6 posts
Inference is now 55-80% of AI cost. Model routing, caching, quantization, and the metric that matters: cost-per-successful-output. An LLM FinOps framework and the Turkey FX context.
Token prices dropped 80% but bills grow. How to control cost with routing, caching, batching, and prompt discipline — plus a CFO-ready FinOps framework.
The three layers of cutting the LLM bill: model, system and application. Prompt caching, model routing, batching and Turkey-specific FX and KVKK risks.
In LLMs the real cost is inference. I explain how I cut cost across the model, system and application layers with caching, routing, quantization and batching.
How do you plan an enterprise AI budget? A pillar cost guide breaking down model/token, cloud/GPU, data, integration, talent, governance and maintenance.
What is LLMOps? The discipline of running large language models in production: MLOps difference, prompt management, evaluation, LLM observability, cost optimization, guardrails, and a maturity model.