Redis vs. OpenSearch
Compare Redis vs. OpenSearch for vector search workloads. We want you to choose the most suitable vector database for your use case, even if it’s not us.
As AI technologies evolve, vector similarity search has become essential for powering modern AI applications like retrieval-augmented generation (RAG), semantic search, and recommendation engines. There are various vector search solutions available, including purpose-built vector databases, vector search libraries, and traditional databases with vector search as an add-on. Selecting the right solution is crucial for the success of your AI applications.
Redis and OpenSearch both bring unique strengths to vector search workloads, each with its own capabilities and limitations. The best choice depends on your specific use case and requirements. In the following sections, we’ll compare both databases regarding functionality, scalability, and availability, helping you determine the most suitable option for your needs—even if it’s not us.
Redis vs. OpenSearch at a Glance
No. It is an in-memory key-value database with vector search as an add-on
No. It is a search and analytics engine with vector search as an add-on.
Redis Source Available License (RSAL)
Apache 2.0
67,893
N/A
On-prem, Cloud
On-prem, Cloud (AWS OpenSearch)
Redis overview
Redis is a fast, in-memory key-value store renowned for its real-time performance in caching and data processing. Its Redisearch module introduces vector search capabilities, enabling approximate nearest neighbor queries for embeddings. This makes Redis a strong choice for AI-driven applications requiring low-latency vector search alongside high-throughput data storage.
OpenSearch overview
OpenSearch is a distributed, open-source search and analytics platform offering a wide range of features, including full-text search, log analytics, and observability. It supports vector search through its k-NN plugin, enabling approximate nearest neighbor queries for AI-driven applications. OpenSearch’s extensibility and scalability make it suitable for hybrid use cases that combine traditional and vector-based searches.
Benchmarking Redis and OpenSearch on your own
VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems using their own datasets and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Check out the VectorDBBench Leaderboard for a quick look at the performance of mainstream vector databases.
The Definitive Guide to Choosing a Vector Database
Overwhelmed by all the options? Learn key features to look for & how to evaluate with your own data. Choose with confidence.