The obvious way to compare building an in-house AI team against hiring a consulting firm is to line up a $180K-$220K engineer salary against a $500K-$800K consulting quote and call it a day. That comparison misses almost everything that actually determines year-one cost.
A detailed Costs comparison of hiring an inhouse consulting firm walks through what's usually left out. For in-house hiring: employer burden adds another 20-50% on top of salary, senior AI roles can take months to fill, and a "five-person team" typically delivers only 2-3 productive contributors in year one once you account for staggered start dates and onboarding. On top of headcount, there's cloud compute and inference, vector databases, observability tooling, security reviews, and governance, costs that rarely show up in a salary-only model.
Consulting isn't automatically cheaper once you look closely either. Smaller AI firms bill $150-$300/hour, with premium consultancies well above that for specialized work, and a lot of proposals quietly exclude cloud usage, data cleanup, legal/security/privacy review, and long-term operations, the stuff that shows up as a surprise later. The piece includes a side-by-side TCO table across labor, ramp/delay, tooling, and knowledge transfer, plus red-flag checklists for both in-house plans (no ramp curve, no ML-ops budget, no named product owner) and consulting proposals (vague deliverables, no acceptance criteria, ambiguous ownership of change orders).
Its actual recommendation isn't "always build" or "always buy," it's to model sensitivity across salary scenarios, ramp time, attrition, and cloud costs, and to request real artifacts (job descriptions and hiring sequence for in-house; a proper SOW with staffing plan and exclusions for consulting) before committing either way. A hybrid model, embedded consultants with an explicit knowledge-transfer plan, gets a specific mention as the middle path when you need both delivery speed and durable internal capability.
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