Qdrant vs Redis Vector Store
- ✦ Open Source (Apache 2.0)
- ✦ Written in Rust
- ✦ HNSW Index
- ✦ 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?”
Qdrant's HNSW indexes integrate payload filtering natively, executing filtered nearest-neighbor search without a post-filter scan step, maintaining sub-50ms latency on complex metadata filters.
Add vector search to an existing Redis deployment for product recommendations with sub-millisecond response times
Qdrant supports sparse vectors natively alongside dense vectors, enabling BM25 and embedding search in the same collection for hybrid retrieval without maintaining two separate indexes.
Combine Redis caching and vector search in one database, reducing infrastructure complexity for recommendation APIs
Qdrant's on-disk HNSW stores vectors on SSD while keeping only graph navigation data in RAM, serving collections larger than server memory at acceptable latency for cost-sensitive deployments.
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
Pricing IntelligenceMedium confidence
Qdrant Plans
Free tier available
- • Apache 2.0 License
- • Docker & Kubernetes deployment
- • Full vector search capabilities
- • 1 managed cluster
- • 1GB RAM
- • 0.5 vCPU
- • Pay-as-you-go pricing (starts at $0.014/hour)
- • Automated backups & monitoring
- • Horizontal & vertical scaling
- • Private cloud deployment (AWS, GCP, Azure)
- • Single Sign-On (SSO) integration
- • Dedicated support with custom SLAs
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
3 differences found across 14 standardized features
- •Open Source (Apache 2.0)
- •Written in Rust
- •HNSW Index
- •Sparse Vectors (BM25-compatible)
- •Multi-vector Support
- •Payload Filtering
- •Full-Text Search
- •Named Vectors
- •Quantization (Scalar, Product, Binary)
- •Distributed Mode
- •Snapshot & Recovery
- •REST & gRPC APIs
- •Python/JS/Rust/Go SDKs
- •LangChain Integration
- •On-Premise + Cloud
- •Web UI Dashboard
- •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
- +Top benchmark performance via Rust and quantization
- +Named vectors enable multimodal and complex search patterns
- +Binary quantization reduces memory 32x
- +Excellent documentation and developer experience
- −The area for improvement in Qdrant is its clustering capability.
- +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
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