Short answer: a small outsourced AI team of three to four people typically costs $15,000 to $35,000 per month depending on seniority and region. Hiring the same team in-house in the US costs $60,000 to $90,000 per month in salary alone, plus recruitment time of three to six months per role. Individual AI engineers through an outsourcing partner run roughly $40 to $120 per hour depending on location and specialism.
The table below gives indicative hourly rates through an outsourcing partner. Rates vary with seniority and specialism, and the spread within a region is usually wider than the gap between regions.
For comparison, US in-house salaries for these roles typically run $140,000 to $260,000 annually for AI and ML engineers and $120,000 to $200,000 for senior backend engineers, before benefits, equipment, and recruitment cost.
Team cost is determined more by how long you need people than by their hourly rate. The factors below are where the real variance sits.
Most AI projects need one AI engineer and two integration engineers rather than the reverse. Staffing to the perceived glamour of the work rather than its actual distribution inflates cost substantially.
Short engagements carry proportionally higher ramp-up cost. Continuity across phases reduces the cost of every subsequent piece of work because context is retained.
A senior lead with mid-level engineers usually outperforms an all-senior team on cost-effectiveness, since much of the work does not require the most expensive people available.
Where data needs substantial preparation, the data engineering allocation grows and the timeline extends. Assessing this early changes the staffing plan materially.
Overlap with your working day reduces cycle time on decisions. Teams with no overlap appear cheaper per hour and cost more in elapsed time to the same outcome.
The comparison below covers the models organizations actually choose between. Most end up hybrid, with a small in-house core and outsourced capacity around it.
Highest cost and longest lead time, with recruitment for AI roles typically taking three to six months. Justified when AI is core to your product and you need permanent institutional capability.
Fastest to start and lowest total cost, with the vendor carrying recruitment and retention risk. Suits organizations where AI supports the business rather than constituting the product.
An in-house lead or architect setting direction with outsourced delivery capacity underneath, which is the most common structure among organizations delivering AI seriously.
Engineers working under your management inside your process, which suits organizations with engineering management capacity as covered on our staff augmentation comparison page.
Cost control in AI projects comes from scoping and staffing balance rather than from cheaper people. The approaches below reliably reduce spend while improving outcomes.
A short fixed-scope engagement tells you whether the capability works before you staff a full team, and prevents months of a team building something that was never viable.
Underfunding integration engineering is how projects stall at ninety percent complete. Correct staffing balance saves more than any rate negotiation.
Architecture and evaluation strategy need senior input but rarely full-time. A part-time lead over a mid-level team is usually the most efficient structure available.
Retrieval, integration, and evaluation infrastructure built once serves every later use case, so the second project costs a fraction of the first.
Small team for feasibility, larger for build, smaller for maintenance. Keeping a build-phase team through maintenance is a common and expensive habit.
A clear, proven path from idea to production-ready AI.
A named team on your roadmap with a predictable monthly cost, scaling up or down by phase, available through our dedicated development team model.
Defined scopes such as a proof of concept or a first production build, priced with acceptance criteria tied to measured performance.
Individual engineers embedded in your team where you have the management capacity to direct them and want knowledge retained in house.
Teams working with overlap into the US business day, which reduces elapsed time to decisions and is why nominal rate comparisons across regions mislead.
A small outsourced team of three to four people typically runs $15,000 to $35,000 per month depending on seniority and region. The equivalent in-house US team costs $60,000 to $90,000 monthly in salary alone, plus months of recruitment.
Team size and engagement duration, far more than hourly rate. Most projects are overstaffed on AI specialists and understaffed on integration engineers, which inflates cost while slowing delivery.
Start with a proof of concept before staffing a full team, balance staffing toward integration engineering, use a part-time architect over a mid-level team, and scale the team down for maintenance rather than holding build-phase capacity.
Both. Fixed price for defined scopes such as proofs of concept and first builds. Dedicated team billing monthly where the roadmap extends and scope evolves as evidence arrives from real usage.
Indicative only. The rates here reflect typical market ranges, but team size and duration drive total cost far more than rate does, and those depend on your data readiness and integration surface.
If AI is core to your product and you need permanent institutional capability, eventually yes. Recruitment takes three to six months per role, so most organizations use outsourced capacity to move while hiring proceeds.
Tell us what you want to build, what data you hold, and which systems it touches. We will come back within one business day with a proposed team structure, indicative monthly cost, and a realistic phase plan. Book a free consultation through our contact page .
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