Welcome to Cloudtuner documentation!

CloudTuner.ai supports all major cloud platforms and provides unified visibility across your entire multi-cloud environment. Our AI-powered platform analyzes your infrastructure 24/7 to deliver actionable insights and automated optimizations.

Supported Platform

  1. Amazon Web Services (AWS) – Complete cost optimization and security monitoring

  2. Microsoft Azure – Subscription and tenant-level management

  3. Google Cloud Platform (GCP) – Project and organization-wide optimization

  4. Alibaba Cloud – Cost management for APAC deployments

  5. Kubernetes – Container cost optimization across any provider

Dashboard Overview

Your CloudTuner.ai home dashboard provides a centralized view of your multi-cloud environment with real-time insights into costs, security, and performance.

Key Widgets

📊 Cost Overview

  • Current month spend vs. budget
  • Month-over-month cost trends
  • Top 5 most expensive services
  • Projected monthly spend

🛡️ Security Score

  • Overall security posture rating (0-100)
  • Critical vulnerabilities count
  • Compliance status across frameworks
  • Recent security events

⚡ Performance Metrics

  • Resource utilization rates
  • Performance optimization opportunities
  • Service availability metrics
  • Response time trends

🎯 Optimization Opportunities

  • Potential monthly savings
  • Active recommendations count
  • Quick-win optimizations
  • ROI calculator

Customization Options

  • Widget Arrangement: Drag and drop to reorganize
  • Time Ranges: Toggle between daily, weekly, monthly views
  • Filter Options: Filter by cloud provider, region, or tags
  • Export Capabilities: Download reports in PDF, CSV, or Excel

Executive View

Switch to executive dashboard for high-level KPIs:

  • Total cloud spend trends
  • Security compliance ratings
  • Cost optimization ROI
  • Team performance metrics

AI-Powered Optimization

CloudTuner.ai continuously analyzes your cloud infrastructure to identify cost savings, security improvements, and performance optimizations.

Recommendation Categories

💰 Cost Optimization

  • Right-sizing: Reduce over-provisioned instances
  • Reserved Capacity: Purchase commitments for predictable workloads
  • Spot/Preemptible: Use discounted compute for fault-tolerant workloads
  • Storage Optimization: Move data to appropriate storage tiers
  • Orphaned Resources: Remove unused resources

🛡️ Security Enhancements

  • Access Control: Tighten IAM permissions
  • Encryption: Enable encryption at rest and in transit
  • Network Security: Configure security groups and firewalls
  • Compliance: Ensure adherence to security frameworks
  • Vulnerability Patching: Update outdated software and configurations

⚡ Performance Improvements

  • Auto-scaling: Implement dynamic scaling policies
  • Load Balancing: Optimize traffic distribution
  • Caching: Add caching layers for better performance
  • Database Optimization: Tune database configurations
  • CDN Usage: Implement content delivery networks

Recommendation Details

Each recommendation includes:

  • Impact Assessment: Potential savings or improvement
  • Implementation Effort: Time and complexity estimate
  • Risk Level: Low, medium, or high risk assessment
  • Dependencies: Prerequisites or related changes needed
  • Implementation Guide: Step-by-step instructions

Automated Implementation

  • One-click Apply: Implement low-risk optimizations automatically
  • Scheduled Changes: Apply optimizations during maintenance windows
  • Rollback Options: Easily revert changes if needed
  • Change Tracking: Monitor all applied recommendations

Supported Platforms

Amazon Web Services (AWS)

Connection Types:

  • AWS Root Account (Organizations)
  • AWS Linked Account
  • AWS GovCloud

Requirements:

  • IAM user with billing and resource read permissions
  • Cost and Usage Reports (CUR) or Data Exports enabled
  • S3 bucket access for billing data

Setup Time: 5-10 minutes Data Sync: 1-24 hours for initial import

Microsoft Azure

Connection Types:

  • Azure Subscription
  • Azure Tenant (Multi-subscription)
  • Azure Government

Requirements:

  • Azure AD app registration
  • Reader role assignment
  • Subscription or tenant access

