Redis Vector Search

Overview

Redis Vector Search solves the architectural complexity problem facing real-time AI applications: traditional approaches require deploying separate systems for caching (Redis), transactional data (PostgreSQL), and vector search (Pinecone/Weaviate), creating latency overhead, consistency challenges, and operational burden. Redis Vector Search consolidates these capabilities: cache API responses in Redis, store user session data, index product embeddings, and query all three in milliseconds within a single data platform. The killer use case: real-time personalized recommendations. Traditional architecture: (1) Fetch user session from Redis (2ms), (2) Query user embeddings from vector database (20ms), (3) Combine with cached product data (5ms), (4) Total: 27ms. Redis Vector Search: query vectors and fetch cached data in single operation (3ms)—9x faster. The architectural shift: treating vectors as first-class Redis data types alongside strings, hashes, lists, and sets. Create index: FT.CREATE products_idx ON HASH PREFIX 1 'product:' SCHEMA title TEXT description TEXT price NUMERIC embedding VECTOR HNSW 6 DIM 768 DISTANCE_METRIC COSINE. Add product: HSET product:1 title 'Laptop' price 999 embedding '<binary_vector_blob>'. Query: FT.SEARCH products_idx '(*)=>[KNN 10 @embedding $vec]' PARAMS 2 vec '<query_vector>' SORTBY __score. This native integration eliminates data synchronization, reduces infrastructure complexity, and achieves latencies impossible with distributed systems.

Production deployments demonstrate Redis Vector Search's practical advantages. E-commerce platform case study: 50M product catalog, 10M active users, 100K product embeddings for semantic search. Previous architecture: Redis for caching + Pinecone for vectors + PostgreSQL for products—3 databases, complex synchronization, 40ms p95 latency for search + cache lookup. Redis Vector Search migration: consolidated product catalog, embeddings, and frequently accessed data in Redis—single database, atomic operations, 5ms p95 latency (8x improvement), 70% reduction in infrastructure costs ($6K/month down to $1.8K/month). Gaming application: multiplayer matchmaking by playstyle similarity. Redis already storing player sessions, leaderboards, real-time game state—added player behavior embeddings (768-dim vectors from game actions). Matchmaking query combines vector similarity (playstyle), Redis sorted sets (skill rating), and hash lookups (player availability) in single sub-10ms operation. Financial services fraud detection: transaction embeddings for real-time pattern matching. Redis already processing 500K transactions/second for deduplication and rate limiting—added embedding-based anomaly detection without additional infrastructure, flagging suspicious transactions in <5ms by comparing against known fraud patterns. Content platform: 200M user-generated images, CLIP embeddings for visual similarity search. Redis Cluster with 20 nodes, each handling 5M vectors, total 100M indexed vectors, achieving 10K queries/second at p99 latency 8ms—comparable to Qdrant performance but leveraging existing Redis operational expertise and infrastructure.

Key Features

• In-memory speed: Sub-millisecond vector queries with HNSW indexing, 10-100x faster than disk-based vector databases for real-time use cases
• Hybrid operations: Combine vector similarity with Redis queries (sorted sets, hashes, ranges) in atomic operations, single-digit latency
• HNSW and FLAT indexes: Hierarchical Navigable Small World for approximate search (fast), FLAT for exact search (accurate), configurable parameters
• Multiple distance metrics: Cosine similarity (semantic), L2 distance (euclidean), inner product (dot product) for different embedding types
• Redis Cluster scaling: Horizontal distribution across nodes, proven billion-vector capability, automatic sharding and replication
• Real-time updates: Insert, update, delete vectors with immediate query visibility, no index rebuilding delays like batch-oriented systems
• Unified data platform: Vectors, caching, key-value, pub/sub, streams, time-series all in Redis—eliminate architectural complexity
• Hybrid search: Filter by metadata (price ranges, categories, dates) combined with vector similarity in single query
• Redis tools ecosystem: Existing monitoring (RedisInsight), backup, replication, high availability all work with vector data
• Production-proven: Same Redis reliability powering millions of applications, 99.99% uptime, battle-tested at scale

