A comprehensive, enterprise-grade solution for optimizing AI inference workloads across AWS, Azure, GCP, and Oracle Cloud. Built for enterprise customers who demand performance, cost efficiency, and multi-cloud flexibility.
- Multi-Cloud Performance Comparison - Compare performance across all major cloud providers
- Cross-Cloud Cost Optimization - Find the most cost-effective solution across providers
- Unified SLA Compliance Tracking - Monitor uptime guarantees across all clouds
- Historical Cost Analysis - Track spending trends across multiple cloud providers
- Cross-Cloud Scoring - Performance, cost, reliability, and scalability balanced across providers
- Provider-Specific Optimization - Leverage each cloud's unique strengths
- Risk Assessment - Automated risk level evaluation with cloud-specific mitigation strategies
- Alternative Recommendations - Multiple options across different cloud providers
- Cross-Cloud SLA Management - Track and manage service level agreements across providers
- Multi-Cloud Performance Guarantees - Ensure your workloads meet enterprise requirements
- Cost Transparency - Detailed breakdown of infrastructure costs across all clouds
- Multi-Region Support - Optimize across different regions within each cloud provider
- Cross-Cloud Cost Comparison - Visualize costs across AWS, Azure, GCP, and Oracle
- Performance Benchmarking - Compare performance metrics across all providers
- Cost Optimization Tools - Multi-cloud cost breakdown and optimization strategies
- Performance Monitoring - Resource utilization across different cloud providers
- Dashboard - Executive overview with multi-cloud metrics
- Multi-Cloud Optimizer - Advanced configuration engine across all providers
- Cost Comparison - Detailed cost analysis across cloud providers
- Performance Analysis - Cross-cloud performance benchmarking
- Cost Optimization - Multi-cloud cost optimization strategies
- Python 3.8+
- Multi-cloud accounts (AWS, Azure, GCP, Oracle Cloud)
- Streamlit
# Clone the repository
git clone https://github.com/sameermehta/gcp-ai-inference-optimizer.git
cd gcp-ai-inference-optimizer
# Install dependencies
pip install -r requirements.txt
# Run the multi-cloud dashboard
streamlit run app/multi_cloud_dashboard.py
# Or use the launcher
python run_multi_cloud.py- Navigate to Multi-Cloud Optimizer - Configure your workload requirements
- Select Preferred Cloud Providers - Choose from AWS, Azure, GCP, Oracle Cloud
- Set Enterprise Parameters - Choose region, SLA level, and budget constraints
- Get Cross-Cloud Recommendations - Receive optimized configurations across providers
- Compare Performance & Costs - Analyze options across all cloud providers
- Optimize Multi-Cloud Strategy - Implement cost optimization strategies
app/multi_cloud_optimizer.py- Advanced multi-cloud optimization engineapp/cloud_data.py- Comprehensive data for all cloud providersapp/multi_cloud_dashboard.py- Professional multi-cloud dashboard interface
- Cross-cloud scoring (Performance, Cost, Reliability, Scalability)
- Provider-specific SLA compliance checking
- Multi-cloud risk assessment with mitigation strategies
- Cross-cloud cost optimization with budget constraints
- Performance analysis across all cloud providers
- Market Leader - Industry-leading reliability and features
- Instance Types: t3, m5, c5, r5, g4dn, p3 families
- Strengths: Best reliability, extensive global infrastructure, comprehensive AI/ML services
- Pricing: Premium pricing with excellent value
- Enterprise Focus - Excellent enterprise integration
- Instance Types: Standard_B, Standard_D, Standard_F, Standard_E, Standard_NC families
- Strengths: Great integration, hybrid cloud capabilities, comprehensive compliance
- Pricing: Competitive pricing with enterprise benefits
- Cost Leader - Often most cost-competitive
- Instance Types: e2, c2, n1, a2 families
- Strengths: Best AI/ML capabilities, excellent data analytics, cost-effective
- Pricing: Most competitive pricing for AI workloads