Setup Time: 5-10 minutes Data Sync: 2-6 hours for initial import

Google Cloud Platform (GCP)

Connection Types:

  • Single Project
  • Organization (Multi-project)
  • Google Cloud VMware Engine

Requirements:

  • Service account with BigQuery access
  • Billing export to BigQuery enabled
  • Cloud Resource Manager API enabled

Setup Time: 10-15 minutes Data Sync: 4-12 hours for initial import

Alibaba Cloud

Connection Types:

  • Single Account
  • Resource Management (Multi-account)

Requirements:

  • RAM user with ReadOnlyAccess
  • Programmatic access enabled
  • Resource monitoring permissions

Setup Time: 5-10 minutes Data Sync: 2-8 hours for initial import

Kubernetes

Connection Types:

  • Managed Kubernetes (EKS, AKS, GKE)
  • Self-managed Kubernetes
  • OpenShift

Requirements:

  • Cluster admin access
  • Helm 3.x installed
  • Network connectivity to CloudTuner.ai

Setup Time: 5-15 minutes Data Sync: 1-2 hours for initial metrics

Connection Security

  • Read-Only Access: CloudTuner.ai never modifies your cloud resources directly
  • Encrypted Communication: All data transfer uses TLS 1.3 encryption
  • No Data Storage: Your data remains in your cloud environment
  • Audit Logging: All API calls are logged for security and compliance

Overview

Connect your Alibaba Cloud account for cost optimization across APAC regions and international deployments.

Prerequisites

  • Alibaba Cloud account with administrative access
  • RAM user with programmatic access enabled

Setup Process

Step 1: Create RAM User

  1. Go to RAM → Users → Create User
  2. Enable Programmatic Access
  3. Download or copy Access Key ID and Secret

Step 2: Assign Permissions

  1. Select your RAM user
  2. Click Add Permissions
  3. Assign ReadOnlyAccess system policy

Step 3: Connect to CloudTuner.ai Enter your Alibaba Cloud credentials:

  • Access Key ID
  • Access Key Secret
  • Account Name (for identification)

 

Amazon Web Services (AWS) Integration

Overview

CloudTuner.ai provides comprehensive AWS cost optimization through automated billing data analysis, resource discovery, and intelligent recommendations. We support both AWS Organizations (root accounts) and individual AWS accounts.

Prerequisites

  • AWS account with administrative access
  • AWS Cost and Usage Reports (CUR) or Data Exports enabled
  • S3 bucket for billing data storage

Connection Options

Option 1: AWS Root Account (Recommended for Organizations)

Best for companies using AWS Organizations with multiple linked accounts.

Step 1: Configure Data Export

  1. Navigate to AWS Billing & Cost Management → Data Exports
  2. Click Create export
  3. Select Standard data export type
  4. Choose CUR 2.0 format
  5. Enable Include resource IDs
  6. Set time granularity (Daily recommended)
  7. Configure S3 bucket and path prefix

Step 2: Create IAM Policy Create a new IAM policy with these permissions:

 
json
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "CloudTunerBillingAccess",
      "Effect": "Allow",
      "Action": [
        "cur:DescribeReportDefinitions",
        "s3:GetObject",
        "s3:ListBucket",
        "s3:GetBucketLocation"
      ],
      "Resource": [
        "arn:aws:s3:::your-billing-bucket/*",
        "arn:aws:s3:::your-billing-bucket"
      ]
    },
    {
      "Sid": "CloudTunerResourceDiscovery",
      "Effect": "Allow",
      "Action": [
        "ec2:Describe*",
        "s3:GetBucketPublicAccessBlock",
        "s3:GetBucketPolicyStatus",
        "s3:GetBucketTagging",
        "s3:GetBucketAcl",
        "s3:ListAllMyBuckets",
        "iam:GetAccessKeyLastUsed",
        "iam:ListUsers",
        "iam:GetLoginProfile",
        "iam:ListAccessKeys",
        "cloudwatch:GetMetricStatistics"
      ],
      "Resource": "*"
    }
  ]
}

