DingoDB VS Elasticsearch's vector database

Let’s have a side-by-side comparison of DingoDB vs Elasticsearch's vector database to find out which one is better. This software comparison between DingoDB and Elasticsearch's vector database is based on genuine user reviews. Compare software prices, features, support, ease of use, and user reviews to make the best choice between these, and decide whether DingoDB or Elasticsearch's vector database fits your business.

DingoDB

DingoDB
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A multi-modal database that provides multi-modal strong consistency data storage such as relationships, vectors, and text, and provides multi-modal joint analysis capabilities based on SQL

Elasticsearch's vector database

Elasticsearch's vector database
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Build vector search and hybrid search with Elasticsearch's open source vector database — from the leaders in BM25 text search. Try Elasticsearch's vector database, free....

DingoDB

Launched 2021-09
Pricing Model Free
Starting Price
Tech used cdnjs,Three.js,Gzip,Nginx,Ubuntu,Amazon AWS S3
Tag Vector Database,Data Analysis,Data Integration

Elasticsearch's vector database

Launched 2010-7
Pricing Model Freemium
Starting Price
Tech used Google Analytics,Google Tag Manager,Optimizely,Google Fonts,Next.js,Emotion,Gzip,JSON Schema,OpenGraph,Progressive Web App,Varnish,Webpack,HSTS
Tag Vector Database,Data Analysis

DingoDB Rank/Visit

Global Rank
Country
Month Visit

Top 5 Countries

Traffic Sources

Elasticsearch's vector database Rank/Visit

Global Rank 29863
Country United States
Month Visit 1706536

Top 5 Countries

19.96%
8.53%
6.12%
4.67%
3.7%
United States China India Korea, Republic of United Kingdom

Traffic Sources

1.77%
0.82%
0.05%
7.31%
51.58%
38.46%
social paidReferrals mail referrals search direct

Estimated traffic data from Similarweb

What are some alternatives?

When comparing DingoDB and Elasticsearch's vector database, you can also consider the following products

Seekdb - OceanBase seekdb is an open-source, AI-native search database that unifies relational, vector, text, JSON and GIS in a single engine, enabling hybrid search and in-database AI workflows.

VectorDB - VectorDB is a simple, lightweight, fully local, end-to-end solution for using embeddings-based text retrieval.

ArangoDB - ArangoDB: The unified multi-model database. Consolidate document, graph & search for high-performance apps & next-gen AI with rich context.

Lancedb - LanceDB: Blazing-fast vector search & multimodal data lakehouse for AI. Unify petabyte-scale data to build & train production-ready AI apps.

CrateDB - CrateDB: High-performance distributed SQL for real-time analytics, search, & AI. Unify data & get instant insights from massive datasets.

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