Elliott Spira | Mon, 16 Apr 2018
A lot of organisations 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 Instance Scheduler is a solution written by the AWS Solutions team, based off 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 schedules) and can perform a far greater set of actions beyond scheduling instances, around preparation for disaster recovery, snapshot creation/retention, patching, auto scaling management and DynamoDB scaling, to name just a few. GorillaStack also completes the feedback loop by providing an Event Log (with audit information and execution history) as well as our Engine Room (providing ROI and savings tracking to report back to the business).
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. Managed solutions have become increasingly popular over the last few years, as businesses realise the critical importance of maintaining focus on business objectives.
While the AWS Instance Scheduler seems straightforward to deploy and use, 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, with all the features that large businesses require (SAML, role based access control, audit history on rule configuration) and all the features that the end users want (notifications, actions, customisation).
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 flavour. If configured for multiple accounts, all schedules will always apply to all accounts and all regions. In GorillaStack on the other hand, for each rule, the user has the option to select which regions and accounts at a per rule basis, so all accounts and all regions is an option, but isn’t mandated.
Within the AWS Instance Scheduler deployment, the user is required to specify a particular Resource Tag Key, which 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.
In GorillaStack on the other hand, we provide the notion of TagGroups. 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 organisations in the world. The common strand that runs through each of our customers is their focus on innovation and progress. The best practicing organisations 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.