pgvector Hosting
Self-Hosted PostgreSQL Vector Search on Dedicated UK GPU Servers
Run pgvector with private embedding models on bare metal GPU servers. Full root access, no per-query fees, unlimited vectors, predictable monthly pricing.
What is pgvector Hosting?
pgvector is the leading open source vector similarity search extension for PostgreSQL. It lets you store high-dimensional embeddings directly alongside your relational data and run fast nearest-neighbour queries using HNSW or IVFFlat indexing — all within a standard SQL interface.
Self-hosting pgvector on a dedicated GPU server means no managed service markup, no per-query billing, no data leaving your jurisdiction, and no resource contention with other tenants. You get full PostgreSQL access and a dedicated GPU for local embedding generation — meaning both the vector database and the models that generate your embeddings run on the same private machine.
For teams building RAG pipelines, semantic search, recommendation systems, or AI-powered applications — a dedicated GPU server gives you a complete, private vector infrastructure at a flat monthly rate with no per-query surprises.
Used by AI teams, SaaS platforms, and research groups across the UK and Europe for private vector search infrastructure.
Supported Embedding Models
Any open source embedding model that runs via Sentence Transformers, Ollama, or Hugging Face Transformers can be deployed alongside pgvector on the same server.
Most open source embedding models supported by Sentence Transformers, Ollama, or Hugging Face Transformers can be deployed. Vector dimensions must match those used at index creation time in pgvector.
Best GPUs for pgvector Hosting
Recommended configurations based on dataset size, embedding throughput, and query concurrency requirements.
16GB comfortably runs a mid-size embedding model like nomic-embed-text or all-MiniLM alongside pgvector. Good for datasets up to ~20M vectors and most RAG pipeline workloads.
Configure RTX 4060 Ti →The sweet spot for most production pgvector deployments. 24GB handles datasets up to 50M vectors while leaving headroom for co-hosting a reranker or small LLM for a full private RAG stack.
Configure RTX 3090 →Blackwell 2.0 architecture delivers the best embedding generation throughput of any single-card option. Ideal for high-concurrency semantic search APIs and real-time recommendation systems.
Configure RTX 5090 →96GB of GDDR7 VRAM enables billion-scale vector datasets alongside large embedding models like BGE-M3 or jina-embeddings-v3 without memory pressure. The enterprise option.
Configure RTX 6000 PRO →Which GPU Do I Need for pgvector?
Answer three quick questions and we’ll recommend the right server for your vector search workload.
pgvector Hosting Pricing
Flat monthly pricing — no per-query fees, no per-vector storage costs, no surprises.
Vector capacity figures are indicative for 768-dim embeddings with default HNSW parameters. Actual capacity depends on dimensions, index type, ef_construction, and available system RAM. See all GPU plans →
Self-Hosted pgvector vs. Managed Vector Databases
Pinecone, Weaviate Cloud, and Qdrant Cloud charge per vector stored and per query. At production scale, self-hosting is dramatically cheaper — and your data never leaves your infrastructure.
Managed Vector Database
Self-Hosted pgvector on Dedicated GPU
Data Privacy and pgvector
pgvector Hosting Use Cases
From RAG pipelines to semantic search APIs — dedicated GPU servers power every vector database workload.
RAG Pipelines
Combine pgvector with a self-hosted LLM via Ollama or vLLM for fully private retrieval-augmented generation. All data stays on your server. See our RAG hosting guide.
Semantic Search
Replace keyword search with embedding-based similarity search across documents, products, or knowledge bases. pgvector HNSW indexes return approximate nearest-neighbour results in milliseconds at millions of vectors.
Recommendation Systems
Store item and user embeddings in pgvector and serve personalised recommendations with a single SQL query. GPU-accelerated embedding generation keeps recommendation vectors fresh without batch delays.
AI Chatbot Memory
Give AI assistants long-term memory by storing conversation embeddings in pgvector and retrieving relevant context at inference time. Pairs naturally with LangChain and LlamaIndex.
Anomaly & Fraud Detection
Embed transactional or behavioural data and flag outliers using cosine distance queries. Run everything on-premise without sending sensitive financial or user data to external services.
Multimodal Search
Store image, audio, or video embeddings alongside text vectors in the same PostgreSQL database. GPU acceleration is essential for fast embedding generation across large media libraries.
pgvector-Compatible Tools & Frameworks
Works with the full Python and AI ecosystem — no vendor lock-in.
Deploy pgvector in 4 Steps
From server order to running vector similarity queries in under an hour.
Choose Your GPU & Configure
Pick a GPU sized to your dataset and embedding model. Select Ubuntu 22.04 or 24.04 — best ecosystem support for PostgreSQL, CUDA, and Python AI tooling.
Install PostgreSQL & pgvector
Run apt install postgresql-16 postgresql-16-pgvector, then enable the extension in your database with CREATE EXTENSION vector;. That's it.
Install Your Embedding Model
Pull a local model via Ollama (ollama pull nomic-embed-text) or install sentence-transformers via pip for GPU-accelerated batch embedding generation.
Index & Start Querying
Create a vector column, build an HNSW index with CREATE INDEX USING hnsw, and run similarity searches with the <=> operator. Connect LangChain, LlamaIndex, or any pgvector client and go live.
pgvector Hosting — Frequently Asked Questions
Everything you need to know about self-hosting pgvector on a dedicated GPU server.
vector data type and similarity search operators, letting you store and query embeddings alongside relational data. PostgreSQL itself runs on CPU — a GPU is not strictly required for pgvector queries. However, a GPU is strongly recommended because it dramatically accelerates the embedding generation step: creating embeddings for documents, queries, and batch ingestion is typically the main bottleneck in vector search pipelines, and a GPU can process embeddings 10–50× faster than CPU using models like all-MiniLM, nomic-embed-text, or e5-large.ef_construction value is the recommended default.PGVector from langchain_postgres — pass your connection string and an embedding function pointed at your local Ollama or vLLM endpoint. In LlamaIndex use PGVectorStore from llama_index.vector_stores.postgres. Both support similarity search, metadata filtering, and MMR retrieval. See our RAG hosting guide for a full setup walkthrough.apt install postgresql-16-pgvector on Ubuntu. pgvector is actively maintained — check the pgvector GitHub for the latest release before upgrading production indexes.Available on all servers
- 1Gbps Port
- NVMe Storage
- 128GB DDR4/DDR5
- Any OS
- 99.9% Uptime
- Root/Admin Access
Our dedicated GPU servers provide full hardware resources with no shared neighbours — perfect for pgvector deployments that require consistent query latency and predictable embedding throughput. Run PostgreSQL, your embedding model, and any downstream AI tooling on the same private machine, with no per-query costs and no data leaving your control.
Get in Touch
Have questions about which GPU is right for your vector database workload? Our team can help you choose the right configuration for your dataset size, embedding model, and query throughput requirements.
Contact Sales →Or browse the knowledgebase for pgvector setup guides and tutorials.
Start Hosting pgvector Today
Flat monthly pricing. Full GPU resources. UK data centre. Deploy PostgreSQL with pgvector and a local embedding model in under an hour.
