Overview
Product Information on Elastic Search
What is Elastic Search?
Elastic Search Pricing
Overall experience with Elastic Search
“High Performance and Scalability With Elastic Search Offset by Management Challenges”
“Unstructured Data Handling Benefits, with a UI Learning Curve”
About Company
Company Description
Elastic enables organizations to securely harness search-powered AI so anyone can find the answers they need in real-time using all their data, at scale. By integrating AI with search technology, it facilitates the discovery of actionable insights from large volumes of both structured and unstructured data, addressing the need for real-time, scalable data processing. Our Elasticsearch Platform delivers search-powered AI for observability, security and search. Companies can now solve real-time business problems and achieve better business outcomes by taking advantage of massive amounts of structured and unstructured data, securing and protecting private information more effectively, and optimizing infrastructure and talent resources more efficiently. Elastic’s complete, easy-to-use cloud-based platform offers solutions in search, security, and observability, aimed at aiding businesses in leveraging AI technology securely and effectively.
Company Details
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Key Insights
A Snapshot of What Matters - Based on Validated User Reviews
Reviewer Insights for: Elastic Search
Deciding Factors: Elastic Search Vs. Market Average
Performance of Elastic Search Across Market Features
Elastic Search Likes & Dislikes
The strongest aspect of Elastic Search is its speed and flexibility in handling large volumes of data with near real time search capabilities. Its distributed architecture makes it highly scalable and suitable for enterprise workloads. Key strengths include Powerful full test search with high performance. Real time indexing and querying capabilities. Support for vector search and semantic search use cases. Flexible schema design for structured and unstructured data. Strong ecosystem integration with visualization and monitoring tools. Ability to power enterprise search , logging, and analytics on a single platform. From a developer and data perspective, the RESTful APIs and rich query capabilities make search application and analytics workflows.
We have a lot of unstructured data types flowing into the pipeline that we then run through our ML models and with ES, we can easily see if the expected data is coming into the top of the pipeline funnel. This helps with troubleshooting. The UI takes a while to learn and could be simplified but once you know how to use it, it's pretty straightforward.
I like its ability to handle large-scale data with high performance and near real-time search capabilities. The flexibility of the query DSL, powerful aggregations and developer friendliness with an amazing support team from Elastic made it a success story in every project I used it. With it supoorting vectors storage is an additional feather it its hat allowing us to build RAGs on top of it.
One of the main challenges is operational complexity , particularly when managing large clusters or high -ingestion workloads. proper tuning and monitoring are essential to maintain performance. Resource usage ca grow quickly without proper index life cycle management. Query optimization and relevance tuning can require specialized expertise. Cluster scaling and shard management require careful planning. Cost considerations on licensing. Debugging is complex in distributed environments.
There is a learning curve with the product so it's not something we can enable larger groups on. We tend to only have specialized roles use the search options which can create some internal bottlenecks. Not a big deal but a friction point.
Back in 2015, it was mostly about managing operational complexity at scale. Finding those specific config options to tune the cluster and optimize the resources. But as it got more widely adopted and documentation increased, along with a great support team from Elastic, the problems went away.
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Peer Discussions
Elastic Search Reviews and Ratings
- Sdet10B+ USDBankingReview Source
High Performance and Scalability With Elastic Search Offset by Management Challenges
Overall, our experience with Elastic search has been very positive, particularly for implementing enterprise search and real time analytics across large datasets. The platform provides strong indexing and search performance, enabling teams to quickly retrieve and analyze data across multiple sources. As the platform evolves toward AI -powered search capabilities, it provides additional value though semantic search, vector search, and relevance tuning. From an enterprise perspective , it supports a wide range of use cases including observability , log analytics, application search and knowledge discovery. However, successful adoption requires proper architecture planning, cluster management , and governance to ensure performance and cost efficiency at scale. - PRINCIPAL DATA ENGINEER1B-10B USDTravel and HospitalityReview Source
Elasticsearch: A Powerful, Scalable, & Versatile Platform for Search, Observability & RAGs
Started using ElasticSearch back in 2015. Since its induction, it has been a cornerstone in my various projects, consistently delivering scalability, flexibility across search, observability, security, APM and type ahead use cases. It has way superior features than the open-source competitor, and the cost of ingesting & storing data is decoupled from the licensing cost. Being made open-source, it has just exploded with a vast community of developers helping out the product in the long run. - SENIOR PRINCIPAL ENGINEER - FULL STACK1B-10B USDBankingReview Source
Elastic Search Delivers Strong Performance but Increases Operational Complexity Challenges
Elastic Search has become the backbone of our search and observability use cases. We use it to power merchant/transaction lookups with fuzzy matching and scoring, and to centralize logging for fast, reliable investigations. Performance has been consistently strong at scale, and the platform's flexibility lets us evolve the schema and relevance models without vendor lock-in. The trade-off is operational complexity--tuning shards, upgrades, and capacity planning require discipline--but the gains in speed to value and developer productivity have far outweighed the overhead. - VP, CUSTOMER SUCCESS50M-1B USDSoftwareReview Source
Unstructured Data Handling Benefits, with a UI Learning Curve
We use ES as the main indexing backbone for our data pipeline that feeds our Alerts system. It has scaled with our customer base and increased volume over time (6+ years as a customer from angel through series B). It's been a good fit for our use cases so far. - ENGINEER1B-10B USDTelecommunicationReview Source
A powerful, scalable platform with a steep learning curve, best for organizations with dedicated technical expertise.
Elasticsearch has been a game-changer for our organization. The platform's powerful search capabilities have significantly improved our data analysis and retrieval processes. It's incredibly reliable and scalable, handling large datasets with ease.