LLM Observability
Tooling that traces, monitors, and evaluates LLM and AI-agent applications in production — capturing prompts, completions, cost, latency, and quality scores per request.
LLM observability tools (Langfuse, LangSmith, Arize Phoenix, Helicone, and others) are the operational backbone for any team shipping an LLM-powered product: they answer "why did this agent respond that way," "what is this costing us," and "did our last prompt change make quality better or worse." Crail groups this with AI gateways and agent orchestration tooling under the LLM & Agent Infrastructure category.
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