Redis Vector Search

Redis provides vector similarity search through Redis Stack (RediSearch module), enabling low-latency semantic search and RAG applications. As an in-memory database, Redis excels at small-to-medium scale vector workloads requiring ultra-low latency. It integrates vector search with Redis's core data structures, making it ideal for real-time AI applications, semantic caching, and RAG systems. If you want to compare the best vector databases for your data, try Agentset.

Rank: #9License: RSALv2 / SSPLv1 / AGPLv3Cost: medium

Vector Databases Are Just One Piece of RAG

Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no vector database to operate.

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Trusted by teams building production RAG applications

5M+

Documents

1,500+

Teams

99.9%

Uptime

Deployment

Self-Hosted (Redis Stack), Redis Enterprise, Redis Cloud

Cost

Redis Stack: Free (self-host); Cloud: starts $5/mo; Enterprise: shard-based pricing; Redis Flex: hybrid RAM+SSD

Index Types

FLAT, HNSW

Deployment

Infrastructure Options

Deployment Types

Self-Hosted (Redis Stack)
Redis Enterprise
Redis Cloud

Cloud Providers

AWS
Azure
GCP

Strengths

What Redis Vector Search Does Well

Ultra-low latency for small-to-medium datasets (sub-5ms)
Excellent for semantic caching and real-time AI agents
Integrated with Redis core features (pub/sub, streams, JSON)
Simple setup for teams already using Redis
Hybrid search combining vectors with full-text (RediSearch)
Both FLAT (exact) and HNSW (approximate) indexing
Multiple distance metrics (Euclidean, Cosine, Inner Product)
Redis Flex option for cost savings (RAM + SSD hybrid)
Strong ecosystem and community
Available across all major cloud providers
Great for recommendation systems and similarity matching

Weaknesses

Potential Drawbacks

Memory constraints: entire dataset must fit in RAM (expensive)
Storing 10M vectors (1536d) requires ~60GB RAM (~$300+/month)
RAM costs 10-30x more than disk storage at scale
Performance degrades sharply when data exceeds RAM
Not cost-effective beyond 10M vectors
HNSW graph structure consumes more memory than vectors themselves
Limited to 32,768 dimensions per vector
Maximum 10 attributes per index
No automatic memory management (manual eviction policies)
Only HNSW indexing (vs Milvus with IVF, DiskANN options)
Horizontal scaling requires manual sharding
Not suitable for billion-scale vector datasets

Use Cases

When to Choose Redis Vector Search

Ideal For

Real-time AI applications requiring sub-5ms latency
Semantic caching for LLM responses
Small-to-medium vector datasets (under 10M vectors)
Applications already using Redis infrastructure
Recommendation engines with fast response needs
RAG systems with modest document volumes
Chatbots needing ultra-fast context retrieval
Session-based personalization with vectors
Multi-modal search combining Redis data types

Not Ideal For

Large-scale datasets (100M+ vectors) due to RAM costs
Cost-sensitive projects at scale
Applications where latency >50ms is acceptable (use disk-based DBs)
Billion-vector workloads
Teams without Redis operational expertise
Use cases where vectors exceed 32K dimensions
Applications needing advanced indexing options (IVF, DiskANN)

Build RAG in Minutes, Not Months

Agentset gives you a complete RAG API with fully managed vector storage and retrieval. Upload your data, call the API, and get accurate results from day one.

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import { Agentset } from "agentset";

const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");

const results = await ns.search(
  "What is multi-head attention?"
);

for (const result of results) {
  console.log(result.text);
}