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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Pricing
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
Offerings
- 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
- About
- Tech Details
- FAQs
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 Engine™ (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 Engine™ (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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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!
- Business Problems Solved
- Pros
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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
(26-47 of 47)Companies can't remove reviews or game the system. Here's why
- Improved the speed of our website
- Improved the user experience by providing highly efficient text search
- We're using it for logging, which makes it easy to query the errors and solve them
- It takes time to understand the advanced queries in Elasticsearch
April 13, 2018
Find more faster with Elasticsearch
- Full-text searches on certain tables have dropped by up to 98%. Searches that used to take upwards of 45 seconds to complete now take a fraction of a second. From a users perspective.
- Taking the computational load off our servers has allowed us to decrease the number of Oracle cores we have saving us a lot of money in license fees.
March 01, 2018
Enthusiastic for Elasticsearch
- The first and highest reason for switching to Elasticsearch was to speed up the queries that we had that were running slowly(full text search over millions of records). We had some Oracle searches that were taking upwards to 45 seconds. After switching to Elasticsearch those same exact queries were running under half a second. It was obvious to us what the return on the investment was there.
- The first thing(unexpected but made sense) that we noticed when switching to elasticsearch was our database servers didn't need as many cores. As you pay for Oracle licensing by cores, this was a huge benefit. We dropped about 6 cores in our Oracle licensing as soon as we could after switching to ES.
- Since we had such great success with searching one table we decided to include more tables into our searching to help with our database.
January 23, 2018
Need fast querying of text data? Go with Elasticsearch!
- Elasticsearch has allowed us to shave off many (> 100 is some cases) milliseconds from our API response times.
- Elasticsearch coupled with Kibana has allowed us a whole new level of visibility into our log data.
January 18, 2018
Elasticsearch for Log Management
- We have built out an entire service line using Elasticsearch.
- Clients see their compliance audits eased by the Elasticsearch based products.
November 14, 2017
Excelent choice for data analytics and search engine
- Positive impact. We could, for the first time, implement a really useful system to support our business users in working with our big data repositories.
November 10, 2017
ElasticSearch : a powerful and complete tool
- Open source.
- ElasticSearch is immediately ready on the cloud
- Resolving some data volume limitations that a relational database can handle
- Quick to install
- do quick searches on a large volume of data
October 04, 2017
Elasticsearch review
- Most of elasticsearch is free except few things which most of the organizations can live without or have a workaround. Not having to pay Splunk whole bunch of money is a huge ROI right there.
- Indexing the logs and making it searchable has a huge impact on the way we operate. Developers no longer have to log in to the system to know what's happening. Especially when we have hundreds of servers, having a central place for all the logs is essential to operate the system.
- It's really easy to set up and maintain even in a scale. Its hot and warm cluster notion is awesome. The self-maintenance makes a huge impact on the need for system admins.
September 15, 2017
Developer's Elasticsearch Review
- Server cost and infrastructure management became easy.
- Efficiency in data handling.
- Less development time.
August 31, 2017
ElasticSearch is a simple straightforward search engine that literally anyone can get started with!
- When we where initially exploring logging solutions, Splunk was the only vendor in town and they where extremely expensive ($60,000). We haven't revisited them since as ElasticSearch has accomplished all of our needs.
- We haven't spent anything but Admin hours to maintain our ElasticSearch cluster. Right now we haven't incurred any cost of ownership as I have been maintaining the cluster myself.
- We have a huge project to grow a new part of our business, but I am not sure if I can spend the time to really update cluster to support the new Logstash features & any syntax changes so I am reluctant to do so. Time is increasingly becoming scarce, so catering to the latest and greatest features that offer little to our organization isn't something we are interested in pursuing though we are going to need to update the ElasticStack eventually.
- Since all of our metrics are in ElasticSearch, we have had nice trove of data to build our apps around, apps that require specific metrics. Prior to ElasticSearch, we had to build our own tools that handled that metric collection. The cost savings here is that we maintain a simple script that reports back information in our reporting interface vs rolling our own database metric solution that must be modified for every app we develop. That has equated to a huge saving in developer hours in our organization.
June 13, 2017
Powerful and resilient database
- We would not be able to do what we do without Elasticsearch. Our business revolves around it.
April 13, 2017
One of the best search engines out there
- Great search capabilities of our users' transform into more users on the platform
- Analyze the logs and extract data of code issues, allowing us to increase code quality
April 05, 2017
Incredible ROI, Easy to set up
- Optimizations on traditional Relational Databases.
- Minimal operating costs. Open source.
April 05, 2017
Elasticsearch - Great tool for search applications
- Open source project that optimized search views in our application.
- Cost of managing the elasticsearch clusters was added (which is small compared to the gains).
- We are highly satisfied with what we got out of ElasticSearch.
April 04, 2017
Find things with Elasticsearch
- Sped up fault detection, diagnosis and recovery.
- Facilitated getting insights on customer base.
- Helped implement data-driven approach across the whole business.
June 18, 2016
Elasticsearch is the way to go!
- Cost of maintaining a cluster in the cloud (e.g. AWS memory optomized instances) is expensive.
- Project is open sourced, we are free to contribute if a bug is found. And its free!
- More relevant search results are displayed to user. So chances of purchasing a product is very likely.
- Managing an elasticsearch server is very easy. No maintenance is required.
- Documentation is very bad. More time is spent on research than implementation.
June 10, 2016
Elastic - ruler in the index world
- Solved major problems
June 10, 2016
Stretch Your Ambitions With ElasticSearch
- ElasticSearch allows us to build beautiful, responsive web applications that allow users to rapidly filter large amounts of data to find the documents they are interested in.
June 07, 2016
Elasticsearch - go for it!
- Apart from the operational issue that I mentioned previously, the business users have been extremely happy with the results as well as the short time taken to perform the search.
- No negative impacts to date.
- Even though we are only using ElasticSearch for analytics, the possibility of using it for pertinent and supplemental metadata on wells is very possible.
May 10, 2016
How Elasticsearch changed our culture
- ROI since it is open source, yay!
- We have been able to track defects quicker.
- We can detect immediately when deployed changes help or hurt.