Cost and Resource Optimization: From EDA to Orchestrated Automation

AIOps Use Case

Pattern guide, not a partner integration.

Cloud provider examples (AWS, Azure, GCP) illustrate the pattern. Replace data sources and playbooks with your cost and utilization tooling.

Overview

Cloud providers already generate cost and sizing recommendations, but those reports are often verbose, inconsistent, and difficult to operationalize. Teams need a governed path from insight to action: collect utilization and cost signals, let AI correlate workload behavior with spend, and execute rightsizing or reclamation through Ansible.

Challenge: Recommendations sit in portals and spreadsheets while waste continues.

Solved with AI + automation: Automation collects utilization, performance, and cost data across environments. AI correlates consumption with workload behavior. Recommendations translate into governed automation workflows (rightsizing, scheduling, reclamation).

Business outcomes: Lower cloud spend, improved resource utilization, reduced operational risk, continuous optimization.

Buyer question:

How do we identify wasted capacity before it becomes an operational problem?

Background

Cost optimization fails when it stays advisory. FinOps and platform teams already know which instances are oversized; the gap is safe, repeatable execution with audit trails. Entry points include scheduled reviews, human-initiated optimization runs, and event-driven triggers when utilization or spend crosses thresholds.

Solution

Add capabilities progressively:

Event-Driven Ansible (EDA) and AAP for scheduled or triggered collection playbooks and approved remediation job templates
AI enrichment to prioritize recommendations and explain tradeoffs in operator language
Automation orchestrator (AO) when workflows need approval gates, dependency-aware sequencing (for example drain traffic before resize), or multi-cloud branching

Who Benefits

Persona	Challenge	What They Gain
FinOps / Cloud architect	Static reports nobody acts on	Actionable, governed optimization runs
Platform engineer	Fear of breaking production during resize	Approval and validation steps before change
IT operations leader	Unpredictable cloud bills	Measurable savings with audit history

Prerequisites

Ansible Automation Platform 2.5 or later
Event-Driven Ansible (optional, for threshold-driven runs)
Automation orchestrator (when Walk-stage orchestration is required)
Access to cloud or hybrid cost and utilization APIs (customer-specific collections)

EDA to AO adoption path

Coming soon:

Stage-by-stage scenarios (collect and report only, AI-prioritized recommendations, orchestrated rightsizing with approvals) will follow the same structure as Incident and Ticket Enrichment.

Stage 1: EDA + AAP only

Collect utilization and cost facts; run pre-approved rightsizing or reclamation job templates on a schedule or manual launch.

Stage 2: AI enrichment

AI summarizes waste and risk per recommendation before any job runs.

Stage 3+: Automation orchestrator

Switch and approval nodes for environment-specific routing, dependency checks, and human sign-off on production changes.

Validation

Symptom	Status
End-to-end scenario tests	Content in progress