- Emerging Player - Aggressive pricing strategy
- Instance Types: VM.Standard2, VM.Standard3, BM families
- Strengths: Aggressive pricing, strong database integration, improving AI capabilities
- Pricing: Most aggressive pricing for certain workloads
- Model Inference - Optimize for real-time prediction serving across clouds
- Batch Processing - Cost-effective batch inference pipelines
- Training Workloads - GPU-optimized training environments
- Multi-Cloud High Availability - Deploy across multiple cloud providers
- Cross-Cloud SLA Compliance - Enterprise-grade uptime guarantees
- Multi-Cloud Cost Management - Budget-constrained optimizations
- Cross-Cloud Latency Optimization - Sub-10ms inference times
- Multi-Cloud Throughput Maximization - High QPS configurations
- Resource Efficiency - Optimal CPU/memory/GPU ratios across providers
- Model Size - Memory requirements for your AI models
- Target QPS - Expected queries per second
- Latency Requirements - Maximum acceptable response time
- GPU Requirements - Whether GPU acceleration is needed
- Preferred Cloud Providers - Select from AWS, Azure, GCP, Oracle Cloud
- GCP Region - Geographic deployment location
- SLA Level - Service level agreement requirements
- Budget Constraints - Maximum hourly spending limits
- Performance Requirements - Minimum performance guarantees
- Cross-Cloud Cost Savings - Percentage reduction vs. single cloud
- Performance Improvement - Throughput and latency gains across providers
- Multi-Cloud SLA Compliance - Uptime and performance guarantees
- Resource Utilization - CPU, memory, GPU efficiency across clouds
- Cross-Cloud Metrics - Live performance monitoring across providers
- Historical Trends - Cost and performance analysis across clouds
- Multi-Cloud Alert Management - SLA violation notifications
- Capacity Planning - Future resource requirements across providers
- Cross-Cloud SLA Tracking - Monitor service level agreements
- Multi-Cloud Performance Guarantees - Ensure workload requirements are met
- Cost Transparency - Detailed cost breakdown across all cloud providers
- Risk Assessment - Automated risk evaluation and mitigation
- Multi-Cloud Deployment - Geographic and provider redundancy
- Cross-Cloud Auto-scaling - Dynamic resource allocation
- Multi-Cloud Load Balancing - Traffic distribution optimization
- Monitoring & Alerting - Proactive issue detection across clouds
- AWS: Up to 60% savings with 1-3 year commitments
- Azure: Up to 55% savings with reserved instances
- GCP: Up to 55% savings with committed use discounts
- Oracle: Up to 50% savings with universal credits
- AWS: Up to 90% savings but can be interrupted
- Azure: Up to 85% savings with low-priority VMs
- GCP: Up to 80% savings with preemptible instances
- Oracle: Limited spot instance availability
- AWS: Excellent auto-scaling capabilities
- Azure: Good auto-scaling with enterprise integration
- GCP: Very good auto-scaling with cost optimization
- Oracle: Limited auto-scaling capabilities
- All Clouds: 20-25% savings by matching instance size to workload needs
This is an enterprise-grade multi-cloud solution designed for production use. Contributions are welcome for:
- Multi-Cloud Performance Improvements - Optimization algorithms and benchmarks
- New Cloud Providers - Additional cloud provider support
- Enhanced Multi-Cloud Analytics - Advanced monitoring and reporting
- Security Features - Enterprise security enhancements
Multi-Cloud AI Inference Optimizer - Built for Enterprise Multi-Cloud Scale
- Real-time Multi-Cloud API Integration - Live pricing and availability
- Advanced ML Models - Machine learning-based multi-cloud optimization
- Enterprise SSO - Single sign-on integration across clouds
- Multi-Cloud API Access - RESTful API for programmatic access
- Custom Multi-Cloud Dashboards - Configurable enterprise dashboards
- Multi-Cloud Cost Forecasting - Predictive cost analysis
Built for Multi-Cloud Enterprise Scale 🚀 | Optimized for Performance ⚡ | Designed for Reliability 🛡️ | Multi-Cloud Ready ☁️