Sold by: Pulse by BigData Boutique
Deployed on AWS
Pulse for OpenSearch is a powerful, easy-to-use solution that provides deep insights into your OpenSearch clusters. With real-time visualizations, proactive alerts, health assessments, and actionable recommendations, Pulse ensures optimal performance, stability, and scalability for your OpenSearch environment.
4.3
Overview
Pulse for OpenSearch is a robust platform built to enhance the performance and reliability of your OpenSearch clusters. Developed by world-class OpenSearch experts, Pulse delivers proactive monitoring with real-time metrics, intuitive dashboards, and automated alerts to help you address potential issues before they impact your operations.
One of the key features of Pulse is its Health Assessments, which continuously evaluate your OpenSearch environment, detect anomalies, and offer visibility into cluster health. With tailored recommendations for maintaining peak performance, Pulse empowers you to manage challenges like search latency and resource bottlenecks effectively, ensuring that issues are resolved before they escalate.
Beyond health assessments, Pulse provides comprehensive insights into critical metrics, including query performance, index health, and resource utilization. With actionable recommendations and automated insights, Pulse ensures your clusters operate smoothly and scale effortlessly.
Designed for ease of use and scalability, Pulse integrates seamlessly with OpenSearch, offering a unified monitoring solution for both small deployments and large, complex infrastructures. No matter the size of your setup, Pulse is built to support the growing demands of modern search-driven applications.
Highlights
- Comprehensive Health Assessments - Pulse Health Assessments provide continuous analysis of your OpenSearch clusters, identifying potential risks and performance issues. With actionable reports and tailored recommendations, you can maintain optimal system health and prevent costly disruptions before they happen.
- Real-Time Monitoring & Visual Dashboards - Pulse offers intuitive, real-time dashboards that visualize critical OpenSearch metrics such as index health, query performance, and cluster utilization. Gain instant insights and stay on top of issues before they impact performance.
- Proactive Alerts & Automated Insights - Set custom alerts for key performance indicators and receive actionable recommendations based on the behavior of your OpenSearch cluster. The Pulse intelligent alerting system ensures you are always one step ahead of issues, reducing downtime and increasing reliability.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
OpenSearch Cluster | 1 OpenSearch cluster (up to 25 data nodes) | $5,000.00 |
Large OpenSearch Cluster | 1 Large OpenSearch cluster (> 25 data nodes) | $6,500.00 |
Small OpenSearch Cluster | 1 Small OpenSearch cluster (<=5 data nodes) | $2,940.00 |
Dimensions summary
You pay per OpenSearch cluster, billed under a contract. The three dimensions map to cluster size by number of data nodes. Small OpenSearch Cluster covers a cluster with 5 or fewer data nodes. OpenSearch Cluster covers a cluster with up to 25 data nodes. Large OpenSearch Cluster covers a cluster with more than 25 data nodes. You choose the dimension that matches each cluster's node count. Pricing scales with the number of clusters you connect and the size band each cluster falls into.
Top-of-mind questions for buyers
A cluster is one OpenSearch deployment you connect to Pulse. Each distinct cluster endpoint counts as one cluster. You pick the dimension by data node count: 5 or fewer for Small, up to 25 for the standard cluster, and more than 25 for Large.
Each dimension covers a set node range. If a cluster's node count crosses into another band, it fits a different dimension. You would connect it under the matching size dimension for its current node count. Charges reflect the size band each cluster falls into.
Charges apply per cluster. Each connected cluster is billed under the dimension matching its data node count. Small, standard, and Large clusters bill independently. Your total is the sum across every cluster you connect, based on each one's size band.
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Support
Vendor support
Buyers of Pulse can expect dedicated, expert-level support to ensure smooth operations of their OpenSearch clusters. Whether it is troubleshooting, performance tuning, or product guidance, our team is here to help.
