

OpenText AI Operations Management and Elastic Observability are competing in the AI-driven IT operations management category. Elastic Observability is seen as having the upper hand due to its cost-efficiency, robust real-time analytics, and flexible, scalable solutions, despite some customization and integration challenges.
Features: OpenText AI Operations Management provides extensive integration capabilities and offers a consolidated view that improves cost optimization and transparency. It supports hybrid environments and provides robust event correlation. Elastic Observability stands out for its flexibility, offering customizable dashboards and seamless integration capabilities while handling diverse data sources effectively. It also offers real-time data handling and user-friendly interfaces.
Room for Improvement: OpenText AI Operations Management faces criticism for its high setup complexity and expensive costs. Users also call for UI modernization and improved AI capabilities. Elastic Observability, on the other hand, struggles with its complex query language, customization demands, and confusing pricing models. Both solutions need to enhance scalability and simplify deployment processes.
Ease of Deployment and Customer Service: OpenText AI Operations Management operates mainly on-premises, supports hybrid environments, but has inconsistent technical support quality, leaving some users dissatisfied. Elastic Observability offers deployment flexibility across hybrid and public cloud environments but receives mixed feedback on technical support due to a steep learning curve and calls for better customer engagement.
Pricing and ROI: OpenText AI Operations Management is considered expensive with complicated licensing, deterring smaller enterprises, yet provides good ROI through automation and integration efficiency. Elastic Observability is generally more affordable, especially for large-scale deployments, although licensing complexities pose concerns. Elastic is preferred for cost-effective solutions when balancing budget with monitoring capabilities.
Elastic Observability has saved us time as it's much easier to find relevant pieces across the system in one screen compared to our own software, and it has saved resources too since the same resources can use less time.
Elastic support really struggles in complex situations to resolve issues.
Their excellent documentation typically helps me solve any issues I encounter.
OpenText goes out to bring the right people to answer any inquiries I have.
My team works with the customer success team for technical support and customer service for OpenText AI Operations Management.
I rate the scalability of Elastic Observability as a ten, as we have never seen issues even with a lot of data coming in from more customers, provided we have the appropriate configuration.
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.
The stability and scalability depend on architectural considerations and the company's specific situation.
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.
I would rate the stability of Elastic Observability as a ten, as we don't experience any issues.
We are following approximately 10,000 metrics and logs, and the platform performs pretty well.
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.
Normally, predictive features can be more useful, but this is an end-to-end solution that needs to be customized.
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.
We have a platform where we are collecting metrics, logs, and traces for OpenText AI Operations Management, and if there is an anomaly, we directly open a ticket in our ITSM system.
| Product | Market Share (%) |
|---|---|
| Elastic Observability | 3.0% |
| OpenText AI Operations Management | 0.9% |
| Other | 96.1% |
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 10 |
| 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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