Two paths to shipping AI into your product. Both have legitimate use cases. Neither is right for every situation.
What's missing from most comparisons is the actual numbers — not ranges designed to sound plausible, but the specific costs that make the decision obvious once you run them.
What a senior AI engineer actually costs
A senior engineer capable of independently leading an AI product build — someone who can do system design, model selection, prompt engineering, evaluation setup, and production deployment without significant hand-holding — runs $180,000–$250,000 in base salary in most US tech markets. With equity, benefits, payroll taxes, and overhead (laptop, tools, recruiting fees), the fully-loaded annual cost is typically $220,000–$320,000.
That's one person. Most meaningful AI projects require at least two senior engineers: one focused on ML/model work, one on backend infrastructure.
Hiring timeline is a cost that often goes unaccounted. In the current market, sourcing and closing a senior AI engineer takes 3–6 months. Expect to:
- Spend 2–4 weeks sourcing candidates
- Run a 4–6 week interview process
- Negotiate for 2–3 weeks
- Wait out a 4–6 week notice period
That's 3–5 months before your first line of code is written. Then figure another 2–3 months of ramp time before a new hire is productive at full capacity in your codebase.
Realistic time from "we decided to hire" to "we have someone shipping meaningful output": 5–9 months.
What an AI agency costs
A focused AI project — one agent, one integration, one workflow automated — typically runs $15,000–$80,000, with most production-grade builds landing between $25,000 and $50,000.
Timeline from contract to production: 6–14 weeks for most scoped engagements.
Ongoing: agencies typically offer retainer arrangements for maintenance and iteration, ranging from $2,000–$8,000/month depending on scope.
The cost comparison looks like this:
| Approach | Year 1 Cost | Time to First Ship | |---|---|---| | One senior AI engineer (hired) | $220K–$320K | 5–9 months | | Two senior AI engineers | $440K–$640K | 5–9 months | | Agency project ($40K) + retainer ($4K/mo) | $88K | 6–14 weeks | | Agency project only, no retainer | $15K–$80K | 6–14 weeks |
The agency path is dramatically cheaper for year one if you're doing one to two projects. At three or more substantial AI projects per year, the in-house math starts to shift.
When in-house wins
You have an ongoing AI product, not a one-time build. If your product is AI — if the AI feature is your primary differentiator and you ship AI improvements every sprint — an in-house team is the right answer. You can't outsource your core product loop.
Your competitive advantage depends on proprietary model behavior. If you're trying to build something that your competitors can't easily copy, and the differentiation lives in your specific fine-tuning, your eval infrastructure, or your approach to training data, that knowledge needs to live inside your organization.
You'll ship more than three significant AI features per year. Once you're past a certain frequency of AI work, the amortized cost of in-house talent beats the project-by-project agency cost. The crossover is roughly three to four major AI projects per year, assuming each project costs $30K–$50K at an agency.
You have a strong technical co-founder or CTO who can manage AI engineers. AI engineers are hard to manage if you don't understand the domain. If you can't evaluate their work or give meaningful technical feedback, you'll have a slow, expensive team that's hard to hold accountable.
When an agency wins
You're building your first AI feature. The biggest mistake teams make on their first AI project is underestimating the operational complexity: evaluations, monitoring, prompt versioning, model updates, production failure modes. An experienced agency has shipped a dozen similar projects. You're buying that institutional knowledge.
You need speed. A 6–14 week agency engagement gets you to production faster than any hiring process. If there's competitive urgency or a customer commitment, the agency path is the only path that fits the timeline.
Scope is fixed and well-defined. If you can describe the project clearly — "a customer-facing chatbot that answers questions about our 500-page product documentation, integrated into our Zendesk workflow, with human handoff on escalation" — an agency can scope it, price it, and deliver it. Fixed scope + fixed cost is the best-case agency engagement.
You don't have a strong technical AI bench internally. If no one on your current team has built and shipped a production AI system, building your first one in-house is a risky way to learn. Costly mistakes at the architecture level — wrong model choice, no eval infrastructure, ignoring monitoring — are expensive to fix later.
You want to evaluate the ROI before committing to headcount. A $40K agency project that ships in 8 weeks gives you real production data. Did it actually reduce support volume? Did users engage with it? That data is worth having before you hire two engineers to own this long-term.
The hidden cost of hiring wrong
One note on the downside case for in-house: hiring the wrong person is expensive in a way that's hard to fully recover from.
A senior AI engineer who turns out to be a poor fit — technically or culturally — costs you the recruiting investment, their salary for the months they were employed, the opportunity cost of delayed delivery, and the morale impact on adjacent teammates. Realistically, a bad senior hire costs $200,000–$400,000 by the time you've backfilled the role.
The agency equivalent of this — a project that goes sideways — is contained. You lose the project cost, you have work to redo, but you don't have a 12-month notice-to-backfill cycle.
The hybrid approach
The model that works best for most growth-stage companies:
Agency builds the first version. You move fast, get to production in 8–12 weeks, and validate that the use case actually delivers value. The agency establishes the architecture, sets up monitoring and evaluations, and documents the system.
In-house maintains and iterates. Once the system is live and validated, you hire one strong engineer (not necessarily senior ML — a solid backend engineer with LLM experience) to own maintenance, small iterations, and the integration of new capabilities. This person costs $140K–$180K/year and has a working system to take over, not a blank page.
Agency for the next project. When the next AI initiative comes up, you repeat the cycle — agency for the build, in-house for the ownership.
This approach extracts the main benefit of each path: agency speed and expertise for the build, in-house ownership for long-term stability.
The bottom line
Hire in-house when AI is your product, you ship AI constantly, and your competitive advantage is in the model work itself.
Use an agency when you're doing your first build, when scope is fixed and defined, when you need production fast, or when you want to validate ROI before committing to headcount.
The hybrid — agency builds, in-house maintains — is the model that makes sense for most companies that are serious about AI but aren't primarily AI companies.
Run the year-one cost comparison. It's less close than most people expect.