Building AI-Powered Cloud Cost Optimization Advisors with ASP.NET Core

Introduction

Cloud computing has transformed how organizations build, deploy, and scale applications. Services from major cloud providers offer unprecedented flexibility, allowing engineering teams to provision infrastructure in minutes instead of weeks.

However, this convenience often comes with a challenge: uncontrolled cloud spending.

As organizations scale, cloud environments become increasingly complex. Virtual machines, Kubernetes clusters, databases, storage accounts, AI services, serverless functions, networking resources, and monitoring tools can generate substantial costs if not managed carefully.

Engineering and FinOps teams frequently ask:

Which resources are driving cloud costs?
Are virtual machines oversized?
Can workloads be moved to lower-cost services?
Which resources are underutilized?
Where can savings be achieved without affecting performance?
How will future architectural decisions impact cloud spending?

Traditional cloud cost dashboards provide visibility into spending but rarely explain why costs are increasing or how to optimize them effectively.

Artificial Intelligence can analyze cloud usage patterns, operational telemetry, resource utilization, workload characteristics, and historical spending data to provide intelligent cost optimization recommendations.

In this article, we'll build an AI-powered cloud cost optimization advisor using ASP.NET Core, Azure Cost Management APIs, Azure Monitor, OpenTelemetry, and Azure OpenAI.

Understanding Cloud Cost Challenges

Cloud costs often grow faster than expected due to:

Overprovisioned resources
Idle infrastructure
Inefficient architectures
Excessive data transfer
Unused storage
Poor autoscaling configurations
Redundant services

Consider the following example:

Virtual Machine:
Standard_D8s_v5

Average CPU Usage:
12%

Average Memory Usage:
24%

This workload is likely overprovisioned and may be wasting money.

Why Traditional Cost Monitoring Falls Short

Most cloud platforms provide:

Cost reports
Billing dashboards
Budget alerts
Resource utilization metrics

While useful, these tools often leave teams asking:

Which optimization should be prioritized?
What savings can be achieved?
What operational risks exist?
Which workloads can be rightsized safely?

AI can answer these questions using contextual analysis.

How AI Improves Cloud Cost Optimization

AI can evaluate:

Resource utilization
Historical spending
Workload patterns
Service dependencies
Business criticality
Scaling behavior

Example recommendation:

Resource:
Payment API VM Cluster

Current Cost:
$1,200/month

Recommendation:
Reduce instance size

Estimated Savings:
$420/month

Confidence:
93%

This provides actionable guidance instead of raw metrics.

Solution Architecture

An AI-powered cost optimization platform consists of four layers.

Cost Collection Layer

Collect data from:

Azure Cost Management
AWS Cost Explorer
Google Cloud Billing
Kubernetes Metrics

Telemetry Layer

Gather:

CPU metrics
Memory metrics
Network usage
Storage utilization

AI Analysis Layer

Azure OpenAI evaluates optimization opportunities.

Recommendation Layer

Generate savings recommendations and forecasts.

Creating the ASP.NET Core Project

Create a new Web API project.

dotnet new webapi -n CloudCostAdvisor

Install required packages.

dotnet add package Azure.ResourceManager
dotnet add package Azure.AI.OpenAI
dotnet add package Azure.Monitor.Query

These packages provide access to cloud resource and monitoring data.

Designing the Cost Analysis Model

Create a model representing cloud resources.

public class CloudResource
{
    public string ResourceName { get; set; }

    public string ResourceType { get; set; }

    public double MonthlyCost { get; set; }

    public double CpuUsage { get; set; }

    public double MemoryUsage { get; set; }
}

This model becomes the foundation for optimization analysis.

Collecting Resource Utilization Data

Resource metrics are essential for identifying waste.

Example:

public class ResourceMetrics
{
    public double CpuAverage { get; set; }

    public double MemoryAverage { get; set; }

    public double NetworkUsage { get; set; }
}

These metrics help determine whether resources are appropriately sized.

Integrating Azure Monitor

Azure Monitor provides detailed operational telemetry.

Example query:

var client =
    new MetricsQueryClient(
        credential);

var response =
    await client.QueryResourceAsync(
        resourceId,
        new[] { "Percentage CPU" });

This data feeds the optimization engine.

Collecting Cost Data

Cost information can be retrieved from billing APIs.

Example model:

public class CostRecord
{
    public string ServiceName { get; set; }

    public double MonthlySpend { get; set; }

    public DateTime BillingPeriod { get; set; }
}

Historical spending patterns help identify trends.