Technical Architecture

Redis Vector Search architecture integrates vector capabilities into Redis's core data structures through the RediSearch module. Storage Layer: Vectors stored as Redis strings or hash fields (binary blob format), metadata stored in Redis hashes, automatic memory management via Redis eviction policies (LRU, LFU, volatile-ttl). Indexing Layer: HNSW (Hierarchical Navigable Small World) graphs built in-memory for approximate nearest neighbor search with configurable M (connections per node, default 16), EF_CONSTRUCTION (build-time accuracy, default 200), and EF_RUNTIME (query-time accuracy, default 10). FLAT indexes provide exact search for small datasets (<10K vectors) with brute-force comparison. Index structures stored in Redis memory alongside vectors, incremental updates supported (no full rebuilds). Query Layer: FT.SEARCH command extends Redis's command set, query planner determines execution strategy (filter first vs vector search first based on selectivity), parallel execution across Redis shards in cluster mode, result merging and ranking. Distance calculation optimized with SIMD instructions (AVX2/AVX-512 on x86, NEON on ARM). Hybrid queries: WHERE price:[50 100] category:{Electronics}=>[KNN 10 @embedding $vec] combines metadata filtering (Redis hash fields) with vector similarity in single operation. Redis Cluster: Vectors distributed via consistent hashing across shards, each shard maintains independent HNSW index, queries execute in parallel across shards (scatter-gather pattern), results merged by coordinator. Replication: Redis's existing replication (master-replica) applies to vectors, synchronous and asynchronous modes supported, persistence via RDB snapshots and AOF logs. Memory optimization: Vector quantization reduces memory footprint (float32 to float16 or int8), smaller M values reduce HNSW graph memory, TTL-based eviction for temporal data (recent vectors hot, old vectors evicted). Performance: single node handles 100K-500K queries/second for 1M vectors (HNSW, M=16), latency scales logarithmically O(log n) with dataset size, GPU acceleration not available (CPU-only, but in-memory speed compensates). 21medien architects Redis Vector Search deployments: selecting index types (HNSW for >10K vectors, FLAT for <10K), tuning M/EF parameters (balance speed and accuracy), designing cluster topology (shard sizing, replication factor), implementing memory management (eviction policies, quantization), and monitoring performance (query latency, memory usage, throughput).

Common Use Cases

• Real-time recommendations: Personalized product/content suggestions combining user session data (already in Redis) with embedding similarity, sub-5ms latency
• Hybrid caching + search: Semantic search over frequently accessed content (docs, products, articles) with automatic cache invalidation, 10x faster than separate systems
• Session-based personalization: Store user behavior embeddings in session data, query similar users for collaborative filtering, all within Redis session management
• Gaming matchmaking: Match players by skill (sorted sets) and playstyle similarity (vectors) in single query, real-time matchmaking with <10ms latency
• Fraud detection: Real-time transaction embedding analysis against known fraud patterns, integrate with existing Redis rate limiting and deduplication
• Real-time analytics: Combine time-series data (Redis Timeseries) with embedding-based pattern recognition for anomaly detection
• Content deduplication: Identify duplicate/near-duplicate content (images, text, products) using similarity thresholds with Redis's atomic operations
• Chatbot memory: Store conversation history as embeddings in user sessions, retrieve relevant context for responses, all in Redis
• Visual search: E-commerce image search with CLIP embeddings, combine with inventory caching for real-time product availability checks
• API response caching: Cache LLM responses with embedding-based similarity matching, reduce API costs 80-90% by serving similar queries from cache

