LLMOps

LLMOps applies operational discipline to large language models — adding capabilities like prompt versioning, evaluation suites, RAG pipeline observability, token-level cost tracking, and content-safety filtering on top of traditional MLOps. Tools include LangSmith (LangChain's observability platform), Helicone, Phoenix (Arize), Langfuse, and Weights & Biases Weave. LLMOps platforms answer questions traditional MLOps ignored: which prompts are deployed where? How does prompt v1.2 compare to v1.1 on the eval suite? Which conversations triggered safety violations? How much did we spend on GPT-4 last week vs Claude vs Gemini? Enterprise LLMOps platforms increasingly integrate with model gateways, vector databases, and content-safety layers. AI governance and AI compliance programs treat LLMOps observability as the primary evidence source for AI audit trails, AI risk management decisions, and responsible AI iteration across rapidly-evolving large-language-model deployments in production environments.

Centralpoint Is LLMOps for the Governed Enterprise: Oxcyon's Centralpoint AI Governance Platform handles prompt versioning, model routing, metering, and audit logging across OpenAI, Gemini, Llama, and embedded models. Prompts and skills stay on-prem. Embed LLMOps-powered chatbots into your portals via a single JavaScript line.


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