In-house AI Team vs Outsourcing: A Cost Comparison

Why the usual comparison is rigged

There are three models here, and the third is the one most of these comparisons end up recommending. A team of your own means salaries, hiring, and the years of ownership that follow. A partner means a contract, a scope, and someone else carrying the staffing risk. The hybrid keeps some work inside forever and moves the boundary as the product changes.

That boundary is the whole subject. Research on companies that took work back in-house describes five processes running at once — change management, the vendor relationship, building competence, organizational design, transferring ownership — and each of them costs money and time. So machine learning development in-house or outsourced is the wrong frame: the question is which parts belong where, and what moving them later will cost.

Most comparisons skip that question and go straight to a table, and those tables share a pattern: costs that belong on both sides are printed on only one.

One widely shared comparison puts an in-house team at $1.0–1.8 million for year one against $92,000–138,000 for a partner. Tooling, compute, and vector storage sit in the in-house column and appear nowhere in the other. Your partner pays for those too and bills them back inside a rate. Moving them to one side of the page is how a gap of unknown size turns into a claim of six times less.

Attrition gets the same treatment: counted as a risk of your own team, ignored when people rotate off your account at a vendor. You feel that disruption too, and no invoice arrives for it.

A fair comparison puts the same rows on both sides and asks who carries each one.

How much does it cost to build an in-house AI team

Across the US economy, salary is about two-thirds of what an employee costs. The rest is what nobody quotes.

Start with the market. US statistical agencies have no occupation code for “AI engineer,” so the closest published figure covers data scientists: a median wage of $120,230 a year, with the bottom tenth under $67,240 and the top tenth over $199,130. Those come from an employer census. In the United Kingdom, the median advertised salary for an AI engineer is £87,500, drawn from 452 salary quotes across 885 permanent postings, and outside London it is £80,000 and climbing 14.29% a year.

Then add what sits on top. Across the US economy, benefits, payroll taxes, insurance, and paid leave come to 31.6% of an employee’s total cost, or $15.61 of every $49.46 per hour. That ratio covers all civilian workers, and no equivalent figure for AI roles exists in public data. In the UK the components are explicit: employer National Insurance runs at 15% above a £5,000 threshold, plus at least 3% into a pension.

A salary line in a US budget covers about two-thirds of the person it pays for.

Now add time. The median vacancy takes 39 calendar days to fill, measured across more than 4,600 organizations and all roles. Filling any role is hard right now: 72% of employers worldwide report difficulty, 69% in the United States and 73% in the United Kingdom. What is new is the top of that shortage list. AI skills lead it for the first time, with model and application development at 20%.

Finally, add how long people stay. Median tenure across computer and mathematical occupations is 4.3 years, with no separate figure published for AI roles, and replacing someone costs between half and two times their annual salary by Gallup’s estimate. Gallup calls it an estimate, so treat it as a range.

One engineer is not a team

One AI engineer is not a team. It is a single point of failure with a salary. Building a model and running one in production are different disciplines, and Google’s guidance on production ML says so directly: the people who develop models often have no production engineering experience. Give one person data preparation, training, deployment, and monitoring, and most of it stops the moment they take a holiday.

What a partner actually charges for

A rate is a bundle, and we can describe ours because we sell one: salaries, tooling and compute, senior time on review, bench capacity between projects, and the margin a company lives on. Every one of those lines exists on the in-house side too. The difference is that you see one number and sign it.

Two-thirds of organizations surveyed by Deloitte in 2024 — 67%, against 45% two years earlier — buy outcomes instead of time, which is what makes hourly rate comparisons close to meaningless.

The last row is where buyers get caught. In the same Deloitte research, 70% of executives admit their vendor management function is not fully mature, and an immature buyer signs for the deliverable while forgetting to specify the handover. An honest estimate of outsource AI development cost also includes the work that stays with you: writing the brief, reviewing output, running acceptance, and owning the system afterwards.

The cost nobody prices: leaving

Moving the work back in-house takes years, and it does not reliably save money — even when saving money is the entire point.

The best-documented case is a 2025 study in Empirical Software Engineering: a large public organization spent about three years bringing more than 100 systems back from contractors. It rests on 61 interviews across the organization and six suppliers, plus corporate documents and five years of deployment data. Its IT department grew from 90 developers to 309 to absorb the work.

