Recommendations
3 minute read
The Recommendations page surfaces actionable opportunities to reduce your cloud spend, with a page per cloud: AWS recommendations come from AWS Cost Optimization Hub, and Azure recommendations come from Azure Advisor.
Each recommendation identifies a specific resource that could be optimized, shows the estimated monthly savings, and describes what action to take. Recommendations are informational: changes are made in your cloud provider, not from this page.
AWS savings figures are estimated on a before-discount (list price) basis. Azure monthly savings are derived from Advisor’s annual savings figure.
Access the Recommendations page
- Log in to the CloudBolt Platform.
- In the sidebar, select Recommendations to expand it, then choose AWS or Azure.
Recommendation categories
AWS recommendations are organized into four categories:
- Rightsizing: Resources that are overprovisioned relative to their actual usage. Actions include: downsizing to a smaller instance type, migrating to Graviton-based instances, or upgrading to a newer generation.
- Idle: Resources that show little to no usage and could be stopped or deleted.
- Commitment: Opportunities to purchase Savings Plans or Reserved Instances based on your usage patterns.
- Configuration: Other optimization actions that fall outside the above categories.
Azure recommendations are categorized by Advisor’s recommendation type and carry an Impact rating of High, Medium, or Low.
Dashboard
Each provider’s page opens with a summary, a Savings by Category chart, and a table of the top accounts (AWS) or subscriptions (Azure) by savings opportunity.
AWS KPI cards:
- Potential Savings: The total estimated monthly savings across all recommendations, deduplicated so resources with multiple options are counted once using their highest-savings recommendation.
- Active Recommendations: The number of unique resources with at least one recommendation.
- Accounts Affected (or Resources Affected): The number of accounts or resources with optimization opportunities.
- Avg Savings / Rec: The average monthly savings per resource.
Azure KPI cards: Potential Savings, Recommendations, Subscriptions Affected, and counts of High Impact, Medium Impact, and Low Impact recommendations.
Recommendations table
The recommendations table lists every available opportunity.
AWS columns: Category (color-coded), Account, Resource (select to view the resource detail page), Current and Recommended configuration, Savings/mo, Savings %, Effort (Low, Medium, or High), and Source (the originating AWS service). Filter by Category, Effort, Source, Account, Region, and Action Type.
Azure columns: Subscription, Resource Group, Resource, Type, Category, Impact, SKU (the current-to-recommended SKU change), Term, Lookback, and Savings/mo. Filter by Impact, Term, Subscription, and Category.
Account view
On AWS, select an account to view all recommendations scoped to that account. The layout matches the main dashboard (KPI cards, category breakdown, and a recommendations table) filtered to a single account. (Azure does not have a per-subscription drill-down view.)
Resource detail
Select a resource to view every recommendation for that resource.
AWS detail shows:
- Resource Information: The resource ARN, current type, current configuration, account, region, source, and when the data was last refreshed.
- Recommendation Cards: One card per optimization option, each showing: the recommended configuration, estimated monthly savings, estimated cost after optimization, savings percentage, implementation effort, whether a restart is required, and whether the change is rollback-safe. If there are multiple options, the highest-savings recommendation is highlighted.
Azure detail shows the resource’s subscription, resource group, type, and location; the recommendation’s category, impact, term, lookback period, potential savings, and SKU change; a Problem & Solution panel explaining the issue and the fix; and a collapsible Raw Advisor Properties section with the full Advisor record. An Open in Azure portal link jumps to the resource.