Controlling the cost of AI systems end to end — the token economy, model selection and routing, prompt and context optimization, caching, batching, RAG/fine-tuning/self-hosting trade-offs, and cost observability and AI FinOps.
An LLM demo costs almost nothing, so teams ship without a cost model — and then production traffic turns a rounding error into the biggest line item on the bill.
An LLM demo costs almost nothing, so teams ship without a cost model — then production traffic turns a rounding error into the biggest line item on the bill. Why AI costs explode, and how to see it coming.
You cannot optimize what you cannot price, and pricing an AI system starts with understanding the token — what counts as one, why input and output cost differently, and how to compute the true cost of a request.
You cannot optimize what you cannot price. Pricing an AI system starts with the token — what counts as one, why input and output cost differently, and how to compute the true cost of a request.
The single biggest cost lever in most AI systems is not clever prompting — it is not using an expensive model for work a cheap one would do just as well.
The single biggest cost lever in most AI systems is not clever prompting — it is not using an expensive model for work a cheap one would do just as well. Right-sizing and routing models to tasks.
Since you pay for every input token, the fastest way to cut the cost of a call without changing the model is to send fewer tokens — and most production prompts are carrying far more than they need.
Since you pay for every input token, the fastest way to cut the cost of a call without changing the model is to send fewer tokens — and most production prompts carry far more than they need.
A large share of what an AI system processes is repeated — the same system prompt, the same documents, the same questions — and caching is how you stop paying full price for work you have already done.
A large share of what an AI system processes is repeated — the same system prompt, the same documents, the same questions. Caching is how you stop paying full price for work you have already done.
Not all AI work needs an answer this second, and for the work that can wait, batching and asynchronous processing buy meaningfully cheaper tokens in exchange for latency you were not using anyway.
Not all AI work needs an answer this second, and for the work that can wait, batching and asynchronous processing buy meaningfully cheaper tokens in exchange for latency you were not using anyway.
Some of the biggest cost decisions are architectural — whether to feed knowledge through retrieval or bake it into a model, whether to prompt a big model or fine-tune a small one, and whether to rent tokens or run your own — and each trade turns on your volume and your task.
Some of the biggest cost decisions are architectural — retrieval versus long context, prompting a big model versus fine-tuning a small one, and renting tokens versus self-hosting — each turning on your volume.
You cannot manage what you cannot see, and the difference between a team that controls its AI spend and one that is surprised by it is almost always whether they measure cost per feature, per user, and per call.
You cannot manage what you cannot see. The difference between a team that controls its AI spend and one surprised by it is whether they measure cost per feature, per user, and per call.
This series is part of a larger body of work by Pratik Dhanave, an Agentic AI Architect writing about production AI systems, distributed systems, and cloud-native engineering. Explore all course series, browse every post, or find topics via the tag index.