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Address your search, observability, and security challenges with Elastic's leading vector database, built for generative AI, semantic search, and hundreds of open, pre-built integrations. Start a 7-day free trial and harness the power of your data, securely and at scale.
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Elastic's Search AI Platform combines world-class search with generative AI to address your search, observability, and security challenges.
Elasticsearch - the industry's most used vector database with an extensive catalog of GenAI integrations - gives you unified access to ML models, connectors, and frameworks through a simple API call. Manage data across sources with enterprise-grade security and build scalable, high-performance apps that keep pace with evolving business needs. Elasticsearch gives you a decade-long head start with a flexible Search AI toolkit and total provisioning flexibility-fully managed on serverless, in the cloud, or on your own infrastructure.
Elastic Observability resolves problems faster with open-source, AI-powered observability without limits, that is accurate, proactive and efficient. Get comprehensive visibility into your AWS and hybrid environment through 400+ integrations including Bedrock, CloudWatch, CloudTrail, EC2, Firehose, S3, and more. Achieve interoperability with an open and extensible, OpenTelemetry (OTel) native solution, with enterprise-grade support.
Elastic Security modernizes SecOps with AI-driven security analytics, the future of SIEM. Powered by Elastic's Search AI Platform, its unprecedented speed and scalability equips practitioners to analyze and act across the attack surface, raising team productivity and reducing risk. Elastic's groundbreaking AI and automation features solve real-world challenges. SOC leaders choose Elastic Security when they need an open and scalable solution ready to run on AWS.
Take advantage of Elastic Cloud Serverless - the fastest way to start and scale security, observability, and search solutions without managing infrastructure. Built on the industry-first Search AI Lake architecture, it combines vast storage, compute, low-latency querying, and advanced AI capabilities to deliver uncompromising speed and scale. Users can choose from Elastic Cloud Hosted and Elastic Cloud Serverless during deployment.
Ready to see for yourself? Sign into your AWS account, click on the "View Purchase Options" button at the top of this page, and start using a single deployment and three projects of Elastic Cloud for the first 7 days, free!
Highlights
- Search: Build innovative GenAI, RAG, and semantic search experiences with Elasticsearch, the leading vector database.
- Security: Modernize SecOps (SIEM, endpoint security, cyber security) with AI-driven security analytics powered by Elastic's Search AI Platform.
- Observability: Use open, extensible, full-stack observability with natively integrated OpenTelemetry for Application Performance Monitoring (APM) of logs, traces, and other metrics.
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Vector Database Capabilities
Advanced vector database supporting generative AI, semantic search, and machine learning model integration through a unified API
Observability Platform
Comprehensive visibility across AWS and hybrid environments with over 400 integrations including CloudWatch, CloudTrail, EC2, and OpenTelemetry support
Security Analytics
AI-driven security analytics with advanced threat detection capabilities for SecOps, SIEM, and cross-surface attack analysis
Multi-Deployment Architecture
Flexible deployment options including serverless, cloud-hosted, and on-premise infrastructure with enterprise-grade security
Machine Learning Integration
Native support for machine learning models, connectors, and frameworks with seamless integration and scalable performance
Artificial Intelligence Analysis
Advanced AI agent that automates data analysis and accelerates root cause investigations
Telemetry Data Integration
Supports unified visibility across logs, metrics, and traces for cloud-native environments
Anomaly Detection
Real-time system anomaly detection to proactively prevent potential incidents
OpenTelemetry Compatibility
Flexible integration with OpenTelemetry standards for standardized observability pipelines
Multi-Architecture Support
Native compatibility with modern architectures including Kubernetes, serverless, and microservices environments
Data Indexing
Indexes Amazon S3 data without transformation, optimizing for data size and performance
Analytics Integration
Supports search, SQL, and machine learning workloads through open APIs with tools like Kibana, Elastic, Looker, and Tableau
Cloud Storage Transformation
Converts Amazon S3 into a hot analytical data lake with native indexing capabilities
Data Access Architecture
Enables direct data access without complex data pipelines, parsing, or schema changes
Scalability Mechanism
Provides infinite scale data analysis with no administrative overhead for re-indexing, sharding, or load balancing
Validated by AWS Marketplace
FedRAMP
GDPR
HIPAA
ISO/IEC 27001
PCI DSS
SOC 2 Type 2
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Customer reviews
3.8
33 ratings
33 AWS reviews
|
219 external reviews
Star ratings include only reviews from verified AWS customers. External reviews can also include a star rating, but star ratings from external reviews are not averaged in with the AWS customer star ratings.
