Key facts
| Managed alternative | Plugsky RAG collections handle chunking, embedding, indexing and querying |
| Retrieval modes | Keyword, vector and hybrid search with optional reranking |
| Standalone vectors | POST /v1/embeddings works with pgvector or any external store |
| Ingestion | Documents are chunked, embedded and indexed automatically per collection |
| Data handling | Per-collection encryption at rest; API data is not used to train models |
| Deployment | Managed, VPC, on-prem and air-gapped options |
| Free tier | plugsky-micro and plugsky-lite, no card required |
| Product status | Live |
TL;DR
- pgvector wins when vectors belong next to relational data and SQL filters.
- Pinecone wins when you want no database operations at all.
- Filtering and deletes matter more than raw search speed in real RAG workloads.
- You can keep either store and use Plugsky only for embeddings.
- A managed RAG service removes the store decision when answers are the product.
How it works, step by step
- Check whether your team already runs PostgreSQL at the scale you need.
- List the metadata filters queries must combine with similarity search.
- Estimate vector count and dimensions to size memory and indexes.
- Test recall and filtered-search latency on a realistic corpus in both options.
- Verify delete, update and backup workflows, not just insert and query.
- Choose the option whose operations model matches your team, then document the exit path.
Try it yourself
Open the vector database comparison →
How pgvector works
pgvector adds a vector column type and distance operators to PostgreSQL, with approximate indexes such as HNSW and IVFFlat. Documents, embeddings and metadata live in ordinary tables, so similarity search can be combined with relational filters, joins and transactions in one query. Backups, access control and migration tooling are whichever PostgreSQL practices you already have.
The trade-off is that search performance and scale follow your PostgreSQL deployment. Very large corpora, heavy write traffic or strict latency targets may need dedicated hardware or a purpose-built engine. For many internal RAG systems, though, keeping vectors in Postgres removes an entire service from the architecture.
How Pinecone works
Pinecone is a fully managed vector database. Indexes are created through an API, scaling and replication are handled for you, and metadata filtering is built into queries. There is no database to run, patch or capacity-plan, which shortens time to production and shifts operational risk to the vendor.
In exchange, your vectors and metadata live in the vendor's cloud under their terms, cost grows with usage rather than being a fixed platform line item, and some capabilities are tied to the service. Teams with strict residency or air-gapped requirements should validate those constraints before committing.
What actually differs in a RAG pipeline
For retrieval quality, both stores return candidates for the same embeddings; the embedding model and chunking determine what is findable. Where they diverge is filtering, consistency and operations. pgvector lets you enforce access rules with the same SQL logic as the rest of your application, in one transaction. Pinecone gives you a managed query API with metadata filters and no operational surface.
Test the scenarios that break systems: deleting a document and confirming it leaves the index, changing a tenant filter under load, and restoring from backup. Those checks reveal more about fit than a synthetic latency chart.
A third option: managed retrieval
If you would rather not operate a vector store at all, Plugsky RAG collections provide ingestion, automatic chunking and embedding, retrieval with keyword, vector and hybrid modes, optional reranking, and ranked chunks with citations. You can also keep pgvector or Pinecone and use Plugsky only for POST /v1/embeddings, which keeps your existing architecture.
Compare architectures with the vector database comparison and prototype on the free plan with plugsky-micro and plugsky-lite or the 14-day full-access trial. Current plans are on the live pricing page.
Honest comparison
| Factor | pgvector | Pinecone | Plugsky RAG collections |
|---|---|---|---|
| Hosting | Your PostgreSQL instance | Vendor-managed cloud | Managed; VPC and on-prem options |
| Filtering | Full SQL plus vector operators | Metadata filters | Metadata attached to documents |
| Consistency | Transactional with app data | Eventually consistent service semantics | Managed indexing pipeline |
| Operations | Standard Postgres operations | No database to run | No retrieval infrastructure to run |
| Best for | Postgres-centric teams | Teams avoiding all ops work | Teams that want answers, not a store |
Frequently asked questions
Is pgvector fast enough for production RAG?
For many corpora it is. Performance depends on index type, vector count, dimensions, filters and hardware, so benchmark with your own data before deciding.
Can I use Pinecone with Plugsky embeddings?
Yes. POST /v1/embeddings returns vectors you can store in Pinecone or any other vector database.
Does Pinecone support self-hosting?
Pinecone is a managed service rather than a self-hosted product. Teams that need on-prem or air-gapped deployment usually choose a self-hosted engine, a Postgres extension or a private managed deployment.
What about deletes and updates?
Both options support them, but test the mechanics on your data: in pgvector deletes are ordinary SQL rows, while a managed service applies its own indexing semantics.
Is there a free plan?
Yes. The free plan includes plugsky-micro and plugsky-lite with 2 API keys and no credit card, and a 14-day full-access trial is available.
How is pricing structured?
Self-serve plans are flat monthly with unlimited fair-use usage and no per-token charges or overage fees. See the live pricing page for current plans.