Redis vs. Elasticsearch & OpenSearch

Elasticsearch is a distributed search and analytics engine built on top of the Lucene library. It’s commonly used to index and query large volumes of JSON data, powering full-text search, log analytics, and app monitoring. Unlike Redis, which is a real-time data store, Elasticsearch is typically used as a secondary system that performs search on a copy of the data.

OpenSearch is a distributed search and analytics engine built on the Lucene library, originally forked from Elasticsearch 7.10. It was created by Amazon after Elastic changed its licensing model, and remains open source under the Apache 2.0 license. OpenSearch is commonly used for full-text search, log analytics, and observability, most often through the managed Amazon OpenSearch Service. Unlike Redis, which is a real-time data store, OpenSearch is typically used as a secondary system that performs search on a copy of the data.

Elasticsearch and OpenSearch are both distributed search and analytics engines based on the Lucene library. OpenSearch was forked from Elasticsearch 7.10 after Elastic changed its licensing model. Elasticsearch is now available under an OSI-approved open source license (AGPLv3), alongside other source-available options (SSPL and Elastic License v2). OpenSearch is maintained under the Apache 2.0 license by Amazon and the open source community.

While they share a common foundation, the two projects have begun to diverge:

Elasticsearch includes features like Reciprocal Rank Fusion (RRF), ML-based relevance tuning, and built-in vector search.

OpenSearch offers the Neural Search plugin (for vector search), search pipelines, and is maintained by Amazon as the foundation of Amazon OpenSearch Service.

Elasticsearch is led by Elastic, while OpenSearch is community-driven and led by Amazon.

Redis is a real-time data platform designed for speed, simplicity, and versatility. It’s used as a cache, front end database, message broker, and search engine, all in one lightweight system. Redis stores data in memory for sub-millisecond access and supports popular data structures like JSON, hashes, streams, sets, and sorted sets. With the built-in Redis Query Engine, it also enables full-text search, aggregations, vector similarity, and secondary indexing directly on live data.

Redis is available as open source, as Redis Software for enterprise deployments, and as Redis Cloud, a fully managed service built and maintained by the original creators of Redis. Redis Software and Redis Cloud add features like Active-Active replication, Redis Flex, Redis Data Integration, enhanced security controls, and operational tooling for hybrid and on-prem environments.

Redis Open Source is the free and openly licensed version of Redis, starting with Redis 8.0. It is available under the GNU Affero General Public License v3 (AGPLv3) license, making it an official open source project. Both RSALv2 and SSPLv1 licenses are also available, but they are considered source-available licenses.

The AGPL license permits free use, modification, and distribution of software over a network, with a provision that distributed, derivative works of the software including the source code are released under the same license. This provision, called the copyleft provision, ensures that modifications remain open and discourages cloud vendors from easily reselling Redis without our consent.

Redis Open Source includes core Redis functionality along with features previously part of Redis Stack, such as JSON, time series, probabilistic data structures (Bloom, Cuckoo, top-k, etc.), and powerful search capabilities including full-text and vector search.

Redis Open Source is suitable for development, experimentation, and many production use cases, but does not include Redis Software’s operational tooling, Active-Active replication, Redis Flex, or Redis Data Integration.

Redis Query Engine is the built-in search and query layer in Redis. It evolved from RediSearch, a mature project with a long track record of powering full-text and structured search in production environments. Redis Query Engine lets devs run advanced queries on JSON and hash data, including full-text search, vector similarity, filtering, faceting, sorting, and aggregations.

Because it queries live data in memory, there’s no need to reindex or sync from a separate system. Redis Query Engine runs on the same real-time data store used by your app, delivering integrated search with low latency and no duplication.

Yes, Redis can be used as an alternative to Elasticsearch or OpenSearch for many search workloads. It’s a strong fit for scenarios that demand low latency, real-time updates, and close alignment with application logic. Redis supports full-text search, vector similarity, JSON filtering, and secondary indexing directly on live data, without needing to sync from a separate system.

It’s ideal for use cases like personalization, semantic search, chat memory, and recommendations, where performance and freshness matter. For deep text analysis across large document stores or long-term log archives, Elasticsearch and OpenSearch may be a better fit.

Yes, Redis is well suited for AI-powered search and retrieval use cases, including vector similarity search, hybrid filtering, and semantic caching. It supports high-performance vector indexing and querying, with sub-millisecond latency and support for exact-match filters, metadata, and structured queries. Redis is used in real-time AI applications such as retrieval-augmented generation (RAG), recommendation systems, and short-term and long-term memory for AI.

Unlike standalone vector databases that focus solely on similarity search, Redis can combine vector search with full-text, JSON filtering, and real-time session data, all in a single platform.

Yes, Redis supports full-text search natively through the Redis Query Engine. Devs can index and search across fields in JSON and hash documents using tokenization, stemming, scoring, and filtering. Redis also supports advanced features such as phonetic matching, highlighting, auto-complete suggesters, and aggregations.

Because Redis performs search directly on live, in-memory data, it avoids indexing lag and delivers fast, consistent query performance. This makes it a strong fit for real-time search use cases across apps, APIs, and AI systems.

Amazon forked both Redis and Elasticsearch after their maintainers changed licenses to limit how cloud providers could offer them as services without contributing back. Redis has now adopted the AGPLv3 license to ensure the project is open source while protecting against commercial exploitation without reciprocity. Elastic took a similar approach, now adopting AGPLv3 to balance openness with sustainability.

Amazon created open source alternatives: Valkey for Redis and OpenSearch for Elasticsearch. It did so to continue offering managed services under more permissive terms. While both forks remain open source, they have diverged from their origins. OpenSearch lacks many of Elasticsearch’s newer capabilities, and Valkey does not include Redis features such as search and query, JSON, or vector indexing.