Comprehensive monitoring, distributed tracing, and real-time analytics for your LLM applications. Track requests, measure performance, analyze costs, and ensure reliability across your entire AI infrastructure with enterprise-grade observability tools designed specifically for large language model deployments.
Three-layer architecture designed for comprehensive monitoring and minimal performance impact on your LLM applications
Everything you need to monitor, debug, and optimize your LLM applications in production
End-to-end visibility across your entire LLM request pipeline. Track requests from initial API call through multiple model interactions, understand latency breakdowns, and identify bottlenecks with detailed span analysis and trace visualization tools.
Real-time cost tracking and allocation across projects, teams, and individual applications. Monitor token usage patterns, predict monthly costs, set budget alerts, and generate detailed financial reports for accurate chargeback and optimization.
Comprehensive performance monitoring with latency percentiles, throughput analysis, and error rate tracking. Identify slow queries, optimize response times, and ensure your LLM applications meet SLA requirements with detailed performance baselines.
Configurable alerts based on custom thresholds for latency, error rates, cost anomalies, and token consumption. Receive notifications via Slack, PagerDuty, email, or webhooks with intelligent deduplication and escalation policies.
Build personalized dashboards with drag-and-drop widgets displaying real-time metrics, trends, and comparisons. Create team-specific views, executive summaries, and operational monitors tailored to your specific observability needs.
Fine-grained data privacy controls including PII masking, sensitive data redaction, and configurable retention policies. Ensure compliance with GDPR, HIPAA, and SOC 2 while maintaining full observability capabilities for your AI systems.
Capture and replay LLM requests for debugging, testing, and performance comparison. Reproduce issues in development environments, A/B test prompts, and validate changes with actual production traffic without affecting live systems.
Compare performance and cost metrics across different LLM providers and models. Make data-driven decisions about model selection, understand quality-cost tradeoffs, and optimize your model routing strategies with comprehensive benchmarks.
RESTful APIs and SDKs for all major programming languages. Integrate observability data into your existing tools, build custom visualizations, automate workflows, and export metrics to data lakes or business intelligence platforms.
Get started in minutes with simple integration code
# Install the LLM Observability Proxy SDK
pip install llm-observe-proxy
# Initialize the observability proxy
from llm_observe import ObservabilityProxy
proxy = ObservabilityProxy(
api_key="your-api-key",
service_name="my-llm-app",
environment="production",
sample_rate=1.0 # Capture 100% of requests
)
# Wrap your LLM client
import openai
client = proxy.wrap_client(openai.Client())
# All requests are now automatically monitored
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello!"}]
)
# View metrics in dashboard at https://observe.llmproxy.io
Monitor your LLM applications with comprehensive dashboards
See how LLM Observability Proxy compares to traditional monitoring solutions
| Feature | LLM Observability Proxy | Traditional APM | Basic Logging |
|---|---|---|---|
| Token-Level Tracking | ✓ Full Support | ✗ Not Available | ✗ Manual Only |
| Cost Attribution | ✓ Automatic | ✗ Not Available | ✗ Manual Only |
| Prompt/Response Capture | ✓ Configurable | ✗ Not Available | ✓ Basic |
| LLM-Specific Metrics | ✓ Comprehensive | ✗ Limited | ✗ None |
| Multi-Model Support | ✓ All Providers | ✓ Limited | ✓ Manual |
| Distributed Tracing | ✓ LLM-Native | ✓ Available | ✗ Not Available |
| Privacy Controls | ✓ Advanced PII | ✓ Basic | ✗ None |
| Setup Complexity | ✓ 5 Minutes | ✗ Hours/Days | ✓ Quick |
Choose the plan that fits your observability needs
Common questions about LLM observability and our proxy solution