pgvector vs Redis Vector Store
- ✦ PostgreSQL Extension
- ✦ Exact Nearest Neighbor
- ✦ Approximate Nearest Neighbor (IVFFlat, HNSW)
- ✦ HNSW vector index
- ✦ FLAT (exact) vector index
- ✦ Hybrid search (vector + filter)
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
pgvector stores embeddings as a native column type and queries them with standard SQL, avoiding the operational complexity of a separate vector database for applications already running on Postgres.
Add vector search to an existing Redis deployment for product recommendations with sub-millisecond response times
pgvector's HNSW index achieves sub-50ms similarity search for collections under 10M vectors, covering most product recommendation and semantic search use cases without a specialized vector database.
pgvector writes and deletes embeddings within standard Postgres transactions, ensuring vector index and application data never diverge in multi-step operations that require rollback.
Store conversation embeddings in Redis and retrieve semantically similar past interactions for context-aware chatbot responses
Compare transaction embeddings against known fraud patterns in real-time at low latency to flag suspicious activity during checkout
Combine Redis caching and vector search in one database, reducing infrastructure complexity for recommendation APIs
Pricing IntelligenceHigh confidence
pgvector Plans
Free tier available
Redis Vector Store Plans
Free tier available
- • Self-hosted deployment
- • Vector search (HNSW and Flat indexes)
- • Distance metrics (Cosine, L2, Inner Product)
- • 30MB storage limit
- • 1 Redis database
- • Vector search capabilities
- • Storage limits from 100MB up to 5GB
- • Dedicated databases
- • Vector search and real-time indexing
- • Custom storage limits (TB+ scale)
- • Active-Active multi-region replication
- • 99.999% high availability SLA
Feature Matrix
5 differences found across 14 standardized features
- •PostgreSQL Extension
- •Exact Nearest Neighbor
- •Approximate Nearest Neighbor (IVFFlat, HNSW)
- •L2 / Cosine / Inner Product Distance
- •Indexing for Large Datasets
- •SQL Query Interface
- •JOIN with Relational Data
- •ACID Transactions
- •Standard PostgreSQL Tooling
- •Works with Supabase/RDS/Neon
- •pgvectorscale Extension
- •Python/JS/Ruby Support
- •Concurrent Queries
- •Type Safety
- •Open Source (MIT)
- •No Additional Ops
- •HNSW vector index
- •FLAT (exact) vector index
- •Hybrid search (vector + filter)
- •In-memory storage
- •Sub-millisecond latency
- •Redis Cloud managed service
- •Python/Node.js/Java SDKs
- •LangChain/LlamaIndex integration
- •Horizontal scaling via Redis Cluster
- •RDB and AOF persistence
- •Multi-tenancy via keyspaces
- •REST API (Redis Cloud)
- •Vector distance metrics (L2, IP, Cosine)
- •Metadata filtering
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +No new infrastructure: runs inside existing PostgreSQL
- +SQL interface familiar to every developer
- +ACID transactions across vectors and relational data
- +Works on Supabase, RDS, Neon: all managed PG providers
- −Performance trails purpose-built vector DBs at 10M+ vectors
- −No distributed vector search without manual sharding
- +Sub-millisecond vector search latency for applications already using Redis
- +No new database to manage if Redis is already in your stack
- +HNSW index delivers high recall with low query latency at moderate scale
- −There are some points where I feel Redis can be improved.
- −There are a few areas where Redis could improve.
- −Redis could improve its efficiency in handling locally stored data, not just Amazon Cloud or Google Cloud.
- −Redis presents a single point of failure and lacks fault tolerance.
- −The product's main purpose is caching, even though the vendor says we can also use it as a persistent database.
At a Glance
Frequently Asked Questions
Related Comparisons
Sources & Data Trail · Redis Vector Store
- 1.Official Pricing Page·Source of verified tiers(Checked: 2026-05-14)
- 2.Official Website·Official vendor website
- 3.G2·G2 verified reviews · 4.4/5
- 4.TrustRadius·TrustRadius verified reviews
- 5.PeerSpot·PeerSpot enterprise peer reviews