ScienceLogic and Elastic Observability compete in the monitoring software category. Based on features, ScienceLogic holds an edge due to its infrastructure monitoring and customization capabilities, while Elastic Observability stands out for its strong logging and cost-effectiveness.
Features: ScienceLogic provides robust infrastructure monitoring with dynamic apps and Power Packs, extensive integration capabilities, and multi-tenancy with fine-grained permissions. Elastic Observability integrates well with various solutions via its Kibana dashboard, offering strong logging capabilities, end-to-end monitoring, and flexible data visualization.
Room for Improvement: ScienceLogic could improve reporting functionality, streamline setup for common features, and enhance documentation for troubleshooting backend issues. Elastic Observability needs better application performance metrics, easier configuration tools, and standardized logging.
Ease of Deployment and Customer Service: ScienceLogic supports deployment on-premises and private clouds and is appreciated for its rapid and knowledgeable customer service. Elastic Observability offers deployment across on-premises, hybrid, and public cloud environments, with positive customer service feedback but needing improvement in detailed deployment assistance.
Pricing and ROI: ScienceLogic offers flexible pricing that can become expensive with increased endpoint monitoring, with significant ROI due to reduced incident times. Elastic Observability provides an affordable structure, particularly for large-scale use, with open-source options and positive ROI, but smaller businesses may find pricing tiers less accessible.
The return on investment is fair but often challenged by medium-sized businesses who may question its adequacy.
I received excellent support from ScienceLogic.
Problems with Skylar may require longer wait times due to limited resource expertise.
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.
The stability rating is nine out of ten, acknowledging some bugs, but indicating these are minor issues.
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.
While some other companies have easier APIs, using this solution demands significant expertise.
If the knowledge for implementation could be spread through articles, it would reduce this dependency.
Integrating observability and APM monitoring into the overall portfolio would be beneficial.
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.
ScienceLogic is not that expensive and is cost-effective overall.
It could be cheaper.
the most valued feature of Elastic is its log analytics capabilities.
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.
Every integration, whether for Windows or Linux or even Palo Alto or Fortinet, installs the out-of-the-box dashboards along with it, making it easy to parse incoming data meaningfully and immediately start viewing dashboards to see what's happening in the platform.
Notably, its automation features, such as Runbook action, enable domain experts like me to execute one-click automation solutions, which contributes significantly to reducing MTTR.
The solution excels in three areas: application monitoring, server monitoring, and network performance monitoring.
The CMDB update and the automatic CMDB update are valuable.
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.
ScienceLogic is a comprehensive IT infrastructure monitoring solution that supports networks, servers, cloud environments, and applications, suitable for private cloud and on-premises deployments.
Organizations leverage ScienceLogic for its robust capabilities in monitoring IT infrastructures of all sizes. It offers granular discovery, integration with CMDB, and ticketing systems. Valued for its flexibility, incident automation, remediation, and real-time relationship mapping, it supports hybrid environments with scalable and efficient monitoring functionalities. AI and machine learning enhance its feature set, while ease of deployment and strong support are crucial benefits.
What are ScienceLogic's most important features?ScienceLogic is implemented across multiple industries, including large enterprises, for its capability to handle complex IT ecosystems. Its integration with CMDB and ticketing systems ensures it fits within existing workflows. Organizations use it to monitor diverse infrastructure landscapes, ensuring seamless performance and quick incident resolution.
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