Redis Vector Search (RediSearch)
In-memory vector and full-text search via the RediSearch module, enabling sub-millisecond semantic similarity search and real-time keyword search over Redis data.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
TLS requires explicit configuration on self-hosted. ACL users provide command-level access control. Redis Cloud enforces TLS.
⚡ Reliability
Best When
You need the lowest latency vector search (<5ms) and are already using Redis for caching/sessions in your agent stack.
Avoid When
Your vector collection exceeds available RAM or you need durability guarantees without complex Redis persistence configuration.
Use Cases
- • Ultra-low latency vector similarity search for agent RAG pipelines requiring <5ms retrieval
- • Hybrid search combining vector embeddings with full-text and numeric filters in single query
- • Real-time semantic cache for LLM responses using cosine similarity to find near-duplicate queries
- • Session-scoped agent memory with automatic TTL expiry using Redis EXPIRE on vector indexes
- • High-throughput embedding lookup for agents processing thousands of requests per second
Not For
- • Persistent primary vector storage (Redis is in-memory — data loss risk without RDB/AOF persistence)
- • Very large vector collections exceeding available RAM (use Pinecone or Qdrant for disk-backed storage)
- • Teams not already using Redis who need simple vector search without in-memory infrastructure
Interface
Authentication
Redis AUTH password or ACL with username/password. Redis Cloud uses individual user credentials. TLS for connection security.
Pricing
Self-hosted Redis Stack (includes RediSearch) is free. Memory-based pricing makes vector workloads expensive at scale.
Agent Metadata
Known Gotchas
- ⚠ Vector dimensions must match index definition exactly — wrong dimension size causes silent query failure returning 0 results
- ⚠ FT.CREATE index name must not conflict with Redis key prefix — use ft: prefix convention for index names
- ⚠ HNSW index build is blocking for large datasets — create index on empty collection, then bulk load for production
- ⚠ Memory usage is ~4x raw vector bytes for HNSW index overhead — 1M vectors × 1536 dims × 4 bytes = ~24GB index memory
- ⚠ Redis persistence (RDB/AOF) doesn't include RediSearch indexes — must rebuild indexes on restart if not using Redis Enterprise
Alternatives
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Scores are editorial opinions as of 2026-03-07.