Recommendations

The recommendation engine turns detected savings opportunities into prioritized, trackable work items. Each recommendation carries savings, effort, and risk metadata so you can decide what to implement first.

Recommendation attributes

Attribute	Description
Title	Clear, actionable summary of the optimization
Description	Detailed explanation of the issue and solution
Potential savings	Estimated monthly or annual savings
Effort level	Implementation complexity: Low, Medium, or High
Risk level	Potential impact on workloads: Low, Medium, or High
Source	Origin: CloudKeepers, Conversation, Assessment, or Manual

Recommendations start as drafts, become active when promoted, and are tracked through execution in Plan — the full lifecycle is documented on Keepers.

Findings vs recommendations

Keeper detections create findings first. A finding moves through these statuses:

Status	Meaning
New	Just detected; nobody has looked at it yet.
Acknowledged	A team member has seen the finding and owns the follow-up.
Active	Work on the finding is underway.
Resolved	The underlying issue is fixed and verified.
Dismissed	Reviewed and intentionally not acted on.

Promoting a finding creates a recommendation — a separate object with its own lifecycle, impact analysis, and implementation playbook.

Sources

Source	How recommendations are created
CloudKeepers	Detection rules continuously scan for utilization drift, cost anomalies, and best-practice violations
Conversation	Ask Alex with `#recommend` to analyze a specific area
Assessment	Well-Architected assessments generate recommendations under the cost optimization pillar
Manual	Open Recommendations, click New Recommendation, and set savings, effort, and risk yourself

Category	Typical checks
Right-sizing	EC2 instances under 20% average utilization, RDS instances with excess capacity, over-provisioned Lambda memory
Reserved capacity	1-year vs 3-year commitment analysis, Savings Plans coverage gaps, reserved instance utilization
Spot opportunities	Fault-tolerant batch jobs, development/test environments, stateless applications
Storage lifecycle	S3 Intelligent-Tiering enablement, Glacier archive candidates, infrequently accessed data
Unused storage	Unattached EBS volumes, orphaned snapshots, empty S3 buckets, unused EFS file systems
Database	DynamoDB capacity mode, ElastiCache node sizing, index and query optimization, read replica opportunities
Network	Cross-region transfer, NAT gateway efficiency, VPC endpoints, idle load balancers, cross-zone load balancing

Collaboration

Each recommendation includes a discussion thread:

Feature	Use it to
Comments	Add context, questions, or implementation notes
Mentions	Tag team members with `@username`
Attachments	Link related documents or tickets
Audit trail	Track all changes and status updates

Implementing recommendations

Agent-driven (with approval):

Review the recommendation details.
Ask Alex to implement it.
Review the proposed changes.
Approve execution in the Approval Center.
Monitor implementation progress.

Manual:

Follow the implementation steps in the recommendation.
Use the discussion thread for questions.
Record actual savings when done so Savings Tracking can compare them against projections.

Exports and integrations

Action	How
Export	Download recommendations as CSV or Excel
Ticketing	Create Jira tickets from a recommendation
Sync	Send recommendation updates to external systems via webhooks

Examples

Find and understand recommendations:

@alex #recommend show the top 10 cost recommendations by savings
@alex #recommend list low-effort EC2 recommendations with high savings
@alex #recommend explain why we should resize instance i-0abc123

Generate new recommendations for a specific area:

@alex #recommend analyze our S3 storage costs and suggest optimizations
@alex #recommend review EC2 instances idle for more than 7 days

Implement one — Alex shows the proposed change and requests approval before executing:

@alex #recommend implement the resize for instance i-0abc123

Savings Tracking

Measure realized savings against projections after implementation

Cost Analytics

Explore the spend trends and anomalies behind recommendations

Keepers

See how detections become findings and promoted recommendations

Code Review

CostOps

Deep Response Engine

Infrastructure

ChatOps

Skills

Artifacts & Dashboards

Recommendation attributes

Findings vs recommendations

Sources

Categories

Collaboration

Implementing recommendations

Exports and integrations

Examples

Savings Tracking

Cost Analytics

Keepers

​Recommendation attributes

​Findings vs recommendations

​Sources

​Categories

​Collaboration

​Implementing recommendations

​Exports and integrations

​Examples

​Related

Savings Tracking

Cost Analytics

Keepers

Recommendation attributes

Findings vs recommendations

Sources

Categories

Collaboration

Implementing recommendations

Exports and integrations

Examples

Related