MCP provides an observability platform for production LLM applications, capturing high‑volume trace data in real time and visualizing it as hierarchical span trees for debugging and performance analysis. The service includes enterprise‑grade PII protection, fault‑tolerant ingestion with backpressure, and cost analytics that break down per‑request spending across AI providers, helping engineering teams monitor, optimize, and secure their AI workloads.
Funding
Funding not disclosed
Founders
Product
Problem
Developers of large language model (LLM) applications often lack visibility into request flows, performance bottlenecks, and data privacy risks, making it difficult to debug, optimize, and control costs in production environments.
Solution
MCP offers an observability platform tailored for LLM workloads that captures high‑volume trace data in real time and presents it as hierarchical span visualizations. The service automatically manages ingestion queues, applies backpressure, and employs fault‑tolerant delivery mechanisms such as exponential backoff, retry budgets, disk buffering, and circuit breakers to ensure reliable data collection. Built‑in PII protection scans traces against more than 21 predefined patterns and custom regexes, safeguarding sensitive information. Integrated cost analytics break down per‑request spending across multiple AI providers, generate budget alerts, and help teams optimize resource usage. Multi‑tenant architecture with JWT authentication and scope‑based authorization isolates data between customers, while a guided setup wizard and SDK auto‑instrumentation simplify deployment.
Target Audience
Primary customers are engineering teams building production‑grade LLM applications, including AI product developers, DevOps engineers, and security/compliance officers who need traceability, performance monitoring, and data privacy controls.
Features
- High‑volume batch ingestion of up to 1,000 traces per request with automatic queue management and HTTP 503 backpressure
- Hierarchical span trees that display tool calls, timing, and parent‑child relationships for detailed debugging
- Enterprise‑grade PII protection covering 21+ patterns (API keys, SSNs, credit cards, etc.) plus custom regex support
- Fault‑tolerant delivery using exponential backoff, retry budgets, disk buffering, and circuit breakers
- Real‑time streaming of trace data with sub‑2 second latency and 99.2 % success rate
- Cost analytics that track spending across GPT‑4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, provide per‑request cost calculations, and issue budget alerts
- Multi‑tenant isolation with JWT authentication and scope‑based authorization
- SDK auto‑instrumentation for the Model Context Protocol and a guided setup wizard for rapid onboarding