You can connect with our experts directly via the Pulse Support Inbox for efficient communication and quicker issue resolution. For more information or to get assistance, reach out to us at info@pulse.support .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
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Negative reviews
AI generated from product descriptions
Real-Time Cluster Monitoring
Real-time dashboards that visualize critical metrics including index health, query performance, and cluster utilization with instant insights into OpenSearch cluster status.
Health Assessment and Anomaly Detection
Continuous analysis of OpenSearch clusters that identifies potential risks, performance issues, and anomalies with visibility into cluster health and tailored recommendations.
Proactive Alerting System
Custom alerts for key performance indicators with automated insights based on cluster behavior to enable early issue detection and prevention.
Performance Metrics Analysis
Comprehensive insights into critical metrics including query performance, index health, resource utilization, and search latency identification.
Automated Recommendations Engine
Actionable recommendations and automated insights generated from cluster analysis to address performance challenges and resource bottlenecks.
Real-Time Anomaly Detection
Unsupervised machine learning models train on every metric at the edge, scoring anomalies in real time with no configuration required.
Network Topology and Traffic Analysis
Live topology maps built from LLDP, CDP, BGP, and OSPF protocols; NetFlow v5/v7/v9, IPFIX, and sFlow v5 analysis with top talkers and Sankey diagrams; SNMP device auto-discovery across 200+ vendor profiles with trap receiver decoding 150,000+ trap definitions from 800+ vendors.
Per-Second Metrics Collection
Collects and processes per-second granularity metrics across 850+ auto-discovered integrations covering operating systems, Kubernetes, databases, web servers, message brokers, and AWS services.
AI-Powered Root Cause Analysis
One-click AI investigation on every alert that returns root-cause hypothesis with supporting evidence; generates alert configurations from plain English descriptions and back-tests against historical data.
Edge-Based Data Processing
Metrics are stored and processed on infrastructure with only views streaming to cloud; supports eBPF and OpenTelemetry ingestion with approximately 5% CPU core and 150 MiB RAM resource utilization on typical production systems.
Anomaly Detection
Machine learning-powered anomaly detection to proactively identify performance deviations and potential issues
Response-Time Analysis
Root cause analysis using response-time analysis to identify performance problems in databases
Cross-Platform Database Support
Support for monitoring and optimization across Aurora, SQL Server, MySQL, PostgreSQL, Oracle, and other databases on AWS RDS, EC2, and on-premises environments
Query and Execution Plan Diagnostics
Comprehensive performance data visibility including SQL statements, indexes, execution plans, and blocking analysis
Automated Alerting and Management
RESTful management APIs and customizable email alert templates for automated instance monitoring and alerting
Standard contract
No
Customer reviews
SureshBadavath
Search capabilities have powered our product while analytics now drive log-based insights
Reviewed on Apr 06, 2026
Review from a verified AWS customer
What is our primary use case?
My primary use case is powering up the search in the product.
What is most valuable?
It is quite easy, and I think it was really great the way they offered the services. I can just click provision the instance into AWS and then I'm good to play with the fast API.
The analytics is the way I have set up, using the fast API as a wrapper on top of Pulse Elasticsearch and OpenSearch and then exposing these as APIs to our front-end systems. All of the logs go to log files, and I ship those logs to our database and then create analytics on top of it.
What needs improvement?
I think the way they support the ingesting service for Pulse Elasticsearch and OpenSearch is not really great. If I got to attach a few more records, they don't support it. It's all rewrite every time; basically, I truncate the index and then deploy a new index and do a blueprint deployment. I think if they can support ingesting for a few records at any time, that would be great. It will just go ahead and attach to the existing instance.
I think instead of supporting the machine learning services, I'm not sure how many people are using their machine learning service. Rather, if they can support the LLM querying inside Pulse Elasticsearch and OpenSearch, that would be great.
They should support the LLM integration inside Pulse Elasticsearch and OpenSearch.
For how long have I used the solution?
I have been working with Elasticsearch for probably two or three years now.
