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 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!
- Business Problems Solved
- Pros
- Cons
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 47)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
- The license is quite expensive
- The consultation and operation cost is also a high cost
- The performance during the peak period is not stable enough but there isn't good temporary solution
December 29, 2023
Elasticsearch Overall Review
- Elasticsearch provides a convenient way to analyze data from various data sources.
- Visualizations from data analysis provide easy guidance to management.
- Elasticsearch reduces overall IT/Administrative costs
November 09, 2021
Elasticsearch is a tricky, but great data platform
- Greatly reduced data-in-transit and at-rest overheads
- Provided us with a truly scalable solution for our data
- Kibana offers a reporting platform based on our custom queries. Extremely useful for reports from automated test executions.
August 18, 2021
Elasticsearch Observability Enables an Outstanding Capacity To Transform IT Operations
- Data driven enabler
- Capacity to scale
- Unify IT operations
- Most of our investment is in programming hours which is expensive
- Easy to set up nodes
- Free version has a lot of the great basic features
May 17, 2021
Elasticsearch OSS Review
- Finding talent.
- Using open source versus paid.
- Management if it gets too big.
April 01, 2021
Elasticsearch Review
- It has allowed fast searching on large datasets which allow our customers to conduct business in a timely and simple manner.
January 13, 2021
Elasticsearch: for searches, you know!
- Faster searches on our application have resulted in better usability and increased application use
- Analytics dashboard has given our managers a better understanding of day-to-day activities
- Being a backup data store, we need not touch SQL database while doing data dumps for local data science projects
March 06, 2020
Elasticsearch: Open-source, Fast, Excellent!
- Free alternative has given us incredible value at the cost of time and energy.
- We spend 70-90% less time on investigations of incidents.
February 14, 2020
Elasticsearch helps you find the information you need!
- No initial investment required.
January 06, 2020
Brilliant search powerhouse
- It's simple and the fast set-up spares you lots of money on the set-up stage.
- Immediate results give back a lot without spending too much.
- Improves developer velocity
- Improves ability to diagnose problems
December 02, 2019
Elasticsearch is a great product!
- Was able to demo its background use at a conference quickly.
- Was able to index 100s of documents quickly.
- Easy to create queries.
November 19, 2019
Reliable and affordable solution which is figuring as a industry pattern for managing huge data searching.
- Shorter outages since it is easier to figure out root causes
- Better visibility over your environment
- Security keeping your data at your own environment
October 30, 2019
Win quickly with Elasticsearch
- Easy to maintain.
- Simple to use.
- Great monitoring out of the box for the product.
October 26, 2019
Elasticsearch is the future!
- I am not in finance and I suspect even if I was this would be hard to measure. But for sure, Elasticsearch has enabled us to have the most flexible data model in the industry for our customer's data, and in doing so we have attracted many many technical customers and got much of their $$$.
- One problem with Elasticsearch is that because it runs on the JVM, there can be some stop-the-world JVM garbage collections happening that can take down nodes and reduce indexing speed. The solution for that tends to be "let's just upgrade the CPU on that machine". And before you know it you are paying $$$ because this'll happen with 40+ machines.
- On the other hand, I do think that ES is more efficient than other systems and so it requires fewer nodes to keep it highly tolerant and available, so we probably saved some money that way.
October 09, 2019
Very useful for eCommerce
- Investment is primarily the cost of developer time and maintenance time and with its cost being zero, Elasticsearch has a positive ROI.
June 06, 2019
An amazing search engine
- It reduced our solutions' time-to-market, as it's very easy to set up.
- It greatly boosted the user experience with near-instant search results.
- Elasticsearch can give you insights based on predictability, to do a Capacity Plan for infrastructure metrics.
- The Visualization and Dashboards can give you a real view of business KPIs.
- With OOTB anomaly detection, you can see potential issues with systems.
February 27, 2019
ElasticSearch handles a large number of requests quickly and easily
- ElasticSearch was able to meet the high demands of our product when it mattered most.
- Implementation of ElasticSearch was easy and quick, saving on the cost of implementation.
- Managing ElasticSearch is very easy. With the right monitoring tools in place, it really is "set it and forget it".
February 27, 2019
Works well but is difficult to set up and manage
- Allowed us to give accurate, real-time feedback for RPA.
- Able to see what works, and what doesn't work.
- Allows us to identify consistent problems across 60+ different processes.
Score 7 out of 10
Vetted Review
Verified User
- (Negative) Expense: Just Time. Early on, I had issues getting it installed in an exotic distribution, so labor/hours invested.
- (Positive) Configuration and Modularity: You don't *have* to implement a full ELK stack. In fact, you could just run one, two or three components. However, marrying up ES with something like Syslog-ng, you combine two very powerful, feature-rich software packages in their own right, into an amazingly powerful data collection and gathering tool.
- (Positive) Shallow learning curve: if you can write your own Unix configuration files, you will be able to maintain and develop on Elasticsearch.
January 10, 2019
The Best Available
- Time saving
- Employee time
- Innovation
October 15, 2018
The gold standard for text-based search
- Quick to implement a powerful, effective search product that makes our user happy
- Cheap, fast, and scalable - an ideal component of our stack
- We have had great luck with implementing Elasticsearch for our search and analytics use cases.
- While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
- We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.