Great search, aggregation and visualization products.
We use ECE platform and Elasticsearch for the delivery data to track delivery. And also use kibana for visualization of business analysis …
Starting at $16 per month
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What is Elasticsearch?
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
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Standard
$16.00
Cloud
per month
Gold
$19.00
Cloud
per month
Platinum
$22.00
Cloud
per month
Entry-level set up fee?
- No setup fee
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- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Demos
How to create data views and gain insights on Elastic
YouTube
Setting Up a Search Box to Your Website or Application with Elasticsearch
YouTube
ChatGPT and Elasticsearch: OpenAI meets private data setup walkthrough
YouTube
Product Details
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What is Elasticsearch?
Elasticsearch is a distributed, RESTful search and analytics engine capable of addressing a growing number of use cases. As the heart of the Elastic Stack, it centrally stores data for fast search, fine‑tuned relevancy, and analytics that scale.
Elasticsearch now features generative AI search capabilities. Elasticsearch Relevance Engineâ„¢ (ESRE) powers generative AI solutions for private data sets with a vector database and machine learning models for semantic search that bring increased relevance to more search application developers.
Elasticsearch now features generative AI search capabilities. Elasticsearch Relevance Engineâ„¢ (ESRE) powers generative AI solutions for private data sets with a vector database and machine learning models for semantic search that bring increased relevance to more search application developers.
ESRE combines AI with Elastic’s text search to give developers a full suite of sophisticated retrieval algorithms and the ability to integrate with large language models (LLMs). It is accessed through a single, unified API.
The Elasticsearch Relevance Engine’s configurable capabilities can be used to help improve relevance by:
- Applying advanced relevance ranking features including BM25f, a critical component of hybrid search
- Creating, storing, and searching dense embeddings using Elastic’s vector database
- Processing text using a wide range of natural language processing (NLP) tasks and models
- Letting developers manage and use their own transformer models in Elastic for business specific context
- Integrating with third-party transformer models such as OpenAI’s GPT-3 and 4 via API to retrieve intuitive summarization of content based on the customer’s data stores consolidated within Elasticsearch deployments
- Enabling ML-powered search without training or maintaining a model using Elastic’s out-of-the-box Learned Sparse Encoder model to deliver highly relevant, semantic search across a variety of domains
- Combining sparse and dense retrieval using Reciprocal Rank Fusion (RRF), a hybrid ranking method that gives developers control to optimize their AI search engine to their unique mix of natural language and keyword query types
- Integrating with third-party tooling such as LangChain to help build sophisticated data pipelines and generative AI applications
Elasticsearch Video
What is Elasticsearch?
Elasticsearch Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
Reviewers rate Support Rating highest, with a score of 7.8.
The most common users of Elasticsearch are from Enterprises (1,001+ employees).
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(205)
Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
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Elasticsearch has become an essential tool for users across various industries and domains. Its distributed architecture enables efficient searching of large datasets, even with partial text matches and across multiple fields. This capability makes it invaluable for tasks such as logging and analysis in cloud environments, where managing hundreds or thousands of servers is a necessity. Elasticsearch's fast and powerful search capabilities find application in B2B and B2C eCommerce websites, allowing users to search by various criteria like title, artist, genre, price range, and availability date. It serves as a reliable solution for tracking logs, incidents, analytics, and code quality. Additionally, Elasticsearch's ability to index and search large sets of data facilitates the creation of reporting dashboards. The product's built-in data replication features ensure data availability and easy retrieval while its scalability supports operational needs. It also enables tokenized free text search in audio transcripts as well as indexing and analyzing HTTP Request Response messages to detect security threats. With its wide range of use cases spanning from web search engines to scientific journals and complex data indexing, Elasticsearch proves to be an indispensable tool for organizations seeking efficient data storage solutions.
Highly Scalable Solution: Elasticsearch has been consistently praised by users for its highly scalable nature. It is able to handle storing and retrieving large numbers of documents, offering redundancy and distributed storage across multiple hosts with minimal configuration required.
Extensive Search Capabilities: Users highly praise Elasticsearch for its extensive search capabilities, especially in terms of full-text search. They find it easy to search and filter through millions of documents efficiently, even on large datasets, thanks to its fast search speeds.
Valuable Aggregations and Facets: Elasticsearch's support for aggregations and facets is highlighted as a valuable feature by users. They appreciate the ability to progressively add search criteria to refine their searches and uncover trends in their data.
