Speed to value wins
It’s time to thoroughly address a question that keeps many of our prospective customers up at night. And that question is, should you build your own AI or buy a solution?
While it sounds simple, the decision you make here will ripple through your organization for years. It'll affect your budget, your timeline, your team's capacity, and ultimately whether you're ahead of or behind your competitors when it comes to AI. Get it right, and you've solved a massive business problem. Get it wrong, and you're burning cash with no results in sight.
The right answer really depends on your situation. Sometimes building makes sense. Most of the time, it doesn't. Let's walk through how to think about this decision clearly, with actual numbers and real-world scenarios.
Before you can decide, you need to understand what you're actually choosing between. Building custom AI means taking your team (or hiring new people) and having them develop a solution from scratch. You're designing the architecture, choosing the technology stack, building the integrations, and managing the entire process. Your team owns the code, owns the roadmap, and owns the future. While this gives you complete control, the tradeoff is that you're responsible for everything.
Buying means selecting a commercial AI platform or solution that already exists and deploying it in your environment. The vendor owns the product, manages the roadmap, and handles the heavy lifting. You don't have to hire AI engineers or manage a complex project. You're trading customization and control for speed and lower upfront complexity. But you're also dependent on the vendor's roadmap and vision.
The most dangerous mistake companies make when evaluating build versus buy is looking only at the immediate budget impact. They see a vendor asking for $100,000 for software, blink, and then decide to "just build it" with their existing team. They're not doing the math on the full picture. So let's break down what you're actually spending in each scenario:
Building custom AI starts with hiring. You need specialized talent like AI/ML engineers, data scientists, platform engineers. And salaries for these people run $200,000 to $300,000 per person fully loaded.
Beyond salaries, you've got infrastructure costs. Cloud compute (AWS, GCP, Azure), data storage, specialized AI tools, and development infrastructure add up. We're talking $20,000 to $50,000 per year, easily. Then there's the hidden costs that nobody budgets for. Project management overhead. Onboarding costs. Training.
Once the solution is built, it's not done. You need to maintain it, update it, fix bugs, and improve performance. However, your team now spends 60% of their time maintaining and fixing the thing they built, not building new capabilities. The projected first year cost of $600,000 for building evolved to $150,000-$200,000 per year in ongoing maintenance costs, which you're locked into forever.
The total cost of ownership for a custom build over five years? You're looking at roughly $2 million to $3 million when you add it all up.
Buying a commercial solution has a completely different financial profile. There's usually a one-time implementation cost, between $50,000 to $150,000 depending on how complex your needs are. Then ongoing software costs $20,000 to $100,000 per year depending on usage. Over five years, you're only spending $200,000 to $700,000 total.
Now let's talk about deployment time, which is where buying wins most decisively.
A custom AI project typically follows this timeline: you need 4-6 weeks just to nail down requirements and architecture, because your team is learning as they go. Then development takes 12-20 weeks minimum, depending on complexity.
Testing takes another 4-6 weeks. Integration with your existing systems takes another 4-8 weeks, often longer. Deployment and stabilization takes another 2-4 weeks. Add it all up and you're looking at 26-44 weeks. That's six to eleven months. And this is the optimistic timeline.
Buying a pre-built solution? You're looking at 4-8 weeks total, start to finish. Discovery, configuration, integration, testing, and deployment all happen in that window. Some vendors can do it in less if it's a standard use case. You're looking at going live in 30-60 days instead of 6-12 months.
Most organizations underestimate the true cost of building because they don't account for the invisible expenses that don't show up as line items on a project budget. Let’s take a look at the most common costs new riders experience.
The first hidden cost is hiring and retention. Finding experienced AI engineers is harder than it sounds. There's a war for talent, which means you'll pay premium salaries, and even then, the person you hire might get poached by a bigger company in twelve months.
The second hidden cost is scope creep. You think you want "AI to handle document extraction." By month three of development, people realize they also need "AI to validate the extracted data" and "AI to integrate this with their reporting system". Scope, timeline, and budget all expand. Most custom projects end up being 30-50% more expensive than the original estimate because scope crept.
The third hidden cost is technical debt. When your team is racing to build something, they don't always build it the right way. Later, when you want to add new capabilities or fix performance issues, you discover the original architecture doesn't support it. This is incredibly expensive and time-consuming.
The fourth hidden cost is maintenance burden. After launch, your AI system needs monitoring, updates, security patches, and optimization. If you've got one engineer maintaining the system and something breaks, you've created permanent operational overhead that will exist as long as that system exists.
The fifth hidden cost is opportunity cost. While your engineers are building custom AI, they're not building other things. If you have three AI engineers tied up for a year on a custom build, you've essentially lost those three people from your roadmap. What could they have built instead? What business problems could they have solved?
Sometimes, building your own AI actually is the right call. But it's rare enough that you should really scrutinize whether you're the exception or the rule. With that said, building makes sense if:
Building does NOT make sense if…
Every path has risks. The key is understanding which risks matter for your situation and whether you can manage them.
The biggest risk of all? Choosing the wrong path. If you decide to build and it goes sideways, you've lost a year and millions of dollars with nothing to show for it. If you decide to buy and it doesn't meet your needs, you've got a non-performing solution. The cost of being wrong is high.
If this initial primer has you looking to explore further, talking to someone who understands both approaches is invaluable. We've helped dozens of enterprises work through this exact decision. We know the tradeoffs, the hidden costs, and the real financial picture.
We'll walk through your specific situation, show you what the timelines actually look like, model the costs based on your constraints, and give you an honest recommendation about what makes sense for your organization. No agenda—just clarity.
Schedule a conversation with us and get your build versus buy assessment. We'll help you make this decision with full information instead of intuition.