What do I think about the stability of the solution?
To be honest, Elastic didn't solve the problem for us, and I don't recall why I moved to Pulse Elasticsearch and OpenSearch, but I think Pulse Elasticsearch and OpenSearch is what we are on now completely.
What do I think about the scalability of the solution?
I would not really rate my experience great in Elastic, but in Pulse Elasticsearch and OpenSearch. It's the same thing any which way, but we never scaled up in Elastic.
How are customer service and support?
The alerting feature? I think we have it. We have an alert coming to Slack for all of the 500 errors, but we never added the PagerDuty or any other alerting system into it.
Which solution did I use previously and why did I switch?
I tried Algolia, then moved to Elastic, and then moved to Pulse Elasticsearch and OpenSearch.
Which other solutions did I evaluate?
I think we provisioned an instance inside AWS for Elasticsearch by Elastic.
What other advice do I have?
I have not used the SQL query support feature.
I did assess the RESTful API support.
I have not utilized Pulse Elasticsearch and OpenSearch machine learning features, as there was no use case for me. But I think I did add the LLM outside that, the machine learning that is provided by Pulse Elasticsearch and OpenSearch. I did add the LLM querying outside the product. So if at all I need, I just call the generate services and do a little bit of research and development there.
I would rate this product an eight overall.
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?
Amazon Web Services (AWS)
Rafayel Agamyan
Search performance has transformed customer document access and continues to improve satisfaction
Reviewed on Dec 04, 2025
Review from a verified AWS customer
What is our primary use case?
Pulse Elasticsearch and OpenSearch has been utilized for around three years. The main use case is using it as a search engine for customers. Depending on the company, insurance companies search for PDF documents and claims, while medical insurance companies search for medical notes and other medical-related information.
What is most valuable?
The best features that Pulse Elasticsearch and OpenSearch offer include the scalability, so no matter how many indexes are stored and searched, it scales very well.
Scalability has helped the organization in many situations. For example, when disk space runs out, it automatically increases the disk space to serve customer needs.
In addition to scalability, the speed of Pulse Elasticsearch is greatly appreciated, as it is very fast compared to other resources used for that purpose, which makes customers happy.
For how long have I used the solution?
Three and a half years have been spent working in the current field.
What do I think about the stability of the solution?
Pulse Elasticsearch and OpenSearch is stable.
What do I think about the scalability of the solution?
The scalability of Pulse Elasticsearch and OpenSearch is working very well, and that is one of the key metrics of the service.
How are customer service and support?
Pulse Elasticsearch and OpenSearch customer support has been interacted with many times, and it was always extremely helpful. A rating of 10 would be given to customer support.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
A different solution was previously used, and the only reason for switching was the speed, as the old solution was very slow on searches.
Since using Pulse Elasticsearch and OpenSearch, migration from other search services has occurred without experiencing any issues so far, unlike the other systems where a lot of latency was faced and caches had to be warmed up before searching. With Elasticsearch, everything is much easier.
What was our ROI?
A return on investment has been seen in terms of time saved, as dealing with Pulse Elasticsearch and OpenSearch through its blue-green deployment means only scheduling and monitoring its completion is required without spending a lot of time on upgrades.
Which other solutions did I evaluate?
The decision to use Pulse Elasticsearch and OpenSearch was made by a higher level without evaluating other options.
What other advice do I have?
Customer satisfaction has significantly improved since switching to Pulse Elasticsearch and OpenSearch from old legacy services, as many insights from customers indicate that latency has changed dramatically and the speed is significantly better now.
For others looking into using Pulse Elasticsearch and OpenSearch, it is definitely recommended to give it a try, especially if old legacy systems that are slow are currently being used. Lately, OpenSearch has come up with a new version that greatly enhances speed, so trying it is encouraged and it will be loved. A review rating of 9 reflects the overall satisfaction with Pulse Elasticsearch and OpenSearch.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)