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How to Pick the Right Vector Database for Your Use Case

How to Choose A Vector Database: Elastic Cloud vs. Zilliz Cloud

Sep 05, 20236 min read

Compare Elastic Cloud vs. Zilliz Cloud in this in-depth benchmark, cost and features comparison.

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This Elastic Cloud comparison was last updated on September 6, 2023. To provide you with the latest findings, this blog will be regularly updated with the latest benchmark figures.

Vector databases vs. vector search plugins: unveiling the distinction

As vector databases continue to garner attention, traditional databases and search systems like Elasticsearch are racing to integrate dedicated vector search plugins. A prime example is Elasticsearch 8.0, which introduces upgraded features, including vector insertion and Approximate Nearest Neighbor (ANN) search capabilities, conveniently accessible through RESTful API endpoints. In this landscape, as the fully managed version of Elasticsearch, Elastic Cloud offers its own take on vector search capabilities.

However, it's essential to recognize that vector search plugins don't provide a comprehensive solution to embedding management and vector search. These plugins function more as supplementary components to existing systems, which can limit their overall performance concerning factors such as latency, capacity, and throughput. The endeavor to construct applications for unstructured data atop conventional databases resembles retrofitting lithium batteries and electric motors into a gasoline-powered car — an approach that falls short in innovation.

Within this Elastic Cloud vs. Zilliz Cloud comparison, we will venture beyond this distinction, delving into several other comparison areas. We'll delve into benchmarks to offer a performance perspective and perform an in-depth Elastic Cloud feature analysis.

Benchmarking Elastic Cloud and Zilliz Cloud

At Zilliz, developers and architects alike ask us, "How does Zilliz measure up to Elastic Cloud for vector embedding workloads?" This curiosity might spring from various motivations, all stemming from building semantic similarity search for capabilities like product recommenders or reverse image search, and lately, when building LLM applications with Retrieval Augmented Generation (RAG).

Over the past few weeks, we undertook a comprehensive analysis, delving into the performance and attributes of Zilliz Cloud vs. Elastic Cloud regarding vector embedding workloads. With our open-source benchmark tool, VectorDBBench, our exploration focused intently on critical aspects such as queries per second (QPS), queries per dollar (QP$), and latency.

Datasets tested

We’ve tested two types of datasets to benchmark Zilliz Cloud and Elastic Cloud.

Dataset 1: 1,000,000 vectors with 768 dimensions
Dataset 2: 500,000 vectors with 1,536 dimensions

Products tested

We’ve tested Zilliz Cloud and Elastic Cloud with similar hardware resources.

Zilliz Cloud (1cu-perf): Zilliz Cloud with one performance-optimized compute unit
Zilliz Cloud (1cu-cap): Zilliz Cloud with one capacity-optimized compute unit
Elastic Cloud (up to 2.5c8g): Elastic Cloud with up to 2.5vCPU and 8 GB of RAM

Note: For more information about Zilliz Cloud’s compute units, see Zilliz blog introducing Zilliz Cloud CU type.

Results: QPS

Zilliz outperforms Elastic Cloud by 34x and 22x, respectively, in terms of QPS when processing 1 million vectors with 768 dimensions.

Zilliz outperforms Elastic Cloud by 26x and 13x in terms of QPS when processing 500,000 vectors with 1,563 dimensions.

This is a 1-100 score based on each system's performance in different cases according to a specific rule. A higher score denotes better performance.

According to our testing results, Zilliz outperforms Elastic Cloud cost for comprehensive performance.

Results: Queries per $ (QP$)

Zilliz is 102x and 65x more cost-effective than Elastic Cloud when processing 1 million vectors with 768 dimensions.

Zilliz is 79x and 38x more cost-effective than Elastic Cloud when processing 500,000 vectors with 1,536 dimensions.

This is a 1-100 score based on each system's performance in different cases according to a specific rule. A higher score denotes better performance.

According to our testing results, Zilliz outperforms Elastic Cloud for comprehensive QP$ performance.

Results: Latency

Zilliz outperforms Elastic Cloud by 123x and 96x in terms of P99 latency when processing 1 million vectors with 768 dimensions.

Zilliz outperforms Elastic Cloud by 502x and 108x in terms of P99 latency when processing 500,000 vectors with 1,536 dimensions.

This is a >1 score based on each system's performance in different cases according to a specific rule. A lower score denotes better performance.

According to our testing results, Zilliz Cloud outperforms Elastic Cloud for comprehensive latency performance.

These benchmarks were conducted using the open-source VectorDB Bench tool on GitHub, where you’ll also find the leaderboard. VectorDBBench provides unbiased vector database benchmark results for mainstream vector databases and cloud services. Designed for ease of use, VectorDB Bench makes finding the optimal choice among a plethora of vector database cloud services and open-source vector databases a breeze.

Elastic Cloud Features Comparison

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.

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

Scalability

	Zilliz Cloud	Elastic Cloud
Separation of storage and compute	√	x
Separation of query and insertions	Yes. At the component level (which provides more fine-grained scalability).	No. Only scale at the server level.
Multi-replication	√	√
Billion-scale vector support	√	x
Dynamic segment placement vs. static data sharding	Dynamic segment placement	Static sharding

Functionality

	Zilliz Cloud	Elastic Cloud
Role-based Access Control (RBAC)	√	√
Disk Index support	√	x
Hybrid Search (ie Scalar filtering)	Yes with Scalar filtering	Yes (combine Sparse and Dense Vectors)
Partitions/namespaces/logical groups	√	x

Purpose-built

Features	Zilliz Cloud	Elastic Cloud
Purpose-built for vectors	√	x
Tunable consistency	√	x
Support for both stream and batch of vector data	√	√
Binary vector support	x	√
Multi-language SDK	Python, Java, Go, C++, Node.js, Ruby	Python, Java, Go

For more details, visit our comparison page.

Migrating from Elasticsearch to Zilliz Cloud, a fully managed Milvus service

As a fully managed version of Milvus, Zilliz Cloud elevates the game in vector database management by zeroing in on performance, security, and operational efficiency. While Milvus lays the groundwork with essential vector storage and search capabilities, Zilliz Cloud steps it up with a proprietary ANN search engine and AUTOINDEX for optimized performance. It addresses security through advanced controls and compliance measures, ensuring your data is both secure and compliant. On the operational front, Zilliz Cloud simplifies database management with automated infrastructure provisioning and real-time performance insights, making it the ideal choice for developers who want to focus on building AI applications without the operational overhead.

If you are considering Zilliz Cloud, you can easily migrate from Elasticsearch to Zilliz Cloud. Once you make the decision to migrate your vector workloads from Elasticsearch to Zilliz Cloud, just refer to this migration tutorial on how to import your data and follow the tutorial.

For more information about migration from various databases to Milvus/Zillz, refer to this Comprehensive Migration Guide.

What’s next?

Chris Churilo
Chris Churilo is the VP of Marketing (or ALT) at Zilliz where she leads all community, developer relations, and marketing efforts. Prior to Zilliz, Chris was a founding member of the InfluxData’s go to market efforts and helped propel the time series database platform to dominance in the market. In earlier roles she defined and designed a SaaS monitoring solution at Centroid, and prior to that she was the VP of product management at iPass and was the LOB for several cloud services that required her to track the business and operational metrics and analytics to help identify and resolve issues.

Next: How to Choose a Vector Database: Qdrant Cloud vs. Zilliz Cloud

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