# LLMOps Panel Discussion on Cost and Performance Optimization ## Overview This case study covers a panel discussion featuring experts from various companies sharing their experiences and strategies for optimizing LLM deployments in production. The panel included representatives from: - Neva (private search solution) - Intercom (customer service platform) - Prompt Layer (LLM management platform) - OctoML (model optimization platform) ## Cost Optimization Strategies ### Model Selection and Deployment Approaches - Initial development with foundation model APIs - Moving to in-house deployment ### Hardware Optimization - Key threshold: Running on single A100 or A10 GPU - CPU deployment for certain components - Hardware selection based on actual requirements ### Technical Optimization Techniques - Structured printing and knowledge distillation - Smart API usage - Open source alternatives ## Latency Optimization ### Technical Approaches - Optimization libraries - Model compression techniques - Batch size optimization ### User Experience Considerations - Managing user expectations - API-specific considerations ## Monitoring and Evaluation ### Evaluation Approaches - Three main categories: - Critic modeling ### Performance Monitoring - Tracking tail latencies - Cost tracking - Quality assurance ## Key Learnings and Best Practices - Start with API services for rapid prototyping - Consider in-house deployment at scale - Focus on hardware-appropriate optimization - Implement comprehensive monitoring - Balance cost, latency, and quality requirements - Use appropriate evaluation techniques - Consider user experience in optimization decisions - Regular review and optimization of problematic cases