

OpenText AI Operations Management and Azure Monitor are two prominent operations management solutions competing mainly in the enterprise monitoring space. OpenText AI Operations Management seems to have the upper hand with its comprehensive event correlation and automation features, particularly beneficial for large Managed Service Providers. Azure Monitor excels in integration with Azure services, log analytics, and performance monitoring.
Features: OpenText AI Operations Management offers comprehensive event correlation, automation capabilities, and effective alert management. It integrates with various systems, providing a single point of truth for monitoring infrastructure. Azure Monitor has strong integration with Azure services, supports log analytics, and provides robust performance monitoring capabilities. It is noted for its efficient data ingestion and real-time insights.
Room for Improvement: OpenText AI Operations Management requires a complex initial setup and consumes significant system resources, limiting scalability. Its user interface is not visually appealing, and integration between components needs enhancement. Azure Monitor's alerting mechanisms and user interface navigation could be improved. Its pay-as-you-go pricing model can be expensive, depending on usage and data ingestion.
Ease of Deployment and Customer Service: OpenText AI Operations Management is mainly deployed in on-premises and hybrid environments, often resulting in a complex setup process. Customer support has mixed reviews, with some users finding it slow. Azure Monitor benefits from a simpler setup process typical of cloud solutions and generally receives positive reviews for its customer service, especially in effective problem resolution.
Pricing and ROI: OpenText AI Operations Management is seen as expensive due to its complex licensing model but offers positive ROI through automation of manual processes. Azure Monitor operates on a cost-effective pay-as-you-go model, with competitive pricing for flexible integration with Azure services, although usage costs can be high. Both products provide valuable ROI, with Azure Monitor offering affordability and OpenText delivering enhanced automation benefits.
Azure Monitor helps prevent impacts on their system.
However, the second-line support is good.
Users end up getting no resolution from their team because they're outsourced vendors, and they don't have deeper expertise over any of the products they are referring to.
I would rate the support for Azure Monitor as a seven.
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.
With APM, you can go heavy or you can go light. It just depends on what you want, what your use case is, and how reactive you want to be to system load or resilient to failure.
Azure Monitor is very scalable; there are no issues with scalability for different kinds of businesses.
The stability and scalability depend on architectural considerations and the company's specific situation.
Azure Monitor is working fine, yet I face a costing issue as if there are a lot of logs collected in the workspace or in the center, it becomes very costly.
We are following approximately 10,000 metrics and logs, and the platform performs pretty well.
If Azure Monitor can independently add one gigabyte, two gigabytes, or five gigabytes at least to log storage, I can fix the logs without syncing with Log Analytics Workspace and Sentinel.
The cost skyrockets once you start using it, and there are complaints that the actual cost of the Kubernetes cluster was less than the cost they were incurring for Azure Monitor.
The challenges with Azure Monitor are that it's initially complex to set up because you need multiple components.
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.
When I export logs into the application, workspace, log analytic workspace, and into Sentinel to read reports, I need to add storage, which increases the cost.
From a cost perspective, OpenText Operations Bridge is cost-effective as it saves us man hours.
The alerting features definitely help in reducing operational downtime for my customers by allowing us to get notifications in advance and take active actions.
I also appreciate the ability to measure feature activity, see what types of devices they are on, follow specific use cases, and measure the amount of traffic going to a particular application.
Resource monitoring is essential.
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 | Mindshare (%) |
|---|---|
| Azure Monitor | 2.1% |
| OpenText AI Operations Management | 1.1% |
| Other | 96.8% |

| Company Size | Count |
|---|---|
| Small Business | 23 |
| Midsize Enterprise | 7 |
| Large Enterprise | 29 |
| Company Size | Count |
|---|---|
| Small Business | 10 |
| Midsize Enterprise | 7 |
| Large Enterprise | 35 |
Azure Monitor delivers comprehensive monitoring across applications and cloud resources, integrating seamlessly with Azure services to enhance performance tracking and telemetry analysis.
Azure Monitor extends monitoring capabilities for applications, infrastructure, and security, featuring easy integration with Azure and third-party tools. It supports dynamic alerting and telemetry, offering log analytics and metrics gathering. Users benefit from its alert system and intuitive dashboards, making it a preferred choice for multi-cloud and infrastructure monitoring across diverse IT environments. While users seek improved query building and interface navigation, they appreciate its scalability and cost-effectiveness.
What key features does Azure Monitor offer?Azure Monitor sees widespread use for infrastructure and application monitoring across industries. Companies rely on it for performance tracking and incident management, often integrating it with Application Insights for enriched data analysis. Organizations use it to monitor servers and cloud services, utilizing its capabilities in DevOps practices and during cloud transformation processes for analyzing database metrics and ensuring efficient application functioning.
OpenText AI Operations Management excels in event correlation, integrating diverse systems, and centralizing dashboards for efficient alert management. It supports extensive analysis through its data lake and enhances operational management with seamless automation and integration.
OpenText AI Operations Management is designed to consolidate monitoring inputs, enabling holistic analysis and faster root cause identification. Supporting compliance and vulnerability management, its role-based access ensures data security. With scalability and BI tool compatibility, users appreciate its ability to manage server operations efficiently in hybrid and large-scale infrastructures. Improvements are needed in UI design, setup complexity, and resource efficiency, as well as enhanced integration with third-party products and legacy applications. Comprehensive AI and predictive features, simplified licensing, and enhanced stability and reporting could further its effectiveness.
What are the key features of OpenText AI Operations Management?In industries like financial services and telecommunications, OpenText AI Operations Management is implemented for its robust event management capabilities, ensuring compliance and security standards are met. It effectively automates monitoring and server operations, providing businesses with a reliable system for data consolidation and security management.
We monitor all Cloud Monitoring Software reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.