Loom Systems and Elastic Observability are competing in the monitoring solutions category. Elastic Observability holds the advantage with robust features and perceived value for the price, while Loom Systems offers better pricing and customer support.
Features: Loom Systems provides predictive analytics, AI-driven insights, and preemptive problem-solving. Elastic Observability offers comprehensive data analytics, real-time monitoring, and broader analytics insights.
Room for Improvement: Loom Systems could enhance its data analytics depth, expand integration capabilities, and offer more flexible deployment options. Elastic Observability could improve its user interface simplicity, reduce initial setup complexity, and optimize resource allocation efficiency.
Ease of Deployment and Customer Service: Loom Systems has a straightforward deployment process and strong customer support, minimizing setup complexity. Elastic Observability leverages the Elastic Stack for deployment, which may require more initial setup but benefits from flexibility and documentation.
Pricing and ROI: Loom Systems attracts with lower setup costs and faster ROI through simplified operations. Elastic Observability may need higher initial investment but offers value through its depth and scalability.
One example is the inability to monitor very old databases with the newest version.
Elastic Observability could improve asset discovery as the current requirement to push the agent is not ideal.
Elastic Observability seems to have a good scale-out capability.
What is not scalable for us is not on Elastic's side.
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.
It is very stable, and I would rate it ten out of ten based on my interaction with it.
Elastic Observability is really stable.
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.
All the features that we use, such as monitoring, dashboarding, reporting, the possibility of alerting, and the way we index the data, are important.
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.
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