Redis Vector Hosting
Self-Host Redis as a Vector Database on Dedicated UK GPU Servers
Run Redis with the RediSearch vector similarity module on your own bare metal server. Sub-millisecond vector search, real-time filtering, and full data control — no managed-cloud markup or vendor lock-in.
What is Redis Vector Hosting?
Redis Vector hosting means running Redis Stack (with the RediSearch module) as a dedicated vector database on your own server — instead of paying per-operation fees to managed providers like Redis Cloud, Pinecone, or Zilliz.
With a GigaGPU dedicated server you get NVMe-backed storage, up to 128 GB of DDR5 RAM, and a UK-based bare metal environment. Deploy Redis Stack, load your embedding index, and serve vector similarity queries with sub-millisecond latency. No shared resources, no usage caps, no data leaving your infrastructure.
Redis is already one of the most widely deployed in-memory data stores in production. The RediSearch module adds native vector indexing (HNSW and flat), hybrid search combining vectors with tag/text/numeric filters, and JSON document storage — all in the same process you’re probably already running for caching or session management. For teams building RAG pipelines, semantic search, or recommendation systems, self-hosted Redis Vector eliminates per-query billing and keeps latency predictable.
Built for private vector search infrastructure, not shared-cloud database queues.
Why Use Redis as a Vector Database?
Redis isn’t just a cache — with RediSearch, it’s a production-grade vector database that combines the speed of in-memory indexing with powerful hybrid filtering.
Sub-Millisecond Queries
Redis stores vectors in RAM, delivering query latencies measured in microseconds rather than the tens of milliseconds typical of disk-based vector databases. Ideal for real-time search and recommendation workloads.
Hybrid Vector + Metadata Filtering
Combine vector similarity search with tag, text, numeric, and geo filters in a single query. Filter by category, date range, user ID, or any attribute — without a separate metadata store.
Familiar Redis Interface
Use the same Redis client libraries your team already knows. No new SDKs, no new query language to learn. Vector operations are standard Redis commands via the FT.SEARCH and FT.AGGREGATE APIs.
HNSW & Flat Indexing
Choose HNSW for approximate nearest neighbour search at scale, or flat (brute-force) indexing for smaller datasets that need exact results. Both support cosine, L2, and inner product distance metrics.
JSON Document Storage
Store embeddings alongside their source documents in RedisJSON. No need for a separate document store — vector search returns the full document in one round trip.
LangChain & LlamaIndex Native
First-class integrations with LangChain, LlamaIndex, Haystack, and Semantic Kernel. Drop Redis in as the vector store for any RAG pipeline with minimal code changes.
Redis Vector Hosting Use Cases
Common production workloads that benefit from self-hosted Redis Vector on dedicated hardware.
RAG & Semantic Search
Store document chunk embeddings in Redis and retrieve the most relevant context for your self-hosted LLM. Sub-millisecond retrieval keeps RAG pipeline latency low.
Product Recommendations
Embed product catalogues and serve personalised recommendations in real time. Filter by price range, availability, or category alongside similarity — all in a single Redis query.
Image & Media Search
Index CLIP or other vision model embeddings for reverse image search, visual product lookup, content moderation, and media deduplication workflows.
Conversational Memory
Give chatbots and voice agents long-term memory by embedding and indexing past conversations. Retrieve relevant history to maintain context across sessions.
Anomaly & Fraud Detection
Embed transaction patterns and flag nearest-neighbour outliers in real time. Redis’s in-memory speed makes it well suited to low-latency fraud scoring pipelines.
Document Intelligence
Combine embeddings from OCR, PDF parsing, and text extraction pipelines. Search across mixed document types — invoices, contracts, emails — with hybrid vector + keyword queries.
Best Servers for Redis Vector Hosting
Redis Vector is RAM-intensive rather than GPU-intensive. Your GPU handles embedding generation; Redis needs fast storage and large system RAM for the vector index.
16GB VRAM runs embedding models (e5-large, BGE, GTE) while system RAM handles a Redis index of up to ~2M vectors. A strong entry point for RAG prototypes and production semantic search.
24GB VRAM comfortably runs larger embedding models and rerankers alongside Redis Vector. Ideal for production RAG, recommendation engines, and hybrid search with millions of vectors.
Blackwell 2.0 delivers the fastest embedding throughput for high-ingest pipelines. Pair with 128GB system RAM for Redis indexes holding 5M+ vectors with room to spare.