Integration with 21medien Services

21medien provides comprehensive Redis Vector Search implementation services for organizations seeking unified real-time AI infrastructure. Phase 1 (Architecture Assessment): We analyze existing Redis usage (workload patterns, data volumes, current use cases), evaluate vector search requirements (query latency, accuracy, scale), and design consolidated architecture. Key decisions: Redis Stack (community, free) versus Redis Enterprise (HA, multi-tenancy, geo-replication), standalone versus Redis Cluster (based on scale), memory sizing (vectors typically 10-100x larger than cached data), and hybrid query patterns. Phase 2 (Migration & Integration): For organizations already on Redis: we add RediSearch module (Redis Stack upgrade or module load), design schema (hash structures for products/documents with vector fields), implement data pipeline (populate vectors from existing embeddings or generate via API), and migrate incrementally (shadow deployments, A/B testing). For new deployments: greenfield Redis Vector Search architecture with best practices from day one. Phase 3 (Optimization): HNSW parameter tuning (M=16-48, EF_CONSTRUCTION=100-500 based on accuracy requirements), memory optimization (quantization from float32 to float16, reducing dimensions via PCA if acceptable), query pattern optimization (pre-filtering strategies, result caching), and cost reduction (right-size nodes, implement eviction policies for temporal data). Phase 4 (Scaling): Redis Cluster design for >10M vectors (shard count, shard sizing 1-5M vectors per shard), replication topology (replica count, cross-AZ placement), and performance validation (load testing, latency profiling, throughput benchmarks). Phase 5 (Operations): Comprehensive monitoring (query latency, memory usage, hit rates), backup strategies (RDB snapshots, AOF for durability), high availability configuration (Redis Sentinel or Enterprise), and cost management (memory optimization, instance right-sizing). Example implementation: For real-time personalization platform, we consolidated 5 separate systems (Redis cache, Pinecone vectors, PostgreSQL products, Elasticsearch search, Kafka events) into unified Redis Vector Search deployment: 3-node Redis Cluster with 384GB RAM total, 50M product embeddings + 10M user behavior vectors, handling 50K queries/second with p95 latency 4ms, achieved 75% infrastructure cost reduction ($24K/month down to $6K/month), eliminated data synchronization issues (atomic operations), and improved development velocity 3x (single API versus orchestrating 5 systems). Client migrated from Pinecone + Redis hybrid to Redis Vector Search only, maintaining comparable accuracy while dramatically reducing complexity and cost.

Code Examples

Basic Redis Vector Search setup: pip install redis redis-py; import redis; import numpy as np; # Connect to Redis Stack; r = redis.Redis(host='localhost', port=6379, decode_responses=False); # Create index with vector field; r.execute_command('FT.CREATE', 'products_idx', 'ON', 'HASH', 'PREFIX', '1', 'product:', 'SCHEMA', 'title', 'TEXT', 'description', 'TEXT', 'price', 'NUMERIC', 'category', 'TAG', 'embedding', 'VECTOR', 'HNSW', '6', 'TYPE', 'FLOAT32', 'DIM', '768', 'DISTANCE_METRIC', 'COSINE'); # Add product with embedding; embedding = np.random.rand(768).astype(np.float32).tobytes(); r.hset('product:1', mapping={'title': 'Wireless Headphones', 'description': 'Premium noise-canceling', 'price': 79.99, 'category': 'Electronics', 'embedding': embedding}); # Vector similarity search; query_vec = np.random.rand(768).astype(np.float32).tobytes(); results = r.execute_command('FT.SEARCH', 'products_idx', '(*)=>[KNN 10 @embedding $vec AS score]', 'PARAMS', '2', 'vec', query_vec, 'SORTBY', 'score', 'RETURN', '3', 'title', 'price', 'score', 'DIALECT', '2'); print(f'Found {results[0]} results'); for i in range(1, len(results), 2): doc_id, fields = results[i], results[i+1]; print(f'{doc_id}: {fields}') — Hybrid search (vector + filters): hybrid_results = r.execute_command('FT.SEARCH', 'products_idx', '(@price:[50 100] @category:{Electronics})=>[KNN 10 @embedding $vec AS score]', 'PARAMS', '2', 'vec', query_vec, 'SORTBY', 'score', 'DIALECT', '2') — redis-py convenience wrapper: from redis.commands.search.field import VectorField, TextField, NumericField, TagField; from redis.commands.search.indexDefinition import IndexDefinition, IndexType; from redis.commands.search.query import Query; schema = (TextField('title'), TextField('description'), NumericField('price'), TagField('category'), VectorField('embedding', 'HNSW', {'TYPE': 'FLOAT32', 'DIM': 768, 'DISTANCE_METRIC': 'COSINE'})); r.ft('products_idx').create_index(schema, definition=IndexDefinition(prefix=['product:'], index_type=IndexType.HASH)); # Query with wrapper; query = Query('(*)=>[KNN 10 @embedding $vec AS score]').sort_by('score').return_fields('title', 'price', 'score').paging(0, 10).dialect(2); results = r.ft('products_idx').search(query, query_params={'vec': query_vec}) — LangChain integration: from langchain.vectorstores.redis import Redis as RedisVectorStore; from langchain.embeddings import OpenAIEmbeddings; vectorstore = RedisVectorStore(redis_url='redis://localhost:6379', index_name='docs_idx', embedding=OpenAIEmbeddings()); vectorstore.add_texts(['document 1', 'document 2'], metadatas=[{'category': 'tech'}, {'category': 'business'}]); docs = vectorstore.similarity_search('find tech documents', k=5) — 21medien provides production Redis Vector Search templates, performance tuning guides, and migration playbooks for Pinecone/Weaviate to Redis transitions.

