LLM system monitoring tracks the operational health of your AI infrastructure: API availability, error rates by error type (rate limit, timeout, content filter), provider incidents, multi-provider failover, and latency SLOs. Unlike traditional APIs, LLMs have variable latency (token streaming), provider-specific failure modes, and cost implications that require specialised monitoring. Combine with LangSmith or Phoenix for trace-level visibility.
Essential metrics for LLM production monitoring.
For streaming responses, total latency doesn't matter — users are reading as tokens arrive. TTFT (first token latency) is what determines perceived responsiveness. Monitor TTFT separately from total completion time.
OpenAI, Azure, and Anthropic all have incidents. Single-provider dependence means your feature is down when they're down. LiteLLM proxy or Azure API Management provides provider-agnostic routing with automatic failover in 1 line of config change.
LLM costs are invisible until the bill arrives. Log input + output tokens per request, per feature, per team. Set up daily budget alerts. A rogue feature using GPT-4o on every keystroke can cost $5k/day.
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