The community submitted 26 reviews to tell
us what they like about ElasticSearch, what ElasticSearch can do better, and
more.
5.0
Based on 26 reviews
Review ElasticSearch?
Review evidence here is extremely sparse: there are no written user reviews, and the only signals are positive founder ratings without any comments. That means reviewers clearly lean favorable, but there is no usable detail on strengths, weaknesses, setup, performance, or tradeoffs. Makers of products such as Playbook, Gan.AI, and Layer rate it positively, suggesting quiet approval among builders, but the feedback does not explain why.
+12
Summarized with AI
Pros
Cons
Tines The single, secure environment for agents, apps, and automations.
Elasticsearch serves as the backbone of Super's search capabilities, enabling fast and accurate retrieval of information across diverse data sources. Its flexibility allows us to handle complex queries generated by our AI system, while providing robust analytics on usage patterns. The graph-based architecture we built integrates Elasticsearch queries with LLM prompts, creating a powerful system that can search, analyze, and synthesize information. Elasticsearch's scalability and customization options have been crucial for local development, error handling, and continuous improvement of Super's performance. Simply put, Elasticsearch's speed and adaptability made it the ideal foundation for building Super's advanced knowledge retrieval system.
What's great
customization options (1)AI integration (2)scalability (1)fast and flexible search capabilities (3)complex queries handling (1)data analysis features (1)
One of our key challenges was identifying duplicate events, and Elasticsearch proved to be the ideal solution for this task. We also leveraged it for our search bar functionality and some recommendation features. While we considered Algolia as an alternative, Elasticsearch offered better scalability for our needs.
Still the best all-purpose search engine when speed, flexibility, and integration matter. We use it heavily for vector + hybrid search. Scales better than most, works great with embeddings.