Best Practices

• Choose HNSW for >10K vectors (approximate, fast), FLAT for <10K vectors (exact, simpler)—measure recall to validate HNSW accuracy meets requirements
• Tune M parameter based on use case: M=16 for speed-optimized (real-time search), M=32-48 for accuracy-optimized (high recall critical), larger M increases memory linearly
• Set EF_CONSTRUCTION high during indexing (200-500) for quality index, adjust EF_RUNTIME at query time for speed-accuracy tradeoff (10=fast, 100=accurate)
• Implement hybrid queries strategically: pre-filter when selectivity >80% (e.g., category:Electronics narrows 90% of data), post-filter when selectivity <80%
• Use Redis Cluster for >10M vectors: shard data across nodes (1-5M vectors per shard optimal), configure appropriate replica count (2-3 for HA), monitor shard balance
• Monitor memory usage carefully: vectors consume significant RAM (768-dim float32 = 3KB per vector), 1M vectors = 3GB RAM minimum, add 20-50% overhead for HNSW index
• Implement vector quantization for cost reduction: float32 to float16 halves memory (acceptable for most use cases), test accuracy impact before production deployment
• Leverage Redis persistence: enable AOF (append-only file) for durability, RDB snapshots for backups, test restore procedures regularly
• Use TTL for temporal vectors: expire old embeddings automatically (e.g., user session vectors after 24h), reduces memory costs, maintains hot data in memory
• Combine vector search with Redis native features: atomic operations (MULTI/EXEC), pub/sub for real-time updates, sorted sets for ranking, streams for event processing

Redis Vector Search vs Alternatives

Redis Vector Search occupies the 'real-time hybrid operations' niche in the vector database landscape. versus Pinecone: Redis provides 5-10x lower latency for real-time use cases (sub-5ms vs 20-50ms), 10x lower cost when leveraging existing Redis infrastructure ($1-2K/month vs $10-20K/month for equivalent throughput), and unified platform eliminating data synchronization. Pinecone advantages: easier scaling to billions of vectors (serverless architecture), lower operational complexity (fully managed), and better for pure vector search workloads without caching needs. versus Weaviate: Redis offers simpler operations (familiar Redis tooling vs GraphQL learning curve), 3-5x faster queries for small-medium datasets (<10M vectors) due to in-memory architecture, and unified caching + vectors. Weaviate advantages: richer features (cross-references, generative search modules), better suited for >50M vectors, and open-source flexibility for custom deployments. versus Qdrant: Redis integrates with existing Redis infrastructure (massive advantage for Redis users), 2-3x faster for <1M vectors (in-memory vs disk-based), unified platform reduces complexity. Qdrant advantages: 2-5x faster for >10M vectors (optimized Rust implementation), advanced filtering capabilities, better pure vector database features. versus pgvector: Redis provides 10-50x faster queries (in-memory vs disk), real-time updates without index rebuilding, and horizontal scaling via Redis Cluster. pgvector advantages: ACID transactions with relational data, SQL familiarity, zero additional infrastructure for PostgreSQL users. versus ChromaDB: Redis offers production-grade reliability (battle-tested at scale), 10-20x faster queries (in-memory vs embedded), and horizontal scaling. ChromaDB advantages: simpler getting started (embedded mode), lower cost for small deployments, better for prototyping. versus FAISS: Redis provides complete database (persistence, queries, clustering) versus library requiring custom integration, operational simplicity (managed infrastructure), and real-time updates. FAISS advantages: absolute fastest raw vector search (GPU acceleration), maximum flexibility for research, billion-scale optimization. Decision framework: Choose Redis Vector Search for applications already using Redis, real-time latency requirements (<10ms), hybrid operations combining caching and vectors, and infrastructure consolidation priorities. Choose Pinecone for maximum scale with minimum operations. Choose Weaviate for GraphQL and advanced open-source features. Choose Qdrant for pure vector database optimization. Choose pgvector for PostgreSQL shops. Choose ChromaDB for rapid prototyping. Choose FAISS for research and maximum performance. 21medien migration strategy: evaluate existing Redis usage (if substantial Redis infrastructure, Redis Vector Search strong candidate), measure latency requirements (sub-10ms favors Redis), assess scale (Redis sweet spot: 1K-100M vectors), and calculate total cost (infrastructure + operations + development complexity)—typical finding: Redis Vector Search saves 60-80% total cost versus dedicated vector database for organizations already on Redis.