Configuration Process: Users have encountered difficulties when implementing custom functions and have found the configuration process to be lacking. Some reviewers have mentioned challenges in integrating different elements of the program, incomplete documentation, and misleading forums.
Query Editor Limitations: Users have experienced issues with the query editor and noted that certain queries are not supported in the IntelliSense feature. Several users expressed frustration with inadequate documentation, hard-to-debug problems, and the complexities involved in tuning for ingress performance.
Learning Curve: Users have found the learning curve to be challenging, particularly for those with a background in SQL. Many reviewers mentioned a steep learning curve, extensive documentation requirements, and complexities related to mapping and data type conversion.
Attribute Ratings
Reviews
(1-25 of 43)Companies can't remove reviews or game the system. Here's why
March 31, 2024
Great search, aggregation and visualization products.
Score 9 out of 10
Vetted Review
Verified User
Elasticsearch is relatedly cheaper the splunk. OpenSearch is good and we migrated some data into it but the critical data stays in elasticsearch as it has formal support.
December 29, 2023
Elasticsearch Overall Review
They all have their specific pros and cons. Elastic was actually initially brought in to provide less expensive functionality to Splunk, and Splunk use cases. Grafana was brought in to provide less expensive visualizations compared to Splunk and Elastic...I would recommend each organization try out the trial versions of all of these applications and determine for yourself which is the best fit for your enterprise and use cases.
November 09, 2021
Elasticsearch is a tricky, but great data platform
- Apache Solr and MongoDB
Elasticsearch is the most well-known and supported free data platform that we identified. We are taking advantage of community knowledge and practices.
In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch.
We've tried Solr in the past be we encountered issues which were deal-breaking for us.
MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
In terms of flexibility and breadth of use cases no other competitor came close to Elasticsearch.
We've tried Solr in the past be we encountered issues which were deal-breaking for us.
MongoDB - it just did not pass our evaluation parameters as a main data platform. We still use it for smaller purposes, though.
August 18, 2021
Elasticsearch Observability Enables an Outstanding Capacity To Transform IT Operations
Elasticsearch brings the capacity to grow data ingest and provides 24/7 visibility into critical services across IT and Business teams.
With Elasticsarch, specialized support teams can easily view all the relevant information by using real-time dashboards, and can immediately start the initial analysis to isolate and mitigate issues.
With Elasticsarch, specialized support teams can easily view all the relevant information by using real-time dashboards, and can immediately start the initial analysis to isolate and mitigate issues.
Elasticsearch and Solr are both based on Lucene, but the user community for Elasticsearch is much stronger, and setting up a cluster is easier. Splunk is very well suited for Log indexing and searching but is not nearly as flexible as Elasticsearch. Couchbase is a great NoSQL database and is super fast as key value store, but it's indexing abilities are much weaker than Elasticsearch and can not do aggregates and listings in a single query
April 01, 2021
Elasticsearch Review
Power and simplicity along with performance.
January 13, 2021
Elasticsearch: for searches, you know!
Search and analytics capabilities of Elasticsearch are superior to its competitors. Being open source, it is a cheaper and faster solution than other competitors. Installation is straightforward and it can be potentially deployed anywhere and everywhere! There is no need for expensive subscriptions or pay per data.
March 06, 2020
Elasticsearch: Open-source, Fast, Excellent!
Faster, better, more efficient. There was no comparison in Elasticsearch vs LEM. AlienVault was decent but too expensive for what it does compared to Elastic. The only competitor I'd consider as in the same ballpark in the SIEM world is Splunk. Save yourself the money and get a Ferrari and Elasticsearch instead.
February 14, 2020
Elasticsearch helps you find the information you need!
I think Elasticseach works less great compared to Splunk. Mainly the way the Splunk search head works is vastly superior to the way the Elasticsearch query language works. Furthermore, the Splunk architecture is in my opinion easier to roll out and scale-up. Splunk also has a better visualisation editor for dashboarding, which has more freedom and is easier to use.
January 06, 2020
Brilliant search powerhouse
Elasticsearch is very well packed in a broad set of features, ranging from customization capabilities to security and add-ons, and also comes with a great visualization tool named Kibana. Most of the competitors are strong in some of these areas, but I know of no other that's so well balanced as Elasticsearch is.
December 02, 2019
Elasticsearch is a great product!
- Google Search Appliance, Amazon Elastic File System (EFS) and MS SharePoint
- SharePoint seems antiquated.
- Amazon Elastic File System is hard to find things.
- Google Search Appliance does ranking poorly.
