Elastic Observability and Sumo Logic Observability are two leading solutions in the observability space. Sumo Logic Observability seems to have the upper hand due to its advanced predictive capabilities and comprehensive features.
Features: Elastic Observability provides robust data analysis, visualization capabilities, real-time performance monitoring, and effective log management. Sumo Logic Observability offers comprehensive anomaly detection, predictive analytics, and user-friendly dashboards.
Room for Improvement: Elastic Observability needs better integration with third-party tools, enhancements in alerting systems, and improved user interface. Sumo Logic requires a gentler learning curve, better documentation for advanced features, and optimization for initial setup.
Ease of Deployment and Customer Service: Elastic Observability is straightforward to deploy with efficient support channels. Sumo Logic offers flexible deployment options but is criticized for longer setup times. Elastic provides a smoother experience compared to Sumo Logic's more customizable but complex setup.
Pricing and ROI: Elastic Observability is considered cost-effective with relatively lower setup costs and favorable ROI. Sumo Logic, while more expensive, is seen as a worthwhile investment due to its powerful features. Elastic's affordability is attractive, but Sumo Logic's feature set justifies the higher price for many users.
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.
One example is the inability to monitor very old databases with the newest version.
Observability is actually cheaper compared to logs because you're not indexing huge blobs of text and trying to parse those.
Elastic Observability is cost-efficient and provides all features in the enterprise license without asset-based licensing.
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.
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.
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.
Sumo Logic Observability is widely used for log aggregation, analysis, and SIEM capabilities. It assists in monitoring data, creating dashboards, and managing log storage.
Sumo Logic Observability helps teams with logging in production, debugging with trace IDs, and performing queries across large datasets. Developers leverage centralized logs for error detection and tracking metrics like successful transactions and data volume. Security teams integrate it with SOAR systems for automation and enhanced security investigations.
What are the key features?Industries like finance, healthcare, and technology implement Sumo Logic Observability to monitor sensitive data, manage high transaction volumes, and ensure compliance with regulatory standards. Security and development teams benefit from its robust capabilities, enabling effective collaboration and streamlined operations.
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