OpenText AI Operations Management and Elastic Observability are key competitors in the data management and analytics space. Based on the comparison, Elastic Observability holds a slight advantage due to its flexibility and cost-effectiveness while OpenText is better at event correlation.
Features: OpenText AI Operations Management provides robust event correlation, a unified dashboard, and consolidates data sources with strong tool integration. Elastic Observability focuses on powerful search abilities, data visualization, and offers flexibility in customization which facilitates user-driven dashboard creation.
Room for Improvement: OpenText AI Operations Management could improve complex implementation and setup along with performance and correlation mechanisms. Elastic Observability requires enhancements in APM capabilities, visualization, and predictive analytics. Both would benefit from streamlined deployments and better out-of-the-box features.
Ease of Deployment and Customer Service: OpenText primarily deploys in on-premises with mixed reviews on technical support, facing challenges with offshore assistance. Elastic spans on-premises and cloud environments with high flexibility, though its support varies between effective ticket handling and perceived inadequacy.
Pricing and ROI: OpenText is high-cost with a complex licensing model, but it reduces operational costs through automation. Elastic has attractive pricing for large-scale users with tier-based flexibility, offering a cost-effective solution that caters to a wide range of use cases, albeit with complex license understanding.
Elastic support really struggles in complex situations to resolve issues.
OpenText goes out to bring the right people to answer any inquiries I have.
Elastic Observability seems to have a good scale-out capability.
Elastic Observability is easy in deployment in general for small scale, but when you deploy it at a really large scale, the complexity comes with the customizations.
What is not scalable for us is not on Elastic's side.
There are some bugs that come with each release, but they are keen always to build major versions and minor versions on time, including the CVE vulnerabilities to fix it.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
Elastic Observability is really stable.
For instance, if you have many error logs and want to create a rule with a custom query, such as triggering an alert for five errors in the last hour, all you need to do is open the AI bot, type this question, and it generates an Elastic query for you to use in your alert rules.
It lacked some capabilities when handling on-prem devices, like network observability, package flow analysis, and device performance data on the infrastructure side.
Some areas such as AI Ops still require data scientists to understand machine learning and AI, and it doesn't have a quick win with no-brainer use cases.
Splunk is more business-friendly due to its prettier interface.
The license is reasonably priced, however, the VMs where we host the solution are extremely expensive, making the overall cost in the public cloud high.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
Observability is actually cheaper compared to logs because you're not indexing huge blobs of text and trying to parse those.
From a cost perspective, OpenText Operations Bridge is cost-effective as it saves us man hours.
The most valuable feature is the integrated platform that allows customers to start from observability and expand into other areas like security, EDR solutions, etc.
the most valued feature of Elastic is its log analytics capabilities.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
This integration ensures that when monitoring systems alert and subsequently resolve, tickets are automatically created and closed.
| Product | Market Share (%) |
|---|---|
| Elastic Observability | 4.3% |
| OpenText AI Operations Management | 0.5% |
| Other | 95.2% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 7 |
| Large Enterprise | 35 |
Elastic Observability offers a comprehensive suite for log analytics, application performance monitoring, and machine learning. It integrates seamlessly with platforms like Teams and Slack, enhancing data visualization and scalability for real-time insights.
Elastic Observability is designed to support production environments with features like logging, data collection, and infrastructure tracking. Centralized logging and powerful search functionalities make incident response and performance tracking efficient. Elastic APM and Kibana facilitate detailed data visualization, promoting rapid troubleshooting and effective system performance analysis. Integrated services and extensive connectivity options enhance its role in business and technical decision-making by providing actionable data insights.
What are the most important features of Elastic Observability?Elastic Observability is employed across industries for critical operations, such as in finance for transaction monitoring, in healthcare for secure data management, and in technology for optimizing application performance. Its data-driven approach aids efficient event tracing, supporting diverse industry requirements.
OpenText AI Operations Management centralizes event correlation and monitoring across infrastructures, prioritizing scalability and automation for efficient alert management. It empowers organizations with transparency and insights essential for effective IT resource management in hybrid cloud environments.
OpenText AI Operations Management offers comprehensive solutions for event correlation, integration, and centralized alert management. With capabilities that streamline operations, this tool supports efficient IT management across AWS, GCP, and on-premises environments. Despite requiring improvements in performance and usability, its robust reporting and seamless monitoring provide valuable insights for root cause analysis. Users leverage this platform to integrate event data, automate incidents, and manage hybrid infrastructures effectively, making it a key component in enhancing service perspectives globally. Its heavy architecture and reliance on Java and Flash, coupled with complex licensing and pricing, necessitate attention to functionality and support areas.
What are the key features of OpenText AI Operations Management?OpenText AI Operations Management is widely implemented in industries requiring comprehensive monitoring capabilities. Organizations benefit from its ability to consolidate tools and manage events effectively across hybrid environments. The integration of incident automation and performance evaluation tools is particularly beneficial for those looking to enhance compliance support and reduce response times. Despite some challenges, the platform remains a valuable asset in managing complex IT environments and improving operational effectiveness.
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