Moogsoft vs. Splunk Application Performance Monitoring (APM)

Moogsoft is well suited in below scenarios - 1. If you are looking for a Event Management layer tools. 2. If you are looking for event correlations, suppression and enhancement feature before opening a ticket in ITSM layer tool. 3. If you are looking for enabling corrective actions even before opening a ticket at MOM layer and AI-ML capabilities in monitoring world.

Incentivized

Good for below cases 1. There is a front end and need to correlate data with front end data 2. multiple microservices and need to check the health of each system 3. correlate data from various sources 4. Application performance is a key to be captured 5. application performance is a key metric.

Incentivized

• Event Management Tools
• Event Enrichment
• AIOps and ML features
• Entropy based prediction

Incentivized

• Detect and Troubleshoot, which allows team to eliminate errors.
• Dashboard creates a one stop location for environment health
• Provides visibility, access to logs and user data real time.

Incentivized

• Automation
• Config files
• Ansible

Incentivized

• I wish Splunk Application Performance Monitoring could integrate with packet capture and analysis tools and provide the integrated analysis results on each tier of the application

Incentivized

Splunk is a great tool for log mining. It is less time-consuming and easy to use. It provides high security, it has customisable dashboards. Furthermore, it acts as a search head, and it gives real-time status. It has the ability to collect data from any source and multiple sources, and it can correlate them.

Incentivized

Moogsoft has the similar capabilities as of SPLUNK, MuleSoft and ServiceNow however it's a unique product in terms of its internal functioning and application framework. While ServiceNow is a SaaS application Moogsoft on the other side comes with On-Prem as well as SaaS offering which gives pretty much flexibility in choosing the right solution as per the customer requirement.

Incentivized

It is good that the correlation analysis and the cause of the failure can be analyzed more extensively through the collection of many data. It is good because it provides a user interface (usability) well and can be used conveniently.
It is good because it can utilize anomaly detection through various AI-based ML models.

Incentivized

• Unique Event Handling and Management
• Situation-based event grouping
• Auto Remediation of issues using tight integration options

Incentivized

• It gave us more quicker alerts in a organize way to distribute to specific audience.
• Helped us to improve our uptime by getting pro-active alerts via emails
• More collaboration in a DevOps culture

Incentivized