Step 3: Create IAM User

  1. Go to IAM → Users → Create user
  2. Attach the policy created above
  3. Generate access keys for programmatic access
  4. Save the Access Key ID and Secret Access Key securely

Step 4: Connect to CloudTuner.ai

  1. Log into your CloudTuner.ai dashboard
  2. Navigate to Data Sources → Add New
  3. Select AWS Root Account
  4. Enter your credentials and billing export details:
    • AWS Access Key ID
    • AWS Secret Access Key
    • Export Name (from Step 1)
    • S3 Bucket Name
    • Export Path Prefix

Option 2: AWS Linked Account

For individual AWS accounts that are part of an AWS Organization.

  1. Follow Steps 2-3 from the Root Account setup
  2. In CloudTuner.ai, select AWS Linked Account
  3. Enter only Access Key ID and Secret Access Key
  4. Billing data will be imported through the root account

Data Synchronization

  • Initial import: 24-48 hours after connection
  • Ongoing updates: Hourly synchronization
  • Historical data: Up to 13 months (depending on AWS retention)

Troubleshooting

  • Connection Failed: Verify IAM permissions and access keys
  • No Billing Data: Ensure Data Export is properly configured and S3 bucket is accessible
  • Missing Resources: Check resource discovery permissions in IAM policy

Overview

CloudTuner.ai supports both Azure subscriptions and Azure tenant-wide monitoring for comprehensive cost management across your Microsoft cloud environment.

Prerequisites

  • Azure subscription with Owner or Contributor access
  • Azure Active Directory permissions for app registration

Azure Subscription Setup

Step 1: Register CloudTuner.ai Application

  1. Navigate to Azure Active Directory → App registrations
  2. Click New registration
  3. Name: “CloudTuner-AI”
  4. Select Single tenant account type
  5. Click Register
  6. Copy the Application (client) ID

Step 2: Create Client Secret

  1. In your CloudTuner-AI app registration
  2. Go to Certificates & secrets
  3. Click New client secret
  4. Set expiration (24 months recommended)
  5. Copy the secret value immediately

Step 3: Assign Permissions

  1. Navigate to Subscriptions → Select your subscription
  2. Click Access control (IAM)
  3. Click Add role assignment
  4. Role: Reader
  5. Assign access to: User, group, or service principal
  6. Select your CloudTuner-AI application

Step 4: Connect to CloudTuner.ai Enter these details in your CloudTuner.ai dashboard:

  • Subscription ID: Found in Azure Subscription overview
  • Application (client) ID: From Step 1
  • Directory (tenant) ID: Found in app registration overview
  • Client secret: From Step 2

Azure Tenant Setup

For multi-subscription organizations, follow the same steps but assign the Reader role across all relevant subscriptions.

Overview

CloudTuner.ai connects to GCP through service accounts and BigQuery billing exports for comprehensive cost analysis and optimization.

Prerequisites

  • GCP project with Billing Admin access
  • BigQuery API enabled
  • Cloud Resource Manager API enabled

Setup Process

Step 1: Enable Billing Export

  1. Follow the official GCP guide
  2. Create a BigQuery dataset for billing data
  3. Note the dataset and table names for later use

Step 2: Create Custom IAM Role Option A – Using Cloud Shell:

 
bash
gcloud iam roles create cloudtuner_role \
  --project=YOUR_PROJECT_ID \
  --permissions=bigquery.jobs.create,bigquery.tables.getData,compute.instances.list,compute.disks.list,compute.snapshots.list,compute.images.list,compute.addresses.list,compute.firewalls.list,compute.networks.list,compute.zones.list,compute.regions.list,compute.machineTypes.list,storage.buckets.list,storage.buckets.get,iam.serviceAccounts.list,monitoring.timeSeries.list

Option B – Using Console:

  1. Go to IAM & Admin → Roles → Create Role
  2. Add all permissions listed in the gcloud command above

Step 3: Create Service Account

  1. Navigate to IAM & Admin → Service Accounts
  2. Click Create Service Account
  3. Name: “cloudtuner-service-account”
  4. Assign the custom role created above
  5. Generate and download JSON key file

Step 4: Connect to CloudTuner.ai

  1. Upload the service account JSON file
  2. Enter BigQuery billing dataset details
  3. Verify connection and wait for initial data sync

Resource Pools Management

Organize and manage your cloud resources using pools for better cost control, governance, and allocation.

