June 5, 2026
pgvector vs Pinecone vs Qdrant vs Weaviate: Which Vector Database Should You Use in Your RAG Pipeline? (2026)
We've shipped production RAG systems on pgvector, Pinecone, and Qdrant. Here's the honest comparison — with real latency numbers, cost breakdowns, and a decision matrix — so you choose right the first time.
pgvector vs Pinecone vs Qdrant vs Weaviate: Which Vector Database Should You Use in Your RAG Pipeline? (2026)
Every team building a RAG system hits the same wall at roughly the same moment.
The prototype works. Embeddings are generating correctly. Retrieval is returning something. The demo looks good. Then someone asks: "Which vector database are we actually going to run in production?"
And the research begins. Four names come up immediately — pgvector, Pinecone, Qdrant, Weaviate. Every comparison article says something different. Everyone on Twitter has a strong opinion. Your lead engineer has used one of them before and is already attached to it.
At Voidcore, we have deployed production RAG systems using three of these four databases across real client workloads — document intelligence platforms, enterprise knowledge assistants, and multi-step retrieval pipelines. This is not a benchmark on synthetic data. This is what we have learned building systems that stay online when real users are using them.
Here is the honest comparison.
What You Are Actually Choosing Between
Before the comparison, a clarification worth making: these four tools are not equivalent products solving the same problem the same way. They are four different architectural decisions with different trade-offs.
pgvector is a Postgres extension. It adds vector similarity search to a database you are probably already running. No new service, no new infrastructure, no sync layer between your application database and your vector store.
Pinecone is a fully managed, purpose-built vector database. You push vectors in, query them out. Zero infrastructure to manage. The trade-off is that it is a separate system from your application data and costs escalate at scale.
Qdrant is an open-source vector database written in Rust. You can self-host or use their cloud offering. It is built specifically for high-throughput vector search with best-in-class filtered search performance.
Weaviate is an open-source vector database with a managed cloud option. Its defining capability is native hybrid search — combining BM25 keyword matching with vector similarity in a single query without external tooling.
Performance: What the Numbers Actually Look Like
These are real figures from our deployments and verified against public benchmarks from Supabase, Timescale, and ANN-Benchmarks 2025.
Database | Query Latency (p99) | Throughput (QPS) | Dataset Size |
pgvector (HNSW) | 5–20ms | 300–500 | Up to 10M vectors |
Pinecone | 8–25ms | 500–1,000+ | Any |
Qdrant | 3–12ms | 1,500–2,000+ | Any |
Weaviate | 10–30ms | 400–800 | Any |
A note on these numbers: at the dataset sizes most startups and growing companies actually operate at — under five million vectors, which covers the majority of document intelligence use cases — pgvector's performance is competitive with every option on this list. The gap widens significantly above ten million vectors, at which point the purpose-built databases earn their complexity.
For our production deployments using pgvector on NeonDB, we see query latency sitting consistently between 5–8ms on HNSW indexes for datasets under three million vectors. That is fast enough for any user-facing application.
Cost: The Number People Underestimate
This is where the comparison gets more meaningful than any benchmark.
pgvector: If you are already running Postgres, your vector store costs almost nothing to add. Hosting on NeonDB with serverless scaling brings the infrastructure cost for a typical RAG system to under $50 per month at early production scale. This is the lowest-cost option by a wide margin for teams with existing Postgres infrastructure.
Pinecone: The serverless tier is genuinely competitive for low-scale workloads — reasonable for prototyping and early production. The cost model changes significantly above ten million vectors and high query volumes. For enterprise-scale deployments we have seen monthly bills reach $800–$2,000+. Fine if you have the revenue; a problem if you are still growing into it.
Qdrant: Self-hosted Qdrant costs only the infrastructure it runs on — typically $100–$300 per month for a production deployment on a reasonably sized instance. Their cloud offering is priced competitively for teams that do not want to manage the infrastructure themselves.
Weaviate: Similar pricing model to Qdrant. Self-hosting is free. Their managed cloud offers scales with vector count and query volume. Slightly higher operational overhead than Qdrant in our experience, offset by the hybrid search capability being native rather than requiring external tooling.