Pricing and Deployment

Redis Vector Search pricing depends on deployment model. Redis Stack (Free): Community edition includes RediSearch module with full vector capabilities, no licensing fees, unlimited usage. Deploy anywhere (cloud VMs, on-premise, Docker, Kubernetes). Costs: infrastructure only (AWS/GCP/Azure compute + memory). Typical costs: $100-500/month for small deployments (single r6i.xlarge with 32GB RAM, handles 1-5M vectors), $1K-5K/month for medium (Redis Cluster with 3-10 nodes, 10-100M vectors), $10K-50K/month for large (multi-region clusters, billions of vectors). Redis Enterprise: Commercial offering adds high availability (99.999% uptime SLA), active-active geo-replication, multi-tenancy, enhanced security, and enterprise support. Pricing: contact sales, typically $1K-10K/month minimum based on nodes and features. Advantages: production-critical HA, automated failover, Redis Labs support, compliance certifications (SOC2, HIPAA, PCI). Disadvantages: higher cost versus self-managed Redis Stack. Cloud Marketplace: Redis Enterprise available on AWS Marketplace, Google Cloud Marketplace, Azure Marketplace with hourly/reserved pricing. Infrastructure costs: memory-intensive workload—vectors stored in RAM. 1M vectors at 768 dimensions (float32): 3GB vectors + 1-2GB HNSW overhead = 5GB total. 10M vectors = 50GB RAM minimum. AWS pricing: r6i.2xlarge (64GB RAM, 8 vCPUs) costs $0.50/hour = $360/month, handles 10M vectors comfortably. Redis Cluster for 100M vectors: 10x r6i.2xlarge = $3.6K/month infrastructure. versus managed alternatives: 100M vectors on Pinecone costs $10-15K/month (serverless pricing), Redis Enterprise approximately $5-8K/month (enterprise license + infrastructure), self-managed Redis Stack $3-4K/month (infrastructure only). Total cost comparison for 10M vector deployment serving 10K queries/second: Redis Stack self-managed ($400/month: 2x r6i.xlarge with 32GB RAM each), Redis Enterprise ($2K/month: license + infrastructure), Pinecone ($3-5K/month: p2 pods + queries), Weaviate Cloud ($2-3K/month: managed hosting), self-hosted Qdrant ($500/month: similar infrastructure to Redis). Memory optimization: float32 to float16 quantization halves memory costs, reducing dimensions (768 to 384 via PCA) halves again (test accuracy impact), TTL-based eviction keeps hot data in memory (store 10M, keep 1M hot = 90% memory savings for temporal use cases). 21medien cost optimization strategies: right-size instances based on actual memory usage (vector count × bytes per vector + HNSW overhead), implement tiered storage (hot vectors in Redis, cold vectors in Qdrant or S3-backed system), leverage Reserved Instances (40-60% savings on AWS/GCP/Azure), and design eviction policies (LRU, TTL-based) for cost-effective scale.