Resource Recommendations

Balancing team infrastructure actions such as rightsizing or refactoring changes against infrastructure commitment savings and long term goals can be challenging. We built the Resource Recommendation engine to help with commitment aware right sizing as well as to provide the flexibility to track recommendations from a number of sources such as AWS Compute Optimizer, other cloud providers, and internal tools for your company. The system can do things such as recommending EC2 sizes, Unattached EBS volumes, and other non commitment type optimizations.

Setting Up Compute Optimizer

  • For AWS, follow the setup instructions to enable Compute Optimizer

    • We pull the recommendations in daily, so you should see recommendations coming into your account within the next day

  • Azure and GCP

    • Contact Sales

Custom Recommendations & Syncing Recommendations with external sources

  • Can be added manually or through our API. For API access contact sales.

  • You can also use the API to query Recommendations and send them to your proffered system.

You can filter specific Recommendations by Segment

Recommendations Overview

The overview provides you a breakdown of recommendations by Provider / Source as well as top recommendations by Type. When new recommendations come in they are flagged as New and team members can mark the stages from New, to Reviewed, to Completed.

Recommendations Table

Recommendations are filterable by the Segment Selector at the top of the page. From there you can do additional filtering in the search for things such as region, type, any field thats available in the table should return those types of recommendations.

You can Search on Region, Type, Resource Family

You can select a specific recommendation and if there are multiple options select an option for the team to take. Choice changes are tracked and you can add more notes to the recommendation. If for example you have EC2 recommendations around CPU and you are more interested in Network and Memory based recommendations, mark the recommendation as not relevant and leave us a note. This acts as a feedback loop for the Reserved.ai team on the relevancy of the recommendations.

Items that are marked as complete or reviewed will show up in the Events stream for team members to review.

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