Redis Vector Store vs Weaviate
- ✦ HNSW vector index
- ✦ FLAT (exact) vector index
- ✦ Hybrid search (vector + filter)
- ✦ Open Source (Apache 2.0)
- ✦ Hybrid Search (BM25 + Vector)
- ✦ Multimodal Support
Real-World Scenarios: When to Choose Which
The question that matters: “In what situation will I regret choosing A over B after 3 months?”
Add vector search to an existing Redis deployment for product recommendations with sub-millisecond response times
Weaviate's multi2vec module indexes text and image objects in the same collection, enabling cross-modal search where a text query returns images and vice versa without separate pipelines.
Combine Redis caching and vector search in one database, reducing infrastructure complexity for recommendation APIs
Weaviate's structured schema enforces data types on vector objects, enabling filtered vector search that combines nearest neighbor with exact property matches and reducing false positives in metadata-sensitive retrieval.
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
Weaviate's Generative Search module passes retrieved objects directly to an LLM within the same query, cutting latency by eliminating a separate LLM API call for RAG retrieval-generation pipelines.
Pricing IntelligenceMedium confidence
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
Weaviate Plans
Free tier available
- • Self-hosted deployment
- • Apache 2.0 open-source license
- • Full vector database capabilities
- • Fully managed SaaS deployment
- • Pay-as-you-go billing based on storage and queries
- • Zero operational overhead
- • Dedicated cluster resources
- • High availability and multi-region replication
- • Guaranteed uptime SLAs
- • Managed data plane inside your AWS, GCP, or Azure account
- • Zero data egress to Weaviate cloud
- • Custom VPC and network configuration
Feature Matrix
3 differences found across 14 standardized features
- •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
- •Open Source (Apache 2.0)
- •Hybrid Search (BM25 + Vector)
- •Multimodal Support
- •GraphQL API
- •REST API
- •gRPC API
- •Built-in Vectorizers (OpenAI, Cohere, etc.)
- •Generative Search
- •Multi-tenancy
- •Modules Architecture
- •Kubernetes Deployment
- •Schema Management
- •Batch Import
- •Cross-References (Graph)
- •Python/JS/Go SDKs
- •LangChain Integration
Pros & Cons Face-Off
Evaluative strengths and weaknesses: not feature lists
- +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.
- +Open source with managed cloud option gives deployment flexibility
- +Built-in vectorizers reduce pipeline complexity
- +Knowledge graph cross-references unique in category
- +Active community and excellent documentation
- −GraphQL API has steeper learning curve
- −Performance benchmarks trail Qdrant at very high scale
At a Glance
Frequently Asked Questions
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Sources & Data Trail · Weaviate
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