Menlo Ventures' 2024 State of Generative AI in the Enterprise report documented something that surprised a lot of technology leaders: 76% of enterprise companies are now buying AI capabilities rather than building them in-house. That's a dramatic shift from just two years prior, when the conventional wisdom was that any self-respecting technology organization should own its own AI stack.
What changed? The economics finally became visible. Not the headline costs — everyone has always been able to calculate compute and tooling. What became visible was everything else: the costs that don't show up on a vendor invoice but absolutely show up on your income statement, your headcount, and your strategic calendar.
This article lays out the full picture. I'm going to be honest here — sometimes building in-house is the right answer. But most organizations making that choice are working from an incomplete cost model, and the gap between what they think it costs and what it actually costs is significant.
The Costs Everyone Calculates
Any competent technology leader can estimate these before signing a budget:
- Compute costs: GPU instances, inference costs, training runs. These are real and quantifiable, though they frequently exceed initial estimates as usage scales.
- Tooling and infrastructure: Vector databases, orchestration frameworks, monitoring platforms, development environments. The open-source ecosystem is rich but not free to operate.
- Data preparation: Cleaning, labeling, and structuring the training or retrieval data your AI system needs. Usually estimated in weeks; usually measured in months.
- Initial development: Engineer time to build the system. This is the cost most organizations anchor on when making the build vs. buy decision.
These are real costs. They're also the minority of the total. The majority lives in the next category.
The Costs Most Organizations Miss
Talent Acquisition and Retention
Hiring AI/ML engineers is expensive and slow. Median compensation for senior ML engineers at enterprise companies now exceeds $280,000 in total comp in competitive markets. More importantly, the talent pool for engineers who can build production-grade AI systems — not toy demos, but systems that are reliable, observable, and maintainable — is genuinely thin. Organizations often spend 6-9 months recruiting for these roles, during which the AI initiative stalls. And when you do hire them, keeping them is its own challenge: AI engineers are in extraordinary demand and will leave for more interesting problems the moment yours feels like maintenance.
Time-to-Value Delay
This is the hidden cost that kills the most build-in-house initiatives. Every month you're hiring, building infrastructure, and iterating on data pipelines is a month your competitors who bought and deployed are capturing market share, reducing costs, or improving customer outcomes. In a domain moving as fast as AI, a 12-month head-start for a competitor is not recoverable in most markets. Time-to-value isn't just a cost — it's a strategic risk.
Security Debt
Teams building AI systems for the first time almost universally underinvest in security architecture. This isn't negligence — it's a sequencing problem. Security feels like something you add after the system works, and the system takes longer to build than expected, so security gets deferred. The security debt accumulated during an in-house AI build then gets discovered either during an audit or, worse, during an incident. The cost of unwinding that debt is consistently higher than the cost of doing it right the first time.
Build estimates almost always cover initial development. They rarely account for what happens after launch: model drift, dependency updates, prompt engineering adjustments as underlying models change, retraining as data evolves, and the ongoing engineering capacity required to keep a production AI system performing. A system that costs $500K to build often costs $200-400K per year to maintain properly. That's a 10-year cost of $2.5-4.5M that wasn't in the original business case.
Opportunity Cost
The engineers you're deploying to build AI infrastructure are not building your core product. The leadership attention consumed by an in-house AI initiative is not being applied to your market strategy. The organizational energy spent navigating a complex build project is not driving your primary revenue streams. These aren't hypothetical costs — they're the concrete consequences of resource allocation, and they're almost never included in build vs. buy analyses.
When Building In-House Actually Makes Sense
I said I'd be honest, so here it is: there are legitimate reasons to build in-house, and they shouldn't be dismissed.
The Third Option: Partner, Not Just Buy or Build
The build vs. buy framing misses a third path that's increasingly common and often optimal: partnering with a firm that builds custom AI implementations on your behalf, using your data, within your security perimeter, but with the expertise and infrastructure already in place.
This isn't the same as buying an off-the-shelf AI product. It's custom development with an expert team who has already solved the hard problems — the security architecture, the evaluation frameworks, the deployment infrastructure — so you're not paying for them to learn.
The organizations getting the most value from AI in 2026 are largely in this category. They're not paying enterprise SaaS prices for generic tools, and they're not bleeding capital and time on in-house builds. They're deploying faster than the in-house teams and getting more customization than the SaaS products offer.
Before deciding to build in-house, ask this: "If we could have a production-ready, custom AI system in 90 days instead of 12 months, what would that be worth?" The answer — in revenue captured, costs avoided, and competitive positioning — is the real comparison point. Most organizations find the gap is much larger than they expected.
The Full Cost Model
When you add the visible and hidden costs together — talent, time-to-value, security debt, maintenance burden, and opportunity cost — the real cost of a typical in-house enterprise AI build over three years is usually 3-5x the initial development estimate. That's not a reason to never build in-house. It is a reason to go in with an accurate cost model rather than the abbreviated version that usually drives the initial decision.
The 76% who are buying rather than building aren't giving up on AI competency. They're being strategic about where their organization's energy creates the most leverage. In most cases, that leverage is in applying AI to your core business, not in becoming an AI infrastructure company.
The question isn't whether to build or buy. It's where your organization's energy creates the most competitive advantage. Most companies aren't in the business of building AI infrastructure — they're in the business of using it.