November 19, 2019
Reliable and affordable solution which is figuring as a industry pattern for managing huge data searching.
From my perspective, there is nothing currently on the marker better than Datadog, but unfortunately, that's a pricey product, Elasticsearch deliver us part of Datadog functionalities being cheaper. Fluentd as a service (provided by the company behind Fluentd) looks like a medium service. I didn't find anything better than Elasticsearch, so, from my perspective, Elasticsearch is a product between Fluentd and Datadog.
October 30, 2019
Win quickly with Elasticsearch
- Apache Druid and InfluxDB
Elasticsearch is more expensive, especially on disk storage. In terms of functionality and ease of use, it's better than most solutions out there.
October 26, 2019
Elasticsearch is the future!
Almost no one uses Solr anymore--most have migrated to Elasticsearch. I've never tried it myself but I heard Solr is much more difficult to configure and because it doesn't use a REST API, it locks you into Java and XML. XML--ick!
Lucene: Elasticsearch is built using Lucene instances for each index (the ES code essentially just glues together tons of Lucene instances), so it's not a fair comparison. But I suppose if you wanted the flexible data-model and you don't need the system to be distributed and highly available and parallel, Lucene would be a good choice.
Lucene: Elasticsearch is built using Lucene instances for each index (the ES code essentially just glues together tons of Lucene instances), so it's not a fair comparison. But I suppose if you wanted the flexible data-model and you don't need the system to be distributed and highly available and parallel, Lucene would be a good choice.
October 09, 2019
Very useful for eCommerce
Previously, we used Microsoft SQL Server's full-text search. Elasticsearch is faster and that includes searching and indexing and re-indexing the catalog of products.
June 06, 2019
An amazing search engine
Elasticsearch is much easier to set up and maintain. It provides better distributed architecture and fault tolerance, and is much faster searching.
With Elasticsearch you can integrate a lot of data sources. It can act as a small DataLake where you can put different kinds of data and extract important insights. With Splunk, additional to elevated costs of licensing and hardware, you need to have expert engineers to address business and platform requirements. If you have Elasticsearch, it can be easily deployed and scaled.
February 27, 2019
ElasticSearch handles a large number of requests quickly and easily
All database systems have things they are good at, and things they aren't as good at. Riak/SOLR is great as a K/V store, but SOLR cannot handle requests as fast as ElasticSearch. In fact, SOLR is the reason we had to migrate to ElasticSearch.
Redis is great at SET operations on large sets of data and quick in-memory operations. We actually use Redis for a small subset of tasks in our product that wasn't appropriate to perform on ElasticSearch. In this case, it was much faster and cheaper to use Redis.
Redis is great at SET operations on large sets of data and quick in-memory operations. We actually use Redis for a small subset of tasks in our product that wasn't appropriate to perform on ElasticSearch. In this case, it was much faster and cheaper to use Redis.
Score 7 out of 10
Vetted Review
Verified User
- Logstash, Redis, Jenkins, Ansible, Puppet Enterprise (formerly Puppet Data Center Automation), Chef and Loggly
ES does not compete with the above packages but compliments them. By automating and mining logs, you are able to get a sense of the business process, marketing data or whatever else you need to capture and mine. The potential energy stored within Elasticsearch makes it a great tool to include in your DevOps toolbox.
January 10, 2019
The Best Available
Elasticsearch is the most powerful and easy to use platform in this market. It's open source which makes enhancements very possible and also makes customization something that is commonplace. We're able to create custom modules to pull data from both log and config files, which is a very unique ability.
October 15, 2018
The gold standard for text-based search
As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
When we first evaluated Elasticsearch, we compared it with alternatives like traditional RDBMS products (Postgres, MySQL) as well as other noSQL solutions like Cassandra & MongoDB. For our use case, Elasticsearch delivered on two fronts. First, we got a world-class search engine out of it, that we custom-built for our specific domain (healthcare). We've got, easily, the most expressive (easy to use & powerful) healthcare search engine out there. Second, along with the search, we also received an analytics engine that could do most analytics jobs as quickly as it retrieved search results. Overall, it would be very difficult for us to find a single solution for these two different problems.
We've not tried any other products as Elasticsearch is the best fit for our requirements.
March 01, 2018
Enthusiastic for Elasticsearch
The only other competitor we researched was mongo as some of our table information is stored in an XML file, but as we were doing searching we gravitated towards Elasticsearch. We knew mongo had some of the qualifications for what we wanted, but went with Elasticsearch for specifically our searches and actually used Mongo for more DB storage.