Building the AI Cost Optimization Engine

Create an AI service.

public class CostOptimizationService
{
    private readonly OpenAIClient _client;

    public CostOptimizationService(
        OpenAIClient client)
    {
        _client = client;
    }

    public async Task<string> AnalyzeAsync(
        string costData)
    {
        var prompt = $"""
        Analyze cloud spending.

        Determine:

        1. Cost reduction opportunities
        2. Resource optimization
        3. Risk assessment
        4. Estimated savings

        {costData}
        """;

        var response =
            await _client.GetChatCompletionsAsync(
                "gpt-4o",
                new ChatCompletionsOptions
                {
                    Messages =
                    {
                        new ChatMessage(
                            ChatRole.User,
                            prompt)
                    }
                });

        return response.Value
            .Choices[0]
            .Message
            .Content;
    }
}

The AI engine transforms cloud metrics into optimization recommendations.

Example AI Analysis

Input:

VM Size:
D8s_v5

CPU Usage:
12%

Memory Usage:
22%

Monthly Cost:
$680

Generated output:

Recommendation:
Resize to D4s_v5

Estimated Savings:
$310/month

Risk:
Low

Confidence:
95%

This enables quick identification of savings opportunities.

Detecting Idle Resources

Idle resources are a common source of cloud waste.

Example:

Storage Account

Requests:
0

Activity:
None

Last Access:
45 Days Ago

AI recommendation:

Action:
Archive or delete resource.

Estimated Savings:
$120/month

This improves resource efficiency.

Kubernetes Cost Optimization

Containerized workloads often consume unnecessary resources.

Example metrics:

Requested CPU:
4 vCPU

Actual Usage:
0.8 vCPU

Requested Memory:
8 GB

Actual Usage:
2 GB

AI output:

Recommendation:
Reduce pod requests and limits.

Estimated Savings:
18%

This helps improve cluster efficiency.

Predicting Future Cloud Costs

AI can forecast spending trends.

Example:

Current Monthly Spend:
$18,000

Forecast:

Projected Spend
in 6 Months:
$26,000

Growth Rate:
44%

This helps organizations plan budgets proactively.

Evaluating Architectural Decisions

Cloud costs are often influenced by architecture.

Example:

Current Architecture:
Dedicated VM Cluster

AI recommendation:

Alternative:
Azure Container Apps

Estimated Savings:
28%

This supports strategic decision-making.

Detecting Autoscaling Issues

Improper scaling configurations frequently cause overspending.

Example:

Minimum Instances:
10

Peak Requirement:
4

AI recommendation:

Reduce minimum instances to 4.

Estimated Savings:
$540/month

This aligns infrastructure with actual demand.

Multi-Service Cost Correlation

AI can identify cost relationships across services.

Example:

Application Gateway
      ↓
AKS Cluster
      ↓
Azure SQL Database

Generated insight:

Primary Cost Driver:
Database tier selection.

This helps teams focus on high-impact optimizations.

Advanced Enterprise Features

Large organizations often expand cost optimization systems with additional capabilities.

FinOps Integration

Align engineering decisions with financial objectives.

Department Cost Attribution

Allocate costs to business units automatically.

Cost Anomaly Detection

Identify unexpected spending increases.

Example:

Cost Increase:
38%

Reason:
Unexpected storage growth.

Sustainability Analysis

Estimate carbon impact alongside financial costs.

Executive Reporting

Generate strategic cost optimization reports.

Best Practices

Monitor Continuously

Cloud environments change rapidly.

Combine Cost and Performance Metrics

Optimization should not sacrifice reliability.

Review AI Recommendations

Engineering validation remains important.

Implement Cost Governance

Define spending policies and ownership.

Track Savings Achieved

Measure the effectiveness of optimization efforts.

Benefits of AI-Powered Cloud Cost Optimization

Organizations implementing intelligent cost advisors often achieve:

Lower cloud spending
Improved resource efficiency
Better capacity planning
Enhanced FinOps practices
Reduced operational waste
Increased engineering visibility

Teams gain clear recommendations rather than manually analyzing complex billing reports.

Conclusion

Managing cloud costs has become a critical responsibility for modern engineering organizations. As cloud environments grow more complex, traditional dashboards and reports are often insufficient for identifying meaningful optimization opportunities.

By combining ASP.NET Core, Azure Cost Management, Azure Monitor, OpenTelemetry, and Azure OpenAI, organizations can build AI-powered cloud cost optimization advisors that continuously analyze resource usage, forecast spending, and recommend cost-saving actions. As FinOps and cloud governance continue to evolve, intelligent cost optimization platforms will become a key component of successful cloud-native operations.