Auto-Discovery and Application Footprinting | AppFirst AppFirst Has Merged with ScienceLogic First to Provide Full Stack Visibility across All Hybrid IT Services Learn more

Steady-State Footprinting

Support application capacity and migration decisions based on live application utilization to ensure performance and cost optimization


Without AppFirst

Overprovision and overspend to ensure applications perform well.


With AppFirst

Determine what "Normal" is at a level never before possible.

Real-time Capacity Planning

Traditionally, planning for capacity involved reviewing the historical maximum on a server and over-provisioning by at least 30%. However, it’s becoming increasingly more important to support service levels at the lowest possible price. Only AppFirst enables capacity management based on app performance. You can now add or remove resources based on live application behavior, the direct measure of service effectiveness. This has never been able to be accomplished until now.

Sizing Applications for New Environments

Whether you are consolidating servers for pure cost reasons, or looking to migrate to the cloud for all of its benefits, you'll need to size what the new environment looks like. Understanding how these applications behave across server instances is key to making such moves as painless and inexpensive as possible.

It starts at understanding CPU and memory resources across servers and extends into exploring bandwidth and IO requirements. Additionally, migrations require an understanding of what communications are taking place between the application and any external resources. Only AppFirst provides the granular details required to ensure cost and performance optimization when migrating applications to new, shared infrastructure.

Read how a Global Pharmaceutical company leveraged AppFirst to footprint their applications.


Baselining and DevOps

You’ve successfully sized your application and migrated it to a new environment. Ensuring ongoing performance is now critical. This includes scaling out when load gets high and seeing alerts when things become wobbly. Now, when a user says “the system seems slower today” you have a historical footprint to compare against. This is critical for agile and continuous development. When developers are pushing code without the involvement of Ops, you can now look and say “before this release, memory usage was X and it's now 40% higher than normal after this release.”

Real-time Capacity Planning