Monitoring with traditional tools and manual labor sets you up for failure. With the scope and variability of performance data that needs to be included in your monitoring scope traditional methods fail. Azure is no different.
What we face with Azure monitoring is:
- The resources and performance metrics you need to monitor explode with Azure and microservices
- Azure is dynamic and code deploys are frequent
- Increasing use of containers
- Scaling can be elastic
- Cost is a new integral performance metric
Needless to say these are challenges where humans are not equipped to succeed. Success with these environments relies on automation and machine learning.
The ideal solution is to automate the monitoring of Azure. How can this work?
Step 1: auto-discovery of resources and new deployments
Step 2: auto-capture performance and billing metrics for all resources
Step 3: dynamic monitoring baselines for each metric powered by machine learning
Step 4: anomaly detection identifies anomalies for all metrics for proactive monitoring
Step 5: topology discovery identifies relationships and dependencies between resources without any instrumentation of code
Step 6: intuitive dashboards and topologies lets you understand, navigate, monitor, and govern the IT your business runs on
And yes, you probably guessed right. We have done it and yes - you can try it for yourself.
What’s in it for you?
- Comprehensive monitoring of Azure covering performance and billing for infrastructure, applications, containers, and microservices
- Automated, no manual work - free up time for your team and up and running in 15 minutes
- The insight that lets you operate at the speed your business requires
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Read the complete guide to Azure Monitoring or go ahead and try the AIMS Community Edition