The stated goal was removing the consultant markup, which the organization itself put at roughly $100,000 per developer per year. Researchers found no clear evidence of significant cost savings. Ownership, motivation, delivery speed, and production quality all improved — real gains, and none of them the gain that justified the move.

Seven in ten executives in the Deloitte survey say they have selectively brought work back in-house over five years. The question is therefore not whether you will move the boundary, but what moving it will cost.

Read that as an argument for pricing the exit at the start rather than for avoiding partners altogether. The study names knowledge asymmetry as the central difficulty: your supplier understands the system in ways the documentation never captures. Hiring people turned out to be easier than keeping them, and the legacy technical debt needed its own budget. So the cost is set on day one, by how the contract was written: a supplier who agrees to name the handover artifacts at signing charges the same per hour as one who does not, and costs far less on the way out.

What has to come back, and who runs it after

Source code is the smallest part of the handover, and on its own it is close to useless. On our own projects it has six parts, and a contract that names all six saves an argument later:

• Model artifacts. Weights, adapters, fine-tuned embeddings, checkpoints, and the training script.
• Evaluation assets. Eval datasets, scoring rubrics, and judge prompts, without which you cannot tell whether your next change helped.
• Prompt registry. Every production prompt, versioned, with the reasoning.
• Data. Training and labeling sets in a native format, plus what the system collected in production.
• Infrastructure. Deployment manifests and infrastructure-as-code, so it rebuilds elsewhere.
• Runbook. What breaks, how it is detected, and who gets paged.

Ownership is a separate question from possession. In the United States, a work-made-for-hire clause does not cover commissioned software: the Copyright Office lists nine categories of commissioned work, and neither software nor models appear among them. Without an explicit assignment of rights, the contractor keeps the copyright. British law arrives at the same place by another route: the author is the first owner unless the contract says otherwise, and the employer exception covers employees only.

Managed platforms add a third case. Fine-tuning on Amazon Bedrock creates a private copy of the base model, and Amazon states that your content is not used to train the base models. The documentation says nothing about who owns the resulting weights or whether they can be exported, and you pay monthly to store the custom model. Ask before you build on it.

After the handover

An AI system can become measurably worse without anyone touching a line of code, because model quality decays on its own. In a controlled experiment across 32 datasets and four industries, researchers saw temporary degradation in 91% of the 128 model-dataset pairs — gradually in some, suddenly in others, sometimes after a long stretch of working well. That is a laboratory result, and the mechanism it describes is the one you inherit.

Around the model sits everything else. The same Google guidance lists ten components required for a production ML system, from data collection and verification to serving infrastructure and monitoring. The model is a small box in the middle of that diagram.

Four things trigger retraining: a schedule, new data, metric degradation, or a shift in the data distribution. Someone has to watch all four, and naming that person before launch costs one conversation.

A one-week test before you sign anything

You can settle this decision with five questions, all answerable in a week.

1. Post the job and watch. Advertise the role and count qualified applicants after seven days. The market answers faster than any salary survey.
2. Ask what arrives at the end. Send a vendor the six-part handover list and ask which parts they deliver. A partner who answers in detail is a different proposition from one who says “full IP transfer.”
3. Name the owner of retraining. Write down who watches the metrics after launch and what their budget is.
4. Price the exit before the entry. Ask what moving the system to another team would take, then put the answer in the contract.
5. Scope one process. Take the smallest useful piece of work and cost it both ways.

Run that week and the choice between machine learning development in-house or outsourced stops being a matter of opinion. You will have your own numbers, and they beat anyone else’s benchmarks.

Where we stand, and what to do next

We sell one of the two options on this page, and you should read everything above with that in mind.

That is why every figure here carries its source, why the exit has a price in this text, and why the next paragraph names the cases where hiring your own people beats hiring us.

If a comparison is written by someone selling one of the options, check whether they costed the scenario that loses them the work.

Hire your own people in three cases: AI is the product you sell, regulation rules a supplier out, or the same work runs every day and the knowledge has to compound inside the company.

Most work is not one of those three, and a partner fits it — including work with no end date. Continuous work can sit outside perfectly well, as long as the contract names who owns the model, who retrains it, and what arrives if you take it back. That is the clause worth negotiating. The rate is not.

If you would rather not do that alone, we run a free assessment: a 15–30 minute call where we look at your processes and your data, then say where automation pays and where the problem has a simpler answer.

For the wider question of where to start with AI at all, see our guide to implementing AI in business.