Buying snapshot
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| Decision point | What Redis states |
|---|---|
How you get it | Starting with Redis 8, the Query Engine is integral to Redis, so no separate module install is needed. Standalone RediSearch releases have stopped. 32-bit systems are not supported. |
License | From Redis 8, your choice of RSALv2, SSPLv1 or AGPLv3. Prior versions remain under RSALv2 or SSPLv1. |
Distributed search | A distributed cluster version scales to billions of documents across hundreds of servers; at the moment it is available as part of Redis Cloud and Redis Enterprise Software. |
Vector queries | KNN, filtered KNN and range queries, beside full-text, numeric and geospatial search in the same engine. |
Embeddings | Different-length text, image and video embeddings from any embedding model provider, in one database. |
Multi-cloud and hybrid | Redis lists multi-cloud and hybrid cloud/on-prem deployment as available with an annual plan. |
Our take
If your team already runs Redis 8 or Redis Cloud and wants vector search next to its existing data, Redis Vector Search is a sensible choice: vectors, filters and full-text search share one engine, so there is no second database to run. If you are starting fresh with a large vector set, model the memory size you will pay for before you commit.
Evaluate Redis Vector Search
Popular comparisons
See all 5 Redis Vector Search comparisons
Where it stands out
Hybrid search in one engine. Redis offers both vector search and exact-match capabilities in the Redis Query Engine, so a query can combine semantic similarity with filters on the same data.
Built for low-latency queries. Redis says its in-memory architecture gives ultra-low-latency vector queries, which suits chatbots and agents that answer in real time.
Framework partners and RedisVL. Redis partners with LangChain and LlamaIndex and works with OpenAI, Amazon Bedrock, Mem0 and NVIDIA. The Redis Vector Library (RedisVL) adds abstractions for storing, indexing and querying vectors.
Aimed at large vector sets. Redis says the engine powers GenAI apps with millions and billions of vectors. That fits teams that expect a GenAI workload to grow well past a prototype.
Product screenshot
Trade-offs to weigh
Scaling out means a commercial product. Because distributed search ships with Redis Cloud and Redis Enterprise Software, a self-managed community install that outgrows one server needs a move to one of those products.
License review is part of adoption. The three license options carry different terms, and Redis asks users to review the full terms in the license folder.
Memory drives the bill. Redis Cloud plans are sized by RAM and storage, so a large embedding set raises cost as it grows. Redis Flex, which mixes RAM with SSD or flash, is the lever Redis offers to lower that cost.
Pricing and plan conditions
On Redis Cloud, the plan you pick sets database size, deployment type, uptime and support level; moving from shared Essentials to dedicated Pro adds a monthly floor.
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| Plan | Published price | What it adds |
|---|---|---|
Free | Always $0, up to 30 MB | Shared cloud deployment, 30 MB single database, best-effort SLA, community support |
Essentials | From $0.007/hour (Total $5/month) | Shared deployment; 250 MB-100 GB RAM & SSD, single database; SAML SSO, RBAC, encryption in transit and at rest; up to 99.99% uptime, basic support only; Redis Flex at 10% RAM for lowest cost |
Pro | From $0.014/hour; minimum $200/month; first $200 free | Everything in Essentials, plus active-active (multi-region), auto-tiering and private connectivity; dedicated cloud deployment; unlimited RAM, multiple databases; up to 99.999% uptime; Flex for TBs of data with an adjustable RAM:Flash ratio |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
495 GitHub commits 90d6.2k GitHub stars0 vulnerabilities across 1 package
See all signals from 5 sources
PyPI · redisvl@0.31.0
Alternatives to Redis Vector Search
The reviewed substitutes for Redis Vector Search among the vector databases, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Milvus
- Milvus and Redis Vector Search serve nearest-neighbour queries over embeddings for chatbots, agents and recommendation systems. Milvus is a dedicated open-source vector database that scales out across a cluster, while Redis Vector Search builds HNSW and FLAT indexes into Redis and blends vector results with other Redis data at in-memory speed.Applies to: Choosing between these two for the vector databases decision.
- MongoDB Atlas Vector Search
- Teams already on Redis or MongoDB can keep embeddings in that database and query them by similarity with Redis Vector Search or MongoDB Atlas Vector Search. Redis Vector Search answers hybrid queries from memory for very low latency, while MongoDB Atlas Vector Search runs in managed MongoDB Atlas and stores vectors in the same documents as the rest of the data.Applies to: Choosing between these two for the vector databases decision.
- Weaviate
- Bring AI-native applications to life with less hallucination, data leakage, and vendor lock-in
- Qdrant
- Qdrant is an Open-Source Vector Search Engine written in Rust. It provides fast and scalable vector similarity search service with convenient API.
- Aerospike
- Redis Vector Search and Aerospike bring vector search to fast operational databases used for real-time applications. Redis Vector Search runs similarity queries in memory next to Redis data structures, while Aerospike is a multi-model key-value, document and vector database designed for predictable low latency on very large datasets.Applies to: Choosing a vector store for embedding search in a retrieval or agent application.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Marqo
- Marqo optimises search conversion using click-stream, purchase and event data, creating a personalised experience that knows what your customers are looking for - better than they do.Applies to: For teams that build their own product search on a vector store rather than buying a hosted ecommerce search platform.