OpenText AI Operations Management and Elastic Observability compete in the domain of IT operations management solutions. Elastic Observability appears to have the upper hand due to its real-time data visibility, ease of deployment, and its strong open-source community support.
Features: OpenText AI Operations Management provides robust event correlation, infrastructure management capabilities, and scalability, along with efficient automation for repetitive tasks. Elastic Observability offers real-time data visibility, seamless search capabilities, and integrates well with various solutions, supported by its flexible and feature-rich open-source framework.
Room for Improvement: OpenText AI Operations Management is criticized for complex setup and performance issues if not configured properly. Its licensing model is also seen as a drawback. Elastic Observability has limitations in visualization and monitoring capabilities, with a need for enhancements in metric tracking and backend process control. Both tools can improve in ease of use.
Ease of Deployment and Customer Service: OpenText AI Operations Management primarily uses on-premises deployment with hybrid cloud capabilities, though setup complexity and mixed customer support experiences are a concern. Elastic Observability supports hybrid and public cloud deployments, earning praise for ease of use. Customer service feedback for Elastic varies, with some noting a lack of responsiveness.
Pricing and ROI: OpenText AI Operations Management is associated with high costs and complex licensing that may deter smaller businesses, but its automation can reduce operational costs, offering substantial ROI for some modules. Elastic Observability is generally perceived as more affordable and cost-effective due to its competitive pricing and open-source components, leading to a favorable ROI.
OpenText goes out to bring the right people to answer any inquiries I have.
Elastic Observability seems to have a good scale-out capability.
What is not scalable for us is not on Elastic's side.
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.
Elastic Observability could improve asset discovery as the current requirement to push the agent is not ideal.
Splunk is more business-friendly due to its prettier interface.
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.
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.
With its automation capabilities and runbooks, it reduces after-hours costs by automatically handling recurring issues and known scenarios.
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.
Elastic Observability is primarily used for monitoring login events, application performance, and infrastructure, supporting significant data volumes through features like log aggregation, centralized logging, and system metric analysis.
Elastic Observability employs Elastic APM for performance and latency analysis, significantly aiding business KPIs and technical stability. It is popular among users for system and server monitoring, capacity planning, cyber security, and managing data pipelines. With the integration of Kibana, it offers robust visualization, reporting, and incident response capabilities through rapid log searches while supporting machine learning and hybrid cloud environments.
What are Elastic Observability's key features?Companies in technology, finance, healthcare, and other industries implement Elastic Observability for tailored monitoring solutions. They find its integration with existing systems useful for maintaining operation efficiency and security, particularly valuing the visualization capabilities through Kibana to monitor KPIs and improve incident response times.
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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