Why Your AI Projects Cost More Than You Think...
- Tomasz Wosinski
- Jan 13
- 7 min read
And What Every Technology Lead and Finance Officer Needs to Know.
Most company projects using artificial intelligence are built on a simple money mistake. They start with tiny tests that look cheap because you only pay a few cents at a time. But when you try to give those tools to everyone at the company, the bill can suddenly jump by hundreds of times, turning a fun experiment into a serious problem. Industry reports show that many teams underestimate how quickly AI costs grow once real‑world usage kicks in and tools move from demo to daily work.
Here I want to explain why that happens, and how to build AI systems that stay affordable even when they succeed. At Maiven, the focus is on setting things up so that your costs do not explode every time an employee uses the software.
The Success That Breaks the Bank
Picture a common scene in a big company. A tech lead shows off a new AI helper that lets a hundred people save a few hours of work every week. It works well, and everyone in the room is smiling.
Then the finance lead asks a simple question: “What does it cost to let all 50,000 employees use this?”
The room goes quiet. The manager looks at the bill for the small test, maybe around eight hundred dollars. Then they do the math and realize that if everyone uses it, the monthly bill could turn into a four‑hundred‑thousand‑dollar invoice from a software vendor. Ouch.
This is the trap. Many outside consultants do not talk about it, because they get paid for the small test today and leave you to deal with the giant bill later. But if you cannot afford the tool when it is successful, that project was a failure from day one.
Why Small Tests Hide the Real AI Projects Cost
Most companies start by plugging into a big AI service over the internet. It is quick to set up and feels low‑risk, but it is also the most expensive way to run things once usage grows.
In a small test, you are often using a huge, very smart model to do simple jobs.
It is like hiring a world‑famous chef just to make toast. You are paying for a level of intelligence you do not need for most everyday office tasks.
The Real Cost of Scaling Up
As you move from a small demo to a real production system, the cost per question can jump by orders of magnitude. One analysis of enterprise AI rollouts shows that the biggest cost drivers are not just the model itself, but everything around it: repeated API calls, large token usage, extra tools like search, and the engineering needed to glue it all together.
In a small test, you usually feed in a handful of clean documents and ask simple questions. In real work, the AI has to read thousands of messy files, dig through old systems, and run through several steps to give a decent answer. Every one of those steps has a price tag and contributes to the AI Projects Cost.
The Math of the Problem
Small Test: One person asks a simple question. The cost might be three cents.
Full Production: One person asks a complex question. The AI has to search, think in multiple steps, and check its own answer. That single question can now cost a couple of dollars.
Full Scale: If 10,000 people each ask ten questions a day, that turns into hundreds of thousands of dollars every single day in usage fees.
The exact numbers will depend on the model and pricing, but the shape of the curve is the same: more people plus more complex questions equals much higher bills.
Three Ways You Lose Money When You Scale
Once you move past the fun test phase, there are three big ways AI projects start burning cash.
Problem 1: You Do Not Own the Engine
If you rely fully on someone else’s AI service, you are renting your tools. If that provider shuts down your favorite model or version, you end up scrambling and paying again to rebuild everything around a new one. If they raise prices overnight, your budget can be wrecked with no warning. You are stuck riding their decisions, not your own.
Consider using open-source LLM services deployed directly on your infrastructure to scale and control price better.
Problem 2: Every “Thought” Costs Money
The AI world is moving toward “agents” that can do multi‑step work for you. These agents break a job into pieces, plan what to do, call tools, and check their own work. It sounds great, but there is a catch: every one of those steps burns extra tokens and extra compute.
In plain terms, every “thought” the AI has is another meter tick. Real‑world cost studies show that most of the surprise bill comes not from the first question, but from all the extra calls and chatter around it as the system reasons, retries, and routes work.
If your setup is not careful about this, you pay a hidden “thinking tax” every time someone uses the tool.
When deploying agentic systems make sure that project team sets-up proper “logic paths” for agent and agent groupings for make sure that agents can get to expected conclusion with as few steps as possible.
Problem 3: Caching Does Not Save You
A common suggestion is “just cache the answers,” meaning you save old responses and reuse them instead of paying to generate them again. That sounds great on paper, but large companies run into two big problems:
Information changes all the time.
Not everyone is allowed to see the same things.
Enterprise AI systems with strict permissions and fast‑changing data cannot blindly reuse old answers, because what was correct and allowed yesterday might be wrong or forbidden today. Real‑world designs for permission‑aware search and AI often treat caching as a small helper, not a magic fix, because access rules and documents keep shifting under the hood.
In practice, this means caching only helps a part of your traffic, not the majority, and it cannot fix a system that is fundamentally too expensive per question.
How to Build Systems That Stay Affordable
At Maiven, the goal is to build systems that you actually own and control.
The idea is simple: you should own the “brains” of your software instead of renting them forever from someone else.
The main strategies are:
Strategy A: Use Smaller, Custom Models
Most office work does not need the biggest, fanciest model on the market. A large share of tasks - logging information, extracting fields, drafting simple replies, summarizing meetings - can be handled by smaller models tuned for your company’s data.
By running these smaller models on machines you control, you pay for the hardware once and reuse it as much as you like. That turns a metered, per‑question bill into a more stable, predictable cost.
Strategy B: Only Use Big Models When Needed
The system can be set up like a smart gatekeeper:
First, send the question to a cheap, smaller model.
Only if that model is not confident, or the task is truly hard, pass it to a larger, more expensive model.
Cost breakdowns of enterprise AI show that routing more requests to cheaper models and trimming token usage are the fastest ways to cut the bill without losing quality. Done right, this kind of “small‑first, big‑only‑when‑needed” setup can cut your usage costs dramatically while still giving people good answers.
Strategy C: Fixed Prices
On the service side, a flat‑fee approach keeps incentives clean. Instead of billing by the hour or by the number of meetings, the focus stays on finishing the job quickly and keeping the system simple enough that your team can run it without hand‑holding. There is no reward for making things more complicated than they need to be.
Questions to Ask Before You Spend More on AI
Before pouring more money into AI tools, it helps to ask your team or your vendor a few very direct questions:
The Actual Cost
Q: “If every single employee uses this tool ten times a day, what will our bill be next month?”
A: If no one can answer that clearly, you are flying blind.
The Exit Plan
Q: “If our AI provider doubles prices tomorrow, can we move this whole system to our own servers within two days?”
A: If the honest answer is no, you are locked in.
Ownership
Q:“Do we own the software and the ‘trained brains’ behind it, or are we borrowing everything from someone else’s platform?”
A: If you do not own the key parts, your risk grows every time you use them.
When This Approach Is Not for You
This way of building is not right for everyone.
If you are a very small company just trying out an idea, you probably should not hire a team like Maiven. Just use a basic web AI tool and move fast.
If you only have a one‑off job, like summarizing one batch of documents, you do not need a full system.
The focus here is on larger organizations that plan to use AI as a core part of their work for years. These are the teams that cannot afford to pay a “success tax” to a third‑party provider forever and need to bring costs under control.
Build for the Long Term
The real goal is to build a system that gets cheaper and faster as more people use it, not one that looks amazing in a demo and then scares everyone once the first real bill arrives.
You want tools your staff feel free to use, not tools you secretly hope they avoid because you are afraid of the meter running. That is what it means to build on a solid foundation: own the engine, keep the thinking efficient, and design for the day the whole company depends on it — not just for the day you show it off.
Are you ready to stop renting your intelligence?
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