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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.

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

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)

Compare Databases

See how it stacks up

Compare Redis Vector Search with other vector databases to understand the differences in deployment options, cost, and features.

vs Qdrant

Qdrant

DeploymentSelf-Hosted, Managed Cloud
CostStarts ~$0.014/hour for smallest node
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vs Chroma

Chroma

DeploymentSelf-Hosted, Managed Cloud
CostFree (local), Chroma Cloud starts at $0 with $5 free credits
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vs Milvus

Zilliz / LFAI & Data Foundation

DeploymentSelf-Hosted, Managed Cloud
CostFree (self-hosted), infra cost only
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