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Comparison

Hire AI engineers vs AI agency: cost, time and fit

Should you hire AI engineers or bring in an agency? Compare salaries, time to a live system, the skills one hire cannot cover and who maintains the result.

Updated 6 min read

The short answer

If you want to hire AI engineers, expect salaries well above $130,000 a year per person, plus recruiting time and the other skills a project needs. An agency fits when you want a working system sooner. Hiring fits when AI is core to your product and you can run a team.

Key points

  • Median AI engineer pay sits around $134,000 to $145,000
  • One engineer rarely covers data, integration and operations
  • Outside partners reached deployment about twice as often as in-house builds
  • Hiring fits when AI is your core product
  • Fair comparison starts with your goal, not the model

Side by side

Engineers and agency side by side

The same project looks different depending on who builds it.

CriterionHire AI engineersWork with an agency
Cost modelSalaries, benefits and tools, paid yearlyProject or retainer, no payroll
Time to startRecruiting comes first, then onboardingWork can begin once scope is agreed
Skills coveredWhatever the hires bring, usually narrowA team across data, integration and testing
OwnershipInside your company by defaultDepends on the contract; confirm it
MaintenanceYour team owns upkeepAgency support or handover
Best fitAI is core to your productAI supports an existing business process

What it costs to hire AI engineers

  • Salary

    Medians of $134,023 and $145,080

  • Benefits and tools

    Costs that come on top of pay

  • Recruiting

    Time spent searching and onboarding

  • Team gaps

    Extra roles beyond one hire

Salary data gives a first anchor. The Bureau of Labor Statistics figure cited by Coursera puts the median annual pay of AI engineers at $145,080.

Glassdoor reports a lower median base salary of $134,023 in the United States. Software developers in general had a median wage of $135,980 in May 2025, according to the Bureau of Labor Statistics.

Those are salaries only, and the AI development cost page shows what building can run. Benefits, tools, cloud use and recruiting time come on top, and a single engineer is rarely the whole team.

Time to hire and time to launch

  1. 01

    Hiring path

    Search, offer, notice period

  2. 02

    Onboarding

    Learn your data and tools

  3. 03

    Build

    First system takes shape

  4. 04

    Launch

    Live and in use

Hiring starts with a search, then onboarding, then months of learning your data and tools. Nothing runs until that is done.

MIT's NANDA study found top performers moving from pilot to full implementation in about 90 days, while larger enterprises took nine months or longer.

An agency removes the search and onboarding steps, but you still need to give it access, data and decisions.

Skills one hire cannot cover

One engineer can cover

  • Model selection and prompts
  • Prototype building
  • Basic evaluation

Often left uncovered

  • Integration with business tools
  • Security and privacy review
  • Monitoring and incident response

Searches like machine learning developers or ml experts describe one profile. Real projects need more than one.

The math is the same when you hire generative AI engineer talent or hire machine learning expert help on contract.

Someone must connect the model to your CRM and inbox, clean the data, test outputs and watch the system in production. NIST's framework lists testing, incident handling, human oversight and monitoring as organizational practices.

If you hire dedicated machine learning developers, ask who covers integration, security review and day-to-day operation.

Ownership, upkeep and risk

Hire in-house

  • Knowledge stays with your team
  • Depends on retaining each person
  • You run upkeep and monitoring

Use an agency

  • Ownership set by contract
  • Knowledge held by a wider team
  • Upkeep through support or handover

With a hire, the code and knowledge sit in your company, but they also leave if the person does. With an agency, ownership depends on the contract, so confirm it before you sign.

Upkeep matters. MIT's report notes that most systems fail to retain feedback or adapt over time, and RAND estimates more than 80 percent of AI projects fail, twice the rate of other IT projects.

Either way, plan who watches, fixes and improves the system after launch.

When each option fits

  • AI is your product

    Hire and build a team

  • AI supports a process

    An agency is usually faster

  • No technical lead

    Hiring alone is risky

  • Want both

    Agency now, hires later

Hire AI engineers when AI is the product, the roadmap spans years and you can manage a technical team.

Choose an agency when AI should improve an existing process and you want a working system without building a department.

MIT's data shows outside partnerships reaching deployment about 67% of the time, while internal builds got there about 33% of the time.

To compare outside builders, read the guide to AI development companies.

Our data

What AI answers when buyers ask

We asked ChatGPT, Gemini and Perplexity 10 buyer questions about AI consulting. They named 95 companies, and 81% appeared in only one of the three.

CriterionChatGPTGeminiPerplexity
Companies named342560
Sites cited mostslalom.com (4 of 10)effectivesoft.com (6 of 10)phosailabs.com and wiserbrand.com (3 of 10)

Nexum measurement, October 20, 2026, via DataForSEO: 10 buyer questions to each AI, US English, 30 answers in total. 6 companies appeared in all three. Google showed an AI Overview for 7 of the 10 questions.

FAQ

Hire AI engineers questions

  • Median pay lands between about $134,000 and $145,000 a year, depending on whether you follow Glassdoor or the Bureau of Labor Statistics figure cited by Coursera. Benefits, tools and recruiting costs are extra.

  • Software developers had a median wage of $135,980 in May 2025, per the Bureau of Labor Statistics. AI engineer medians reported elsewhere run similar or somewhat higher, so expect a specialist premium rather than a different league.

  • Hire a generative AI engineer when you will keep building and running AI for years. Use an agency when you need one working system soon and lack the team. Many companies start with outside help, then hire.

  • An ML specialist selects or trains models, prepares data, evaluates outputs and helps deploy them. They usually need partners for integration, security and operations, which is why one hire rarely finishes a project alone.

  • Define the business problem first, then the data you have and the systems the model must connect to. Check the candidates' production experience, not only research skills, and decide who will maintain the result.

  • In the MIT NANDA study, outside partners reached deployment about 67% of the time, while internal builds did so about 33% of the time. The sample is limited, so treat it as a signal, not a rule.

  • Yes, the two can overlap. An agency can build the first system while your engineer learns it, then hand over documentation and access. Agree on the handover in writing before the project starts.

  • AI can produce confident but false output, as NIST documents, and projects often stall before production. Add human review on risky steps and monitor after launch. The entry on AI governance explains the basics.

Related

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