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.
Elastic support really struggles in complex situations to resolve issues.
Problems with Skylar may require longer wait times due to limited resource expertise.
I received excellent support from ScienceLogic.
We have a lab environment to test solutions before offering them to customers, ensuring everything works correctly.
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.
What is not scalable for us is not on Elastic's side.
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.
Elastic Observability is really stable.
Stability should relate to whether the platform fails, stops working, or breaks.
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.
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.
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.
It could be cheaper.
ScienceLogic is not that expensive and is cost-effective overall.
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.
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.
| Product | Market Share (%) |
|---|---|
| Elastic Observability | 2.9% |
| ScienceLogic | 1.7% |
| Other | 95.4% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 4 |
| Large Enterprise | 16 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 11 |
| Large Enterprise | 24 |
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.
ScienceLogic excels in customizable dashboards, seamless integrations, and real-time data analysis, supporting diverse IT environments with multi-tenant capabilities.
ScienceLogic provides robust infrastructure and network monitoring, catering to cloud, applications, and server environments. It supports hybrid setups, integrating with CMDB and ticketing systems while automating incident management. ScienceLogic's PowerPacks eliminate visibility gaps and its adaptable nature supports modern and legacy systems. Offering agentless monitoring, it ensures efficient operations with scalable infrastructure support and detailed reporting. However, the interface complexity and need for professional support can present usability challenges. Enhancements in reporting, application coverage, API support, and customization are desirable for improved user experience.
What are ScienceLogic's most important features?ScienceLogic is often implemented across industries requiring detailed attention to infrastructure and network monitoring. It finds utility in managing hybrid environments, integrating seamlessly with essential systems like CMDB and ticketing platforms. Its scalability and adaptability are valued in unifying complex, diverse environments under one monitoring platform.
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