A lot of organizations come to us for help after they have spent weeks wrestling with AWS Instance scheduler. Other users who have never tried to grapple with the 40 page instructional document ask us why they shouldn’t use it instead of GorillaStack.
The AWS Solutions Team wrote a solution: AWS Instance Scheduler. It is based on an old blog post outlining how to write your own instance scheduler. That guide and this solution provide users with a roll your own instance scheduler. We will outline the key points of difference between the AWS Instance Scheduler and the GorillaStack Rules Engine.
The first and most obvious difference between GorillaStack and the AWS Instance Scheduler is the difference in the feature set.
GorillaStack’s Rules Engine can be triggered on multiple triggers, not just on schedules. It can perform a far greater set of actions beyond scheduling instances. For instance, you are able to create workflows for disaster recovery, snapshot creation/retention, patching, auto-scaling management, and DynamoDB scaling.
GorillaStack also completes the feedback loop by providing 2 key areas:
Many teams that we speak with are time poor and lack the human resources they need for projects that deliver genuine value to their business. As businesses realize the critical importance of maintaining focus on business objectives, managed cloud solutions have become increasingly popular over the last few years.
The AWS Instance Scheduler seems straightforward to deploy and use. However, there are hidden complexities in the nature of its implementation, configuration, and maintenance. These problems are compounded in enterprises, with complex, large, multi-account environments and distinct teams with different requirements.
GorillaStack provides enterprise-grade automation software as a managed service to addresses the needs of both the business and end users. These features include:
GorillaStack has been designed for large, dynamic teams and continues to be developed by their feedback. Some examples of significant features differences that make a difference to users:
Another area where GorillaStack’s Rules Engine really shines is how it manages granular targeting of resources. Users implementing the AWS Instance Scheduler need to make decisions in implementation about whether to implement a cross region or cross account flavor. Whereas in GorillaStack, the user has the option to select which regions and accounts per rule basis. This gives the user the flexibility to implement all accounts and all regions and they aren’t mandated.
Within the AWS Instance Scheduler deployment, the user is required to specify a particular Resource Tag Key. This tag key will be used to consider Resource Tag values to match against each schedule configured. This means that every resource to be targeted can only be identified on the presence of a single tag.
Whereas in GorillaStack, we provide the notion of tag groups. Users specify a combination of Resource Tag Key:Value pairs and matching strategies (case sensitive, case insensitive or using regular expressions). The user can then combine these using a boolean expression to define how to match against resources at runtime. This gives the ability to cut into specific subsets of resources at a far more granular level.
GorillaStack serves small businesses and startups all the way through to some of the largest private enterprises and government organizations in the world. The common strand that runs through each of our customers is their focus on innovation and progress. The best practicing organizations recognize the importance of enabling their teams to focus on the core work that drives towards a business’s overall strategy whilst allowing the undifferentiated heavy lifting to be taken care of by tools that were specifically designed for the job at hand.
First published on 16 Apr 2018. Updated on 2 Mar 2021.