LLM Observability & Cost Governance Guides
Practical guides for monitoring, controlling, and governing LLM costs in production — built around a privacy-first, metadata-only approach. Whether you're trying to understand observability, cut spend, or meet compliance requirements, start with the cluster that matches your goal.
Observability & Monitoring
Start here to understand what LLM observability is, how it differs from traditional monitoring, and how to compare the tools on the market. These guides cover the foundations of seeing what your AI is actually doing in production.
Cost & Governance
LLM costs are unpredictable by nature — token counts shift with every prompt. These guides show how to measure spend, cut it through prompt tuning, and govern budgets autonomously before overruns happen.
Privacy & Compliance
For teams in healthcare, finance, and other regulated industries, observability cannot mean shipping prompts to a third party. These guides explain privacy-first, metadata-only governance and how it maps to compliance requirements like HIPAA.
Provider Settings & Prompt Tuning
Model parameters and prompt design directly drive cost and quality. These practical guides cover the settings that matter for OpenAI, Claude, and prompt efficiency.
See it on your own LLM calls
Install in three commands. Zero prompt storage, autonomous budget governance, no code changes.
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