> ## Documentation Index
> Fetch the complete documentation index at: https://docs.deductive.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Cost Optimization

> Discover cost savings and performance improvements across your infrastructure and services with Deductive AI.

# Cost Optimization

Deductive AI helps you discover cost savings and performance improvements across your infrastructure and services, surfacing opportunities that would otherwise require dedicated engineering time to find.

## The Challenge

Identifying cost optimization opportunities is difficult:

* **Data Silos** - Cost data spread across multiple systems
* **Complex Relationships** - Hard to correlate costs with usage patterns
* **Time Constraints** - Requires deep analysis that teams don't have time for
* **Hidden Inefficiencies** - Problems only visible when correlating multiple data sources

## How Deductive Helps

Deductive AI continuously analyzes your infrastructure to identify:

1. **Underutilized Resources** - Services running but not actively used
2. **Oversized Instances** - Resources provisioned beyond actual needs
3. **Inefficient Patterns** - Code or infrastructure patterns causing waste
4. **Optimization Opportunities** - Specific actions to reduce costs

## Example: Infrastructure Optimization

### The Discovery

Deductive analyzes your AWS infrastructure and identifies:

> **Cost Optimization Opportunity**: The `analytics-service` is running 10 instances 24/7, but metrics show it only needs 3 instances during peak hours and 1 instance during off-peak.
>
> **Current Cost**: \$2,400/month (10 instances × \$240/month)
> **Optimized Cost**: \$720/month (3 peak + 1 off-peak with auto-scaling)
> **Potential Savings**: \$1,680/month (70% reduction)
>
> **Evidence**:
>
> * CPU utilization averages 15% across all instances
> * Traffic patterns show clear peak/off-peak cycles
> * Auto-scaling is configured but not enabled
> * Similar services successfully use auto-scaling
>
> **Recommended Action**: Enable auto-scaling with min=1, max=5 instances based on CPU utilization.

### The Implementation

Deductive can help you implement the optimization:

1. **Review Current Configuration** - Shows current instance setup
2. **Generate Auto-Scaling Config** - Creates CloudFormation/Terraform changes
3. **Test in Staging** - Validates the configuration
4. **Monitor Impact** - Tracks cost reduction and performance

## Key Capabilities

### Multi-Cloud Analysis

Deductive analyzes costs across:

* **AWS** - EC2, RDS, S3, Lambda, and more
* **GCP** - Compute Engine, Cloud SQL, Storage
* **Azure** - Virtual Machines, SQL Database, Blob Storage
* **Kubernetes** - Cluster resource usage and pod efficiency

### Pattern Recognition

Deductive identifies common inefficiencies:

* **Zombie Resources** - Instances running but unused
* **Over-Provisioning** - Resources sized for peak but rarely used
* **Inefficient Queries** - Database queries causing high costs
* **Redundant Services** - Multiple services doing the same thing

### Cost Attribution

Deductive helps you understand:

* **Service-Level Costs** - What each service actually costs
* **Cost Drivers** - What's driving your cloud spend
* **Trend Analysis** - How costs are changing over time
* **Anomaly Detection** - Unexpected cost spikes

## Example Scenarios

### Database Optimization

**Problem**: High RDS costs from inefficient queries

**Deductive Analysis**:

* Identifies slow queries causing high CPU usage
* Correlates query patterns with code changes
* Finds N+1 query problems in recent PRs
* Recommends query optimization or caching

**Result**: 40% reduction in database costs

### Storage Optimization

**Problem**: S3 costs growing unexpectedly

**Deductive Analysis**:

* Identifies old log files not being cleaned up
* Finds duplicate data across buckets
* Discovers inefficient data formats
* Recommends lifecycle policies

**Result**: 60% reduction in storage costs

### Compute Optimization

**Problem**: High EC2 costs from over-provisioning

**Deductive Analysis**:

* Analyzes actual CPU/memory usage vs. provisioned
* Identifies services that can use smaller instance types
* Finds opportunities for spot instances
* Recommends right-sizing strategy

**Result**: 50% reduction in compute costs

## Best Practices

### Connect Cost Data Sources

Enable comprehensive cost analysis:

* **Cloud Provider APIs** - AWS Cost Explorer, GCP Billing, Azure Cost Management
* **Infrastructure Monitoring** - Prometheus, Datadog for resource usage
* **Application Metrics** - Custom metrics for business logic costs
* **Code Repositories** - Track changes that affect costs

### Regular Reviews

Set up periodic cost analysis:

* **Weekly Reviews** - Quick checks for obvious issues
* **Monthly Deep Dives** - Comprehensive analysis
* **Quarterly Planning** - Strategic cost optimization

### Actionable Recommendations

Deductive provides specific, actionable recommendations:

* **Exact Changes** - Specific configuration changes needed
* **Impact Estimates** - Expected cost savings
* **Risk Assessment** - Potential impact on performance
* **Implementation Steps** - Step-by-step guide to implement

## Results

Teams using Deductive AI for cost optimization report:

* **20-40% Cost Reduction** - Average savings across infrastructure
* **Faster Discovery** - Find opportunities in minutes vs. weeks
* **Better Visibility** - Understand what's driving costs
* **Proactive Management** - Catch cost issues before they grow

## Next Steps

* [Connect AWS Integration](/integrations/aws) - Enable cost analysis
* [Connect Prometheus](/integrations/prometheus) - Track resource usage
* [Get Started](/users/welcome) - Start optimizing costs today