Zilliz Cloud boosts vector database performance

Posted by on 31 January, 2024

This post was originally published on this site

San Francisco-based Zilliz has released a new version of its database-as-a-service (DBaaS) offering, Zilliz Cloud. The company claims the new version offers better performance while reducing cost of ownership compared to its previous version.

Zilliz Cloud is built atop the open source Milvus vector database management system. Zilliz was founded by engineers who had helped develop the Milvus vector database.

The new version of Zilliz Cloud, according to the company, offers 10x better performance than the original Milvus vector database. This is achieved by using the Hierarchical Navigable Small World (HNSW) graph index in combination with an improved filtered search.

HNSW, however, is table stakes for most vector databases, including those of rivals Weaviate and Pinecone. It is one of the most popular graph indexes for building vector databases.     

“HNSW is increasingly a must-have capability, so Zilliz would be at a disadvantage without it being supported by its DBMS,” said Doug Henschen, principal analyst at Constellation Research.

The reason behind the popularity of graph-based indexes can be attributed to their fundamental quality of being able to find the approximate nearest neighbors in high-dimensional data while being memory efficient. This quality results in an increase in performance and reduction in cost of ownership.

Another example of a graph-based index is Vamana. Other types of indexes used in vector databases include the Inverted File Index (IVF).

Additional features of the Zilliz Cloud update include the cosine similarity metric, range search, and upsert.

The cosine similarity metric is often used for text processing, where the direction of the embedding vectors is important but the distance between them is not.

A range search is used in a vector database to narrow search results based on the distance between a query vector and database vectors.

The upsert function, in a vector database, is used to either add a new vector to the index or update one if a vector with the same ID exists.

In addition to providing a unified Milvus Client that Zilliz claims will improve the developer experience, the new version of Zilliz Cloud can be integrated with data analytics, machine learning, and streaming platforms like Apache Spark, Apache Kafka, and Airbyte.

Despite the advantages of the new version, Constellation Research’s Henschen believes that many enterprises will turn to mainstream databases they already use for capabilities such as vector embeddings and vector search.

“The challenge for vendors like Zilliz is that they don’t have the transactional data of the enterprise with them typically,” said Holger Mueller, another principal analyst at Constellation Research.

“Either they have to provide the ease of use of getting transactional data in them or they need to have a solution that helps enterprises update vectors from their system of record. Failure to do so will force enterprises to look at their existing databases, such as the ones from Oracle, AWS, IBM, and Microsoft,” Mueller added.

The competition is even stiffer for Zilliz as rivals such as Pinecone also offer their products as cloud-based services, Henschen added.

However, the analyst said that dedicated AI teams and AI developers may find performance and cost advantages in using a dedicated vector database product or service, assuming it provides all of the features they need for supporting their use cases.   

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