Comprehensive insights into your AI API usage. Track costs, monitor performance, analyze token consumption, and optimize your LLM spending with powerful dashboards and reports.
Everything you need to understand and optimize your LLM API usage
Monitor spending in real-time across all providers. Set budgets, track against projections, and identify cost-saving opportunities.
Detailed breakdown of prompt tokens, completion tokens, and total token consumption by model, endpoint, and user.
Track latency distributions, throughput, and response times. Identify bottlenecks and optimize your API performance.
Attribute API usage to specific users, teams, or applications. Understand who's consuming resources and optimize allocation.
Compare performance, cost, and quality metrics across different LLM models to make informed model selection decisions.
Historical trend analysis with forecasting. Predict future usage and costs based on historical patterns.
Different dimensions of insights available for your LLM operations
# Fetch usage analytics from your LLM proxy from llm_proxy.analytics import AnalyticsClient client = AnalyticsClient(api_key="your-api-key") # Get daily usage summary daily_stats = client.get_usage( start_date="2025-03-01", end_date="2025-03-17", granularity="daily" ) for day in daily_stats: print(f"{day.date}: {day.requests} requests, ${day.cost}") # Get cost breakdown by provider cost_breakdown = client.get_cost_breakdown( group_by="provider" ) # Returns: {"openai": 2802, "anthropic": 1546, "google": 484} # Get performance metrics performance = client.get_performance_metrics( percentiles=[50, 95, 99] )
Performance metrics for connection pools integrated into usage analytics.
IP-based access analytics showing blocked requests and security events.
Track PII detection events and masking statistics in your analytics.
Routing decision analytics showing how requests are distributed.
Get complete visibility into your LLM API operations with powerful analytics dashboards and insights.