AI can get expensive surprisingly quickly.
The first version of an AI application is usually built to prove that it works.
Then usage grows.
More customers.
More employees.
More conversations.
More documents.
Suddenly the question changes from:
“Can AI do this?”
to:
“Why does it cost this much to do it?”
We help answer that question.
We look at the whole picture.
AI cost isn't just the model.
It can include the amount of information sent to the model, how often requests are repeated, how long agents run, how much information they retrieve, and how much computing capacity stays running.
We measure those pieces and find where the money is going.
Not every task needs the most powerful—and most expensive—model. We work across Google Gemini, the Anthropic API, and the OpenAI API and match the model to the task.
Large amounts of unnecessary context increase cost and often slow responses.
Caching can eliminate repeated model calls and repeated computation.
Limits on steps, tool calls, tokens, and execution time prevent runaway processes.
Some workloads make sense on managed AI services. Others may benefit from self-hosted models or a hybrid approach.
We trace representative production traffic, measure tokens, latency, model selection, retrieval, runtime usage, and cost, then test changes against an evaluation set.
That means we're not simply saying "Use a smaller model."
We're asking "Can we use a smaller model and still get the same result?"
If the answer is yes, that's an optimization worth making.
Lower operating cost, faster responses, and a clearer understanding of what your AI is actually costing the business.