> ## Documentation Index > Fetch the complete documentation index at: https://www.getmaxim.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Tracing Overview > Monitor AI applications in real-time with Maxim's enterprise-grade LLM observability platform. Build and monitor reliable AI applications for consistent results with comprehensive distributed tracing, real-time monitoring, and alerting capabilities. export const MaximPlayer = ({url}) => { return ; }; ## Improve your AI application outcomes Build and monitor reliable AI applications for consistent results: * Monitor AI model performance in production * Detect and resolve issues proactively * Improve response quality through data-driven insights * Reduce costs with optimized resource usage ## Understanding LLM observability challenges Why does traditional monitoring fail with LLM applications? * **Cannot track prompt and completion correlations** - Traditional tools lack the ability to correlate input prompts with their corresponding outputs * **Cannot monitor critical LLM metrics** - Missing essential metrics like token usage, model parameters, and response quality * **Struggles with mixed data types** - Ineffective at processing both structured and unstructured data simultaneously * **Cannot trace reasoning processes** - Unable to debug black-box LLM failures or understand internal reasoning * **Fail to track complex workflows** - Cannot handle RAG systems, tool calling, and multi-step reasoning chains * **Limited human feedback support** - Provide minimal capabilities for collecting and analyzing human feedback and preference models * **Lack subjective metric tracking** - Missing support for user ratings, A/B testing, and quality assessments ## Maxim's solution Maxim platform architecture overview Maxim platform leverages three core architectural principles: ### 1. Comprehensive distributed tracing Track the complete request lifecycle, including LLM requests and responses. Debug precisely with end-to-end application flow visibility. ### 2. Zero-state SDK architecture Maintain robust observability across functions, classes, and microservices without state management complexity. ### 3. Open source compatibility Maxim logging is inspired by (and highly compatible with) open telemetry: * Generate idempotent commit logs for every function call * Support high concurrency and network instability * Maintain accurate trace timelines regardless of log arrival order * Production-proven reliability with over one billion indexed logs ## Key Features ### Real-time monitoring and alerting Track GenAI metrics through distributed tracing and receive instant alerts via: * Slack * PagerDuty * OpsGenie Monitor critical thresholds for: * Cost per trace * Token usage * User feedback patterns ### Saved views Find common search patterns instantly: * Store common search patterns * Create debugging shortcuts * Speed up issue resolution ### Online evaluation Monitor application performance with: * Custom filters and rules * Automated reports * Threshold-based alerts ### Data curation Transform logs into valuable datasets: * Create datasets with one click * Filter incoming logs * Build targeted training data * Update datasets for prompt improvements [Schedule a demo](https://getmaxim.ai/demo) to see how Maxim AI helps teams ship reliable agents.