Build AI In-House vs Hire a Consultancy: The Real Cost Comparison

The build vs buy decision for AI is more nuanced than most comparisons suggest. Here is what the full cost of each path actually looks like.

I have an obvious interest in this conversation — I run an AI consulting firm. So let me give you the most honest version of this analysis I can, because the decision is not as simple as it gets presented on either side.

The right answer depends on your situation. Here is how to figure out what your situation is.

The real cost of building in-house

Most estimates focus on salary. That is the wrong number to start with. The full cost of an in-house AI team includes salaries, yes, but also recruiting costs (typically 15-25% of first year salary per hire), ramp time (3-6 months before a new ML engineer is productive on your stack), tooling and infrastructure, and — the one teams consistently miss — retention.

AI engineers have extremely high market demand. Annual turnover in the 25-30% range is common. Every departure means another recruiting cycle, another ramp period, and institutional knowledge walking out the door. Teams do not model this when they build business cases, but it is real.

• →Senior ML Engineer: $200,000-$350,000 total comp, 18-24 months to full productivity on enterprise systems
• →Recruiting cost per hire: $40,000-$80,000
• →Annual turnover cost at 30% rate on a 4-person team: roughly $160,000-$240,000 per year in recruiting and ramp
• →Infrastructure and tooling: $50,000-$150,000 per year for enterprise-scale deployments

The real cost of hiring a consultancy

A focused consulting engagement for a well-defined enterprise AI project typically runs $150,000-$400,000. You get a team with existing expertise, tooling already built, and patterns already learned from other projects. The system is delivered and running in 6-12 weeks, not 6-12 months.

The ongoing cost matters too. A well-built system with good documentation requires modest maintenance — roughly $30,000-$75,000 per year for monitoring, updates, and optimization. That is much lower than keeping a full-time team.

When in-house is the right answer

Building in-house makes sense when AI is core to your competitive moat and you need to maintain proprietary capability over the long term. If your product is the AI system — if the model's performance is what customers are paying for — you need to own it.

It also makes sense when your scale justifies a dedicated team. If you have 50+ distinct AI use cases and the volume to operate them at scale, a full-time team is more cost-effective than a series of consulting engagements.

When consulting is the right answer

Consulting makes sense when speed matters, when the use case requires specialized expertise you do not have, or when you need to validate a business case before committing to a headcount investment. A consulting firm delivers a working production system in weeks. Hiring a team and ramping them up takes months.

The honest comparison

For a single high-value AI use case: consulting wins on cost, speed, and risk in the first two years. For a portfolio of 20+ use cases that you will maintain and grow over five years: building a team starts to win on total cost of ownership.

Most enterprises are in the first category. They have 2-5 high-priority AI use cases, not 20. The math points to consulting for the first projects, with a plan to grow internal capability once you have proven the value and understand the technical requirements.