96GB VRAM runs the largest embedding models, rerankers, and LLMs alongside Redis. For enterprise RAG deployments with tens of millions of vectors and complex multi-stage retrieval.
Redis Vector Hosting — GPU Server Pricing
All servers include full root access, NVMe storage, up to 128 GB RAM, and a 1 Gbps network port. Prices load live from the GigaGPU portal.
Redis Vector is RAM-intensive — the GPU handles embedding generation while system RAM holds the vector index. For large indexes (10M+ vectors), configure maximum RAM at checkout. View all GPU plans →
Redis Vector vs Managed Vector Database Providers
Managed vector database services charge per query, per GB stored, or per dimension indexed. Self-hosting Redis Vector on dedicated hardware gives you predictable costs and full control.
Managed Vector DB Pricing
Dedicated Server
Managed pricing estimates based on publicly listed tiers at time of writing and are indicative only. Actual savings depend on index size, query volume, and the specific tier used. GPU server prices load live from the GigaGPU portal.
Redis Vector vs Other Vector Databases
How Redis compares to popular alternatives for self-hosted vector search. For other options, see our dedicated pages for Qdrant, Milvus, ChromaDB, FAISS, Weaviate, and pgvector.
| Feature | Redis Vector | Qdrant | Milvus | ChromaDB | pgvector |
|---|---|---|---|---|---|
| Storage Model | In-memory (with persistence) | Disk + memory-mapped | Disk + memory cache | In-memory / SQLite | Disk (PostgreSQL) |
| Query Latency | Sub-millisecond | Low single-digit ms | Low single-digit ms | Single-digit ms | ~5–50ms |
| Hybrid Filtering | Native (tag, text, numeric, geo) | Native (payload filters) | Native (scalar + vector) | Basic metadata filters | SQL WHERE + vector |
| Index Types | HNSW, Flat | HNSW | HNSW, IVF, DiskANN | HNSW | IVFFlat, HNSW |
| Document Storage | RedisJSON (built-in) | Payload (built-in) | Separate | Built-in | PostgreSQL rows |
| Best For | Low-latency RAG, real-time apps, existing Redis users | Purpose-built vector search | Large-scale vector workloads | Prototyping, small datasets | Teams already on PostgreSQL |
| LangChain Integration | Yes | Yes | Yes | Yes | Yes |
Comparison is based on typical self-hosted configurations. All listed vector databases can be deployed on GigaGPU dedicated servers.
Deploy Redis Vector in Four Steps
From order to production vector search in under an hour.
Choose a Server
Select a GPU configuration based on your embedding model size and index requirements. Configure RAM, storage, and OS at checkout.
Install Redis Stack
SSH in and install Redis Stack with the RediSearch module: curl -fsSL https://packages.redis.io/gpg | sudo gpg --dearmor -o /usr/share/keyrings/redis-archive-keyring.gpg then apt install redis-stack-server.
Create Your Vector Index
Define your index schema with FT.CREATE specifying vector fields, dimensions, distance metric (cosine, L2, IP), and any metadata filter fields.
Query & Integrate
Use FT.SEARCH for vector similarity queries. Integrate with LangChain, LlamaIndex, or your own application via any Redis client library.
Redis Vector Ecosystem & Integrations
Tools, frameworks, and libraries that integrate natively with Redis as a vector store.
Redis Vector Hosting — Frequently Asked Questions
Common questions about self-hosting Redis as a vector database on dedicated GPU servers.
RedisVectorStore integration. Point it at your self-hosted Redis instance and use it as the retriever in any LangChain RAG pipeline. LlamaIndex, Haystack, and Semantic Kernel also have native Redis vector store integrations.FT.CREATE, specifying your vector field, dimensions, and distance metric. Then connect your application using any Redis client library. Most setups are running within 30 minutes.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 and a dedicated GPU card, ensuring unmatched performance and privacy. Perfect for self-hosting Redis Vector, RAG pipelines, semantic search, recommendation engines, and any other vector search workload — with no shared resources and no per-query fees.
Get in Touch
Have questions about which server is right for your Redis Vector workload? Our team can help you choose the right configuration for your index size, embedding model, and query volume.
Contact Sales →Or browse the knowledgebase for setup guides on Redis Stack, embedding models, and more.
Start Hosting Redis Vector Today
Flat monthly pricing. Full hardware resources. UK data centre. Deploy Redis Stack with vector search in under an hour.
