simpler pricing and fewer systems to synchronize. pgvector keeps embeddings beside their source data and makes filters and joins part of SQL.
It depends on your dataset, hardware, and recall target. Benchmark your own workload. pgvector adds SQL joins and transactions while keeping vectors beside application data.
A dedicated vector service can suit very large datasets or pure vector workloads. pgvector is a natural fit when vectors describe relational data.
Rivestack charges per node rather than per read or write. Send your vector count, dimensions, and traffic for a comparison based on the capacity you need.
Export vectors and metadata, load them into PostgreSQL, build an index, and replace API queries with SQL. We help plan and validate the move.
Combine vector similarity with PostgreSQL text search and SQL ranking. Include the filters, joins, and permissions your application needs.
Database and backup traffic is encrypted in transit, backups are stored separately in S3-compatible storage with point-in-time recovery, and multi-node clusters run Patroni-managed automatic failover. Contact us before relying on a specific encryption-at-rest or compliance control. Everything is vanilla PostgreSQL, so you can pg_dump your database, vectors included, and leave without data-format lock-in.
New customers can try Solo free for 7 days with a card. Test recall and latency with your own vectors. Cancel before the trial ends to pay nothing; otherwise Solo is $29/month.
keep reading
More on vectors in Postgres.
Postgres vs vector databases
When pgvector replaces a dedicated vector store, and when it does not.
The pgvector guide
HNSW vs IVFFlat, tuning ef_search, and getting recall right.
The Switch offer
We plan and run your migration with you, free, for the first 25 teams.
Comparing other providers? See the Supabase alternative and Neon alternative pages, or the full managed pgvector overview.