AI SolutionsService
What AI actually costs to run
Cost for each unit of work at real volume, including the exceptions nobody prices.
Kaelo Global is a Dubai company licensed in Meydan Free Zone, with over 100 clients served so far.
At a glance
What it is
The model itself is usually the smallest line in the bill.
A principal, Kaelo Global AI solutions
AI business cases are often built on demonstration usage and then fail at production volume. The model itself is usually the smallest cost. What moves the number is the volume of retries, the length of the context you send with every request, the exceptions that still need a person, and the engineering time to keep everything running. None of those appear on a vendor's pricing page.
A realistic view of AI inference cost is built for each unit of work, such as each document, query or case resolved, instead of for each token. We model your expected volume including peaks, add retries and failures, price the exception queue from an evaluation instead of from optimism, and include the cost of maintaining the system.
We do not publish current model prices, because they change often enough that any page quoting them becomes misleading. The structure of the cost is stable, and it is what decides whether a case works.
What's included
Six areas of scopeCost for each unit of work, not for each token
Tokens are the billing unit. The meaningful figure is the cost of each document, query or resolved case, including the retries and failures that used tokens without producing an answer.
Context length as the hidden multiplier
Sending a large amount of context with every request is the most common reason a business case is wrong by a wide margin. The quality of your retrieval is a cost decision as much as an accuracy one.
The exception queue, priced
Whatever share of cases the system cannot handle confidently still needs a person. That is a real and continuing cost, and it belongs in the case from the start.
Building against buying, argued honestly
Sometimes an off-the-shelf product costs less than the engineering time to maintain something bespoke. We have no licence income riding on the answer.
Choosing the right model for each step
Using a smaller model where it is reliable and a larger one only where it earns its cost, which often reduces the bill more than negotiating on price.
Watching costs after launch
Simple monitoring of spend for each unit of work, so that a change in usage, prompts or provider pricing shows up quickly instead of at the end of the quarter.
How the work runs
Volume model
Realistic expected throughput, including peaks.
Unit cost build
Cost for each unit of work, with retries and context length included.
Exception rate estimate
Taken from an evaluation instead of from optimism.
Total cost of ownership
Including engineering maintenance and the risk of model changes.
When to come to us
- 01You are approving an AI business case built on a pilot's usage.
- 02A live system's costs have grown faster than its usage.
- 03You are choosing between building and buying.
- 04You want to compare providers on the true cost of the same work instead of on headline prices.
What we do not do
- Publishing model prices as though they were stable. They change, and a page quoting them ages badly.
- Estimating exception rates before an evaluation exists.
- Building a case that counts only the requests that succeed on the first attempt.
Common questions