PH Chiu
Log management capabilities impress but setup presents challenges
Reviewed on May 20, 2025
Review provided by PeerSpot
What is our primary use case?
The main use case for Elastic Search is mainly for log management.
What is most valuable?
I appreciate the indexing capabilities and the speed of indexing in their product, which demonstrates how quickly logs are collected and stored. The search capabilities are also valuable.
What needs improvement?
The architecture of Elastic Search could be improved as it is complicated for most general users to build up the environment and maintain the cluster.
Currently, I do not have suggestions for additional functions that could be added to the product.
For how long have I used the solution?
I have been working with Elastic Search for about two years.
What was my experience with deployment of the solution?
I usually use Elastic Search on-premises, which introduces complexity in deployment. Using the cloud version would reduce the complexity of setting up.
What do I think about the stability of the solution?
I would rate the stability for Elastic Search as eight out of ten.
What do I think about the scalability of the solution?
I would rate the scalability as eight.
How are customer service and support?
I would rate technical support from Elastic Search as three out of ten.
The main issue is a general sum of all factors. Being based in Hong Kong means I can only assess the service in my region and cannot speak for other regions based on my experience.
How would you rate customer service and support?
Negative
Which solution did I use previously and why did I switch?
I am currently working with multiple solutions including Elastic Search, Splunk, and Graylog .
How was the initial setup?
The initial setup for Elastic Search is complex.
What other advice do I have?
The real-time analytics capabilities depend on whether you use the paid version or open-source version.
I work with SME users of Elastic Search, though the solution can technically support enterprise customers.
I have not extensively used AI technology with Elastic Search.
I can recommend Elastic Search to other users.
The pricing for Elastic Search rates as four out of ten. Overall, I would rate Elastic Search as seven out of ten.
Which deployment model are you using for this solution?
On-premises
Himanshu Bhati
User optimizes data analysis with advanced search features and seeks expanded functionality
Reviewed on May 13, 2025
Review provided by PeerSpot
What is our primary use case?
I have been using it for a year. The main use cases involved implementing search functionality.
What is most valuable?
When discussing the features of Elastic Search , the full text search capabilities are particularly beneficial for handling large volumes of data.
The full text search capabilities in Elastic Search have proven to be extremely valuable for our operations.
Regarding AI integration, we have not yet implemented any AI-driven projects or initiatives using Elastic Search.
What needs improvement?
There are some features and functionality that could be enhanced in Elastic Search to improve its overall capabilities.
For how long have I used the solution?
I have been using Elastic Search for a year.
What do I think about the stability of the solution?
In terms of performance and stability, Elastic Search has proven to be a reliable solution.
What do I think about the scalability of the solution?
The environment includes multiple users utilizing Elastic Search across different locations.
Which solution did I use previously and why did I switch?
Before implementing Elastic Search, I had experience working with other search engines from different vendors.
How was the initial setup?
The implementation strategy involved specific steps during the setup process to ensure proper configuration.
What was our ROI?
The main benefits observed from using Elastic Search include improvements in operational efficiency, along with cost, time, and resource savings.
What other advice do I have?
I previously used Graylog .
I am currently working with Elastic Search as the primary solution.
My role is Senior DevOps engineer at UVIK Digital.
On a scale of 1 to 10, with 10 being the highest, I would rate Elastic Search as an 8 overall as a product and solution.
Aman K.
A nosql fast, scalable and realiable big data tool
Reviewed on May 11, 2025
Review provided by G2
What do you like best about the product?
its very fast, easy implementation and scalable, offer end to end solution from data ingestion using huge numbers of integrations with other tools and platforms, with its agents and open source supported data ingestion and communication protocols, Elasticsearch as NOsql data base and kibana as their analytics tool, with many options for dashboards reporting and visualization, like lens, tsvb, vega visualization and many more
What do you dislike about the product?
migration from tradition databases which holds many to many relationships in their table schemas are hard to migrate to elastic as their are some other tricks and techniques to do this but I think it can be improved
What problems is the product solving and how is that benefiting you?
its solving latency issues over networks, we are using in cybersecurity solutions, while working with big data tools as it is very fast and offer end to end data solutions with various options available, it offers data analytics as well as ingestion as well as a fast database solutions.
reviewer2702670
Efficient data storage and quick searching boost productivity
Reviewed on May 06, 2025
Review from a verified AWS customer
What is our primary use case?
