#cost-optimization

Reduce BigQuery costs for a high-volume analytics workload. Strategies: 1. Partition tables by date and cluster by high-cardinality columns. 2. Use materialized views for frequently-run aggregations. 3. Implement query result caching and avoid SELECT *. 4. Set up cost controls with custom quotas per project. 5. Analyze query execution plans to identify expensive operations. Provide before/after cost comparison and ROI calculation. Include monitoring dashboard for ongoing cost tracking.

Conduct cloud infrastructure cost optimization. Analysis areas: 1. Right-sizing over-provisioned instances. 2. Reserved instances and savings plans. 3. Spot instances for non-critical workloads. 4. Unused resources (idle instances, unattached volumes). 5. Data transfer costs optimization. 6. Storage lifecycle policies. 7. Auto-scaling policies review. Use tools like AWS Cost Explorer, CloudHealth. Provide recommendations with estimated savings. Implement FinOps practices with cost allocation tags and budgets. Target 20-40% cost reduction.

Access multiple LLMs via OpenRouter. Benefits: 1. Single API for 50+ models. 2. Cost comparison across models. 3. Fallback to alternative models. 4. Real-time model availability. 5. Usage analytics dashboard. 6. OpenAI-compatible API. 7. Free models available. 8. Model routing based on performance. Switch models without code changes. Monitor costs and reliability.