If you run a business and want AI to take work off your team’s plate, the first big decision is usually not which model or tool to use. It is who builds it, and how you pay them. Most owners end up choosing between three options: hire an agency for a fixed project, rent extra developers by the month, or bring in an engineer who joins your team and is paid to hit a business result.
This guide explains each option in plain terms, what it tends to cost you in money, time and risk, and when each one is the sensible buy. AppUnik sells the third option, so keep that in mind. We have tried to be straight about when the other two are a better fit. If you want the engineering detail behind this, our technical team wrote a longer comparison for engineers.
Why this choice matters more for AI than for a normal software project
With a normal website or app, you can usually write down what you want before work starts. AI projects are harder to pin down. Until someone tries the system on your real documents, emails or transactions, nobody knows which cases it will get wrong. That means the plan often changes in the first few weeks.
The way you buy the work decides what happens when the plan changes. Under some arrangements a change is a quick conversation. Under others it is a new quote, a delay and an argument about scope. That is where most of the hidden cost sits.
Option 1: a project agency
You describe what you want, the agency quotes a price and a date, builds it, and hands it over. For a business owner this is the easiest option to budget for. You deal with one supplier, and your own staff stay focused on their normal jobs.
It works well when the job is clear before it starts. A fixed integration between two systems, an internal tool with known screens, or a short trial to answer one yes or no question are good examples.
The risk shows up later. When the project ends, the people who understand why the system behaves the way it does move on to their next client. AI systems need tuning as your data and customers change. If accuracy slips six months later, you are either paying the agency again or asking your own team to fix something they didn’t build. And if real usage in month one shows the plan was wrong, every change becomes a change request with its own price.
Option 2: extra developers by the month (staff augmentation)
Here you rent developers who work under your direction. Staff augmentation is the industry name for it. You decide what gets built, your own technical lead manages them, and you can scale the team up or down.
This is a good deal if you already have someone in house who knows how to build AI systems and you are simply short of people. You keep control, and what the team learns stays with your company because your own people are directing the work every day.
The catch is that you are buying hours, not results. If nobody on your side has shipped an AI system before, the extra developers will build exactly what they are told, and the instructions may be wrong. You can spend months of budget and end up with something that works in a demo but not on a busy Monday. Progress reports will say the team was busy. They won’t tell you whether the business problem got smaller.
Option 3: an embedded engineer paid to hit a result
The third option goes by the name forward deployed engineer. In plain words, it is an outside engineer who sits inside your team, joins your meetings and chats, works on your systems, and is measured on a business number you agree at the start. That number might be how long it takes to process a claim, how many good customers get wrongly flagged as fraud, or how long staff spend reviewing a contract. Behind that engineer is their own company’s wider team.
Because they sit with your staff, they hear about problems the same day and can ask the person doing the work why a case is unusual. Because they are judged on the result, they will push back on work that won’t move it, and they care about what happens after launch.
Here is what that looked like in three AppUnik projects, using figures from our published case studies:
- A healthcare provider buried in claims paperwork cut claims processing time by 40% within 90 days, with people still reviewing the cases that need a human.
- A digital lender whose fraud checks kept blocking real customers saw 52% fewer false fraud flags after the model was rebuilt on its own data.
- A mid-size law firm made first-pass contract review three times faster with a tool built inside the document system its associates already used.
In each case the target was a business number agreed before any code was written, and that is the habit worth looking for whoever you hire.
This option has real costs too. You have to give the engineer access to your data, systems and people. Someone senior on your side needs to make decisions quickly, often weekly. If the work is small, well defined and doesn’t need daily contact with your team, you are paying for closeness you won’t use, and an agency will usually be cheaper.
A quick way to decide
Ask yourself three questions.
First, can you describe the finished result in writing today, with confidence it won’t change? If yes, an agency is likely the cheapest path.
Second, do you already have someone in house who has built and run an AI system in production? If yes, and you just need more people, rent developers and let your lead direct them.
Third, is the goal a business number, such as faster processing, fewer errors or less manual review, and you aren’t sure yet exactly how to get there? Then an embedded engineer tied to that number is usually the safer bet, because the plan can change without a new contract every time.
Many companies mix these. A common pattern is an embedded engineer for the first few months to prove the approach and set the measures, then a smaller rented team, or your own staff, to keep it running.
What to ask any provider before you sign
Whichever option you choose, a few questions will tell you a lot. What business number will this move, and how will we measure it before and after? What happens if the first approach doesn’t work on our real data? Who on your side will still understand this system a year from now? What will my team need to provide each week? If a provider can’t answer the first question clearly, that tells you how the project is likely to be judged at the end.
Frequently asked questions
Is an embedded engineer more expensive than an agency?
Often per month, yes. It can still cost less overall when the scope is uncertain, because changes don’t trigger new quotes and the system is less likely to need rebuilding later.
Can we start small?
Yes. A short first phase aimed at one measurable result is a sensible way to test any provider before committing to a longer engagement.
Talk it through with us
If you are weighing these options for an AI project, we are happy to tell you honestly which one fits, even when it isn’t ours. You can read more about how our forward deployed engineers and custom AI development work, or book a call and tell us the business number you want to move.
