Technical Problem
As users and queries grow on production AI systems, hardware (GPU) and API token costs get out of hand.
ServicesIV — Security, Data Privacy and LLMOps / Production Management
TokenandInfrastructure
Compute, Token and Infrastructure Cost Optimisation
Architectural solution
We put semantic caching in place so that similar questions are answered from cache in milliseconds without running the model at all. With dynamic model routing we send simple queries to small, inexpensive models and complex analytical ones to large models. Prompt compression and token-trimming algorithms bring data transfer costs down.
Operational outcome
An optimisation effort in which cost is broken down by endpoint, user and question type, the most expensive items are addressed first, and every change passes through quality measurement. How much can be saved is set by the current usage pattern; naming a figure before measuring would be nothing but a guess.
Starting Conditions
This service is needed when the AI bill is rising and nobody knows which usage the rise comes from. Only one thing has to be on your side: access — the billing lines and the traffic logs. Without those two it is guesswork rather than optimisation, and work that cannot say what became cheaper cannot demonstrate a saving either.
How We Work
We break the cost down first: which endpoint, which user, which type of question costs what. Even when the invoice arrives as a single line, that distribution can be recovered from the traffic log. Then we start with the three most expensive items — optimising the long tail spends effort without returning money. Semantic caching and model routing are switched on together with quality measurement: a system that gets cheaper while getting worse is not a saving.
Out of Scope
We do not commit to a saving percentage in advance; how much can be gained depends on the current usage pattern, and that pattern cannot be known before it is measured. Hardware purchasing decisions and vendor negotiation are out of scope. We also do not trade quality for cost: every change passes through the evaluation set, and a change that falls below the baseline is not shipped.