Observability for LLM apps — capture prompts, traces & model behavior from day one

Observability for LLM apps — capture prompts, traces & model behavior from day one targets a $9.0B = 300,000 companies x $30K ACV (global developer/engineering orgs that will buy app-level model observability) total addressable market with medium saturation and a year-over-year growth rate of 40%+ (observability + ML-monitoring adoption driven by LLM rollouts and compliance needs).

Key trends driving demand: LLM proliferation -- Rapid deployment of LLMs into production increases demand for runtime visibility into prompts, completions, and reasoning chains.; API-first models -- Centralized model APIs (OpenAI, Anthropic, Azure OpenAI) expose cost/latency metrics and request/response hooks enabling telemetry capture.; Shift from model metrics to behavior metrics -- Teams need semantic correctness, hallucination detection and policy enforcement, not just accuracy or loss curves.; Observability convergence -- DevOps/monitoring vendors expanding into ML, creating expectations for signal-driven alerting and traces across code and models..