In-House vs Agency AI Development: Pros, Cons, Real Costs
The in-house vs agency AI development decision comes up in almost every conversation we have with product and engineering leaders. "Should we build this team internally, or should we work with you?" The answer depends on your timeline, budget, and how core AI is to your product. Here's the honest breakdown — and the math.
The Real Cost of Hiring In-House
Most cost comparisons anchor on salary. That's a mistake — salary is maybe 60% of the actual cost of an employee.
Here's a full-loaded cost breakdown for a senior AI/ML engineer in 2026 in a US tech hub:
| Cost Component | Annual | |---|---| | Base salary (senior AI engineer, SF/NYC) | $180,000–$230,000 | | Equity (dilution-adjusted, 0.1–0.25% options) | $30,000–$80,000/yr value | | Benefits (health, 401K match, etc.) | $25,000–$40,000 | | Employer taxes (FICA, FUTA, SUTA) | $18,000–$25,000 | | Recruiting fees (15–25% of base, external recruiter) | $27,000–$57,000 (one-time) | | Hardware, tools, licenses | $5,000–$15,000/yr | | Management overhead | Hard to quantify, real | | Total Year 1 fully-loaded cost | $270,000–$420,000 |
And that's one person. A functional AI team — one tech lead, one AI engineer, one backend engineer, one data engineer — runs $800K–$1.5M/year fully-loaded before you've shipped anything.
Then there's the timeline. In a good hiring market, finding and closing a strong AI engineer takes 2–4 months. Add 4–8 weeks ramp time before they're productive. That's 3–6 months before you have real output. If the hire doesn't work out, you restart that clock.
The Real Cost of Working with an Agency
A qualified AI engineering agency charges $150–300/hour for senior engineering work, depending on specialization and location. Project-based pricing for a defined scope is more common.
Ballpark for common engagements:
| Engagement | Timeline | Cost Range | |---|---|---| | Proof of concept / feasibility sprint | 2–4 weeks | $10,000–$30,000 | | MVP with core AI feature | 6–12 weeks | $60,000–$120,000 | | Production-grade AI product | 12–20 weeks | $120,000–$250,000 | | Ongoing retainer (iterate + maintain) | Monthly | $15,000–$40,000/month |
What's included that often isn't with in-house: project management, QA, infra setup, documentation, and a team that has already solved adjacent problems. The ramp time is days, not months.
The ceiling: a great agency is bounded by what you've scoped. They're not embedded in your org, don't attend your all-hands, and won't absorb institutional knowledge the way a full-time engineer will.
Head-to-Head: The Real Tradeoffs
Speed
Agency wins, always, for the first 6 months. An agency with an existing team can start delivering working code in week 1. Even in the best case, in-house doesn't match that until month 4 or 5 after recruiting, onboarding, and ramp.
For companies trying to ship before a competitor, a funding milestone, or a regulatory deadline — speed often decides the question.
Cost
Depends entirely on time horizon.
For a 6-month project: agency wins. For a 3-year product with sustained velocity: in-house becomes competitive as the recruiting cost amortizes. The crossover point is roughly 12–18 months for a team of 2–3 engineers.
The mistake most companies make is underestimating how long "temporary" agency engagements run. Projects scope-creep. Iterations extend. Many "6-month agency engagements" become 18-month partnerships.
Quality
This is where the reputation matters more than the model. A great agency with senior engineers and strong AI expertise will outperform a mediocre in-house team. An average agency will underperform a strong in-house team. Vetting matters.
Ask for references from similar projects. Ask to talk to the engineers you'll actually work with, not just the sales lead. Review code samples or prior project retrospectives.
Institutional Knowledge
In-house wins, long term. Engineers embedded in your org understand the product context, the customer, the internal systems, and the politics. That depth of context produces better software decisions over time. Agencies, however strong, are always working with an abstracted view of your problem.
Flexibility
Agency wins for variable workloads. If you have an intense 12-week build followed by a relatively quiet 6 months of iteration, the agency model lets you dial effort up and down without layoffs or over-hiring. For most early-stage companies, this flexibility is underrated.
When to Build In-House
- AI is a core, sustained competitive advantage — not a feature
- You're post-Series B with a clear 2–3 year product roadmap that requires dedicated engineering capacity
- Regulatory or data privacy requirements mandate that work happens internally (some fintech/healthtech situations)
- You've already validated the AI approach and are scaling a known solution
- You can afford the 3–6 month time-to-productivity hit
When to Hire an Agency
- You need to ship something in the next 90 days
- You're validating whether an AI approach is worth a full team investment
- You want senior AI expertise you can't recruit fast enough (or afford full-time)
- You have a defined scope with clear success criteria
- Your internal team has strong product/domain knowledge but limited AI/ML depth
- You want to run an intensive build, then hand off to a smaller in-house team
The Hybrid That Actually Works
The pattern we see work best at growth-stage companies: agency for the first build, transitioning to a small in-house team for iteration and ownership once the core product is proven.
Concretely: a $100K agency engagement to build and validate, followed by hiring one or two strong engineers to maintain and extend. The agency deliverables become the foundation; the in-house team takes it from there.
This avoids the worst outcomes — neither the 6-month hiring delay of pure in-house nor the long-term knowledge-drain risk of pure agency.
Whatever model you choose, scoping the first build correctly is the most used decision you'll make. A 30-minute conversation can save you 3 months of rework.
Related: Build vs Buy Your AI MVP · How Much Does an AI MVP Cost in 2026? · How We Ship AI MVPs in 3 Weeks
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