Pool Types

Business Unit Pools

  • Separate costs by department or division
  • Set budgets and spending limits
  • Track resource usage by team
  • Implement chargeback/showback models

Project Pools

  • Allocate resources by project or application
  • Monitor project-specific costs
  • Set project budgets and alerts
  • Track ROI per project

Environment Pools

  • Separate development, staging, and production
  • Apply different governance policies
  • Implement environment-specific optimizations
  • Control resource lifecycle by environment

Geographic Pools

  • Organize resources by region or data center
  • Optimize for local compliance requirements
  • Monitor regional performance metrics
  • Implement geo-specific cost controls

Pool Configuration

  • Budget Limits: Set hard and soft spending limits
  • Alert Thresholds: Configure notifications at various spend levels
  • Governance Policies: Apply automated rules and restrictions
  • Access Controls: Define who can access and modify pool resources
  • Cost Allocation Rules: Automatic resource assignment based on tags

Pool Analytics

  • Spend Tracking: Real-time and historical cost analysis
  • Utilization Metrics: Resource efficiency across pools
  • Comparison Reports: Pool-to-pool performance comparisons
  • Forecast Modeling: Predicted future costs and resource needs

Environment Sharing Platform

CloudTuner.ai’s shared environments feature enables efficient resource utilization by allowing multiple users, teams, or applications to share cloud infrastructure.

Benefits of Shared Environments

  • Cost Efficiency: Reduce costs through resource sharing
  • Faster Development: Quick access to pre-configured environments
  • Consistency: Standardized configurations across teams
  • Resource Optimization: Better utilization of cloud resources

Environment Types

Development Environments

  • Shared development clusters
  • Common databases and services
  • Collaborative testing environments
  • Sandbox environments for experimentation

Testing Environments

  • Shared QA environments
  • Performance testing infrastructure
  • Security testing sandboxes
  • Integration testing platforms

Training Environments

  • Learning and training platforms
  • Demo environments for sales
  • Workshop and tutorial environments
  • Certification testing platforms

Booking and Scheduling

  • Reservation System: Book environments for specific time periods
  • Calendar Integration: Sync with team calendars
  • Automatic Cleanup: Environment reset after usage
  • Usage Tracking: Monitor who used what and when

Access Management

  • Role-based Access: Control who can access which environments
  • Time-limited Access: Temporary access grants
  • Audit Logging: Track all environment usage
  • Security Isolation: Ensure tenant separation and data protection

Machine Learning Operations

Manage your ML workflows, models, and datasets with integrated cost optimization and performance monitoring.

MLOps Capabilities

Model Management

  • Model versioning and lifecycle management
  • Performance monitoring and drift detection
  • A/B testing and gradual rollouts
  • Model registry and metadata tracking

Training Optimization

  • Compute resource optimization for training jobs
  • Hyperparameter tuning with cost awareness
  • Distributed training cost optimization
  • Training data pipeline efficiency

Inference Optimization

  • Model serving cost optimization
  • Auto-scaling for inference workloads
  • Latency and throughput optimization
  • Edge deployment cost management

Data Management

  • Training data storage optimization
  • Feature store cost management
  • Data pipeline monitoring
  • Data lineage and governance

Cost Optimization for ML

  • Training Cost Reduction: Use spot instances and right-sized compute
  • Storage Optimization: Efficient data storage and retrieval
  • Model Serving Efficiency: Optimize inference costs and performance
  • Resource Scheduling: Schedule training jobs during off-peak hours

ML Workflow Integration

  • CI/CD Integration: Integrate with ML pipelines
  • Experiment Tracking: Monitor costs across experiments
  • Model Monitoring: Track model performance and costs in production
  • Collaboration Tools: Share models and experiments across teams
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