Inference Cost

Inference Cost is the operational expense of running an AI system in production — typically charged per token for hosted LLMs or measured in compute hours for self-hosted models. Hosted-model pricing varies widely: GPT-4o costs roughly $2.50 per million input tokens and $10 per million output tokens; Claude 4.5 Sonnet sits at $3 input and $15 output; Gemini 2.5 Pro charges $1.25-$2.50 input and $10-$15 output; while open-weight Llama models can be run on dedicated infrastructure for predictable hourly costs. Inference cost dominates the total cost of AI ownership for most enterprise deployments because training is amortized while inference scales with usage. Cost-optimization techniques include prompt compression, response caching, model routing (using cheaper models when possible), and quantization for self-hosted deployments. AI governance, AI compliance, and AI risk management programs increasingly tie cost monitoring to budgets — making cost visibility foundational to responsible AI operations across every enterprise AI portfolio.

Centralpoint Meters Every Token, Across Every Model: Oxcyon's Centralpoint AI Governance Platform tracks inference cost per chatbot, per skill, and per team — across OpenAI, Gemini, Llama, and embedded models. Centralpoint keeps prompts and skills on-prem and embeds cost-tracked chatbots into your portals via one line of JavaScript. AI spend becomes visible, then controllable.


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