When Each Database Is Actually the Right Choice
Use pgvector when:
You are already running Postgres. This is the most important signal. If your application data lives in Postgres, adding pgvector eliminates an entire category of operational complexity — no separate service, no sync requirements, no dual-write patterns, no additional failure point in your architecture.
It is also the right choice for teams shipping their first RAG system. The learning curve is minimal. The operational overhead is near-zero. The performance is sufficient for most early production workloads. You can always migrate later when you genuinely outgrow it — and that migration, in our experience, is less painful than most teams expect when they have clean application code.
We have shipped two production RAG systems on pgvector with NeonDB. At current dataset sizes — under three million vectors — we have no plans to migrate. The 5–8ms query latency is not the bottleneck. Embedding generation is.
Use Pinecone when:
Your team has no infrastructure operations capacity and you need to ship fast. Pinecone is the zero-ops option. You do not manage anything. That is genuinely valuable for teams where every engineer is building products, not managing infrastructure.
It is also a reasonable choice when you know from the start that you will operate at very large scale and cannot afford the time to tune a self-hosted solution. The managed experience is polished and the reliability is production-grade.
The honest warning: if budget is a constraint in the next twelve months, model the cost at your projected vector count before committing. The numbers can surprise founders.
Use Qdrant when:
Filtered vector search is a core part of your retrieval logic. If your RAG system needs to retrieve vectors with strict metadata filters — by user, by document type, by date range, by access level — Qdrant's filtering performance is genuinely best-in-class. We have benchmarked it consistently fastest across filtered workloads.
It is also the right call when you want self-hosted performance without committing to Postgres as your vector store, and when your team has the capability to manage a containerised service in production.
Use Weaviate when:
Your retrieval logic requires native hybrid search and you do not want to maintain a separate keyword search index alongside your vector store. Weaviate's BM25 plus vector fusion is built-in, which simplifies the architecture meaningfully for use cases where exact keyword matching matters alongside semantic retrieval — product catalogs, technical documentation, code search.
It is also a strong option for multi-tenant SaaS platforms where each customer needs isolated vector space, a pattern Weaviate handles cleanly.
The Decision Matrix
Situation | Recommended |
Already running Postgres | pgvector |
Need zero infrastructure ops | Pinecone |
Fastest filtered search is critical | Qdrant |
Need hybrid BM25 + vector search natively | Weaviate |
Building first RAG system | pgvector |
Dataset under 5M vectors | pgvector |
Dataset over 50M vectors | Qdrant or Pinecone |
Multi-tenant SaaS platform | Weaviate |
Self-hosted, budget-sensitive | Qdrant |
Fastest time to production | Pinecone |
The Mistake Most Teams Make
They choose based on what they have seen in blog posts, not based on their actual constraints.
Teams end up on Pinecone when pgvector would have served them better for eighteen months and saved significant cost. Teams end up on Weaviate when they do not actually need hybrid search. Teams spend weeks evaluating all four when they should have shipped on pgvector, learned from real users, and revisited the database question when the data told them to.
The pattern we recommend consistently: start with pgvector if you are running Postgres. Ship the system. Get real retrieval metrics from real queries. Make the migration decision when the data demands it, not before.
The best vector database is the one your team can operate confidently at the scale you are actually at today — not the one optimised for a problem you might have in three years.
What We Use at Voidcore
Our current production RAG deployments run on pgvector via NeonDB. For systems where filtered retrieval is a core requirement and datasets exceed comfortable pgvector territory, we deploy Qdrant. We have used Pinecone for client systems where the team had no infrastructure capacity and needed managed reliability from day one.
We have not yet found a production use case that required Weaviate's specific strengths — but for teams building multi-tenant knowledge platforms with complex hybrid retrieval requirements, it is the most thoughtful choice.
Start With the Right Architecture
If you are building a document intelligence system, enterprise knowledge assistant, or RAG pipeline and want a team that has shipped these in production — not just written about them — we are happy to talk through your architecture before you commit to a stack.
Book a 30-minute architecture call at voidcore.in
No sales pitch. We will tell you what we would actually build, and why.
Voidcore Technologies — AI Systems Engineering Studio. We build production-grade RAG pipelines, document intelligence platforms, and scalable SaaS backends. Based in India. Full IP ownership guaranteed.