Weaviate vs. Redis
Compare Weaviate vs. Redis by the following set of capabilities. We want you to choose the best database for you, even if it’s not us.
Weaviate vs. Redis on Scalability
No. Redis primarily operates by keeping data in memory. The classic architecture of Redis does not inherently follow the storage-compute separation architecture. Instead, it tightly couples data storage and computation in the same node or instance to ensure data access performance.
No. Only scale at the server level.
No. It only scales at the server level. In addition, you need to reshard all the data when scaling out a Redis cluster.
Yes - built-in replication; HA provided in an additional layer by Reis Cluster or Redis Sentinel)
Static sharding
With Redis Enteprise
Weaviate scalability
With static sharding, if your data grows beyond the capacity of your server, you will need to add more machines to the cluster and re-shard all of your data. This can be a time-consuming and complex process. Additionally, imbalanced shards can introduce bottlenecks and reduce the efficiency of your system.
Redis Scalability
High Availabiltiy can be achieved with Redis Enterprise.
Weaviate vs. Redis on Functionality
Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.
Furthermore, differences in insert rate, query rate, and underlying hardware may result in different application needs, making overall system tunability a mandatory feature for vector databases.
Coming soon
Yes. Sparse & Dense Vectors and Scalar filtering.
Yes - Pre-filtering documents against an index containing searchable fields
1 (HNSW)
HNSW & IVFFlat
Weaviate functionality
Weaviate uses two types of indexes to power the database. An inverted index, which maps data object properties to its location in the database and a vector index to support high performance querying. In addition, their hybrid search approach uses dense vectors to understand the context of the query and combines it with sparse vectors for keyword matches.
Redis
Redis has supprt for similarity queries search with the use of vector fields; It is important to note that the k default LIMIT is 10.
Weaviate vs. Redis on Purpose-built
What’s your vector database for?
A vector database is a fully managed solution for storing, indexing, and searching across a massive dataset of unstructured data that leverages the power of embeddings from machine learning models. A vector database should have the following features:
- Scalability and tunability
- Multi-tenancy and data isolation
- A complete suite of APIs
- An intuitive user interface/administrative console
Add on to Redis
No
Python, Java, Go
Python for Vector Search
Weaviate vs. Redis: what’s right for me?
Weaviate
Weaviate is maintained by a single commercial company offering a cloud version of Weaviate. License: BSD-3-Clause license
Redis is an in-memory data structure store used as a database, cache, message broker, and streaming engine that has a vector field type for the storage, querying and indexing of vectors.
License: BSD License
Compare Weaviate 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.