Redis vs. Vespa
Compare Redis vs. Vespa 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 Vespa 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. Vespa at a Glance
No. It is an in-memory key-value database with vector search as an add-on
No. It is a general-purpose, open-source engine for large-scale data serving, search, and real-time analytics with vector search as an add-on.
Redis Source Available License (RSAL)
Apache 2.0
68,382
6,057
On-prem, Cloud
On-prem, Cloud
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.
Vespa overview
Vespa is an open-source engine for large-scale data serving and real-time search. It offers advanced vector search capabilities alongside structured filtering and ranking, making it ideal for applications like recommendation engines, semantic search, and large-scale data processing. Vespa’s robust scalability and support for hybrid queries set it apart in production-grade AI workflows.
Benchmarking Redis and Vespa 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.
Compare Redis with others
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.