Our primary use case was primarily for data storage and quick searching. We focused on getting objects from the database and filtering them efficiently. This involved getting and searching through objects.
How has it helped my organization?
Our productivity was consistently maintained while using this database. Its consistent performance allowed us to maintain steady productivity levels.
What is most valuable?
The most valuable feature of Elasticsearch was the quick search capability, allowing us to search by any criteria needed. The searches were executed very quickly, which made the process reliable. Additionally, full-text queries were integral to our usage. Our productivity was consistently maintained with this database. Its consistent performance allowed us to maintain steady productivity levels.
What needs improvement?
It would be useful if a feature for renaming indices could be added without affecting the performance of other features. However, overall, the consistency and stability of Elasticsearch are already commendable, and they should keep up the good work.
For how long have I used the solution?
I have been using Elasticsearch for two and a half years while at this company.
What do I think about the stability of the solution?
The stability of Elasticsearch was very high, and I would rate it a ten. It was consistent and reliable in our usage.
What do I think about the scalability of the solution?
Elasticsearch was decently scalable, matching our data growth. I would rate its scalability a ten.
How was the initial setup?
I was not involved in the initial setup. However, the setup process for smaller projects was straightforward.
What about the implementation team?
One person from our DevOps team was responsible for the maintenance of Elasticsearch.
What's my experience with pricing, setup cost, and licensing?
We used the open-source version of Elasticsearch, which was free.
What other advice do I have?
If a feature for renaming indices could be added without affecting the performance of all other features, it would be nice to have. Overall, I rate Elasticsearch a ten out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
FaisalKhan2
The command-based configuration simplifies data management and setup
Reviewed on May 05, 2025
Review provided by PeerSpot
What is our primary use case?
I have used the Wazuh SIEM tool, an open-source SIEM tool that uses Elasticsearch for indexing. In this SIEM tool, we have a large amount of logs. Data are converted into alerts, then they are stored in our environment for monitoring and security purposes. For storing that data in Wazuh , we use Elasticsearch indexing.
What is most valuable?
Configuring Elasticsearch is much easier compared to comprehending other SIEM tools like Splunk. It has a full command-based access that allows you to configure how much data you want to store and set up retention policies. I can easily change the bandwidth for the network to send log data. Elasticsearch is quite user-friendly and offers a hands-on experience for configuring databases.
What needs improvement?
Elasticsearch should have simpler commands for window filtering. It is primarily based on Unix or Linux-based operating systems and cannot be easily configured in Windows systems. Multi-operating system support would be a great improvement.
For how long have I used the solution?
I have used it for approximately two years.
What was my experience with deployment of the solution?
It can be installed on cloud and locally, with no issues.
What do I think about the stability of the solution?
I would rate the stability of Elasticsearch as a seven. There have been multiple instances where I faced errors due to network bandwidth issues. The data transfer sometimes exceeded the bandwidth limits without proper notification, which caused issues.
What do I think about the scalability of the solution?
I would rate the scalability of Elasticsearch as an eight. The high scalability is somewhat limited by its lack of support for different operating systems other than Linux.
How are customer service and support?
I have never used their technical support. I usually resolve issues on my own or with the help of online community forums.
How would you rate customer service and support?
Positive
How was the initial setup?
The complexity of the initial setup depends on the requirements. In an MSSP scenario, where multiple clients use the same software, there is a need to segregate the data. This can make the setup more complex, especially for a single client where you need to adjust network configurations.
What was our ROI?
For time-saving, Elasticsearch is a good software. It is stable, and we do not encounter critical issues like server downtime, which could result in data loss. There are minor misconfigurations regarding data transfer rates that I have noticed sometimes.
What's my experience with pricing, setup cost, and licensing?
I'm not familiar with the pricing details as it falls under the finance department. My manager handles the costing. However, given that we have been using it for two years, I can suggest that it's priced sensibly for us.
Which other solutions did I evaluate?
If you can't afford a large SIEM tool like Splunk and QRadar, Elasticsearch is a viable alternative.
What other advice do I have?
Overall, considering key aspects like cost, learning curve, and data indexing architecture, Elasticsearch is a very good tool. I would rate it as a nine.