It's Not Build vs. Buy Anymore. It's Both, Done Right.

Many software evaluations hit the same moment. The demo goes well, the use case fits, and then someone in the room says it: "We could probably just use AI to build this ourselves.” It's a reasonable "instinct” and the honest answer is, sometimes they're right. 

The build vs. buy debate gets treated as binary when the reality is almost always more nuanced. Build or buy, in-house or vendor selection, off-the-shelf or custom. But most mature technology decisions don't live at either extreme. They live in the blend: purpose-built vendor solutions handling some foundational capabilities, while internal builds own others. 

There’s no doubt that AI has changed software development forever, but many companies fail to realize that you can’t just apply generic LLMs to every challenge and call it a strategy. Different types of AI are suited for different tasks and getting the right AI for the right challenge is its own decision layered on top of the build decision.

 Getting that blend right is harder than picking a side. But it's where the best strategies and decisions get made.

When Building Is the Right Answer 

Building makes sense when the capability is genuinely core to how you compete. If it's core IP, if no vendor fits your specific data model or workflow without rebuilding half their product, or if regulatory requirements make third-party data ownership untenable, then building isn't just defensible, it's the right answer. 

The risk isn't building; it's the assumptions that usually surround the decision. The assumptions typically are that the V1 cost is the total cost, that timelines will hold, and that the expertise required to build a functional product are already in the room. Those assumptions are where most internal build decisions go wrong. 

Building is a commitment, not a project. After V1 comes bug fixes, security patches, compliance updates, and continuous innovation to keep pace with quickly changing markets. It’s also the slow accumulation of technical debt that compounds until the system is too fragile to touch. On a three-year horizon, a vendor licensing fee is often the cheaper option once you account for the ongoing engineering cost of maintaining something you built yourself. That math rarely makes it into the original estimate.

When Buying Makes More Sense 

Buying makes sense when the capability is foundational rather than differentiated. If a customer would never know or care whether you built it or bought it, that's a signal worth paying attention to. 

There's also an argument that doesn't get enough weight in these conversations. A vendor who has spent years solving the same problem across hundreds of customer environments brings a depth of engineering maturity your team would have to earn the hard way. That expertise is baked into the product through sustained R&D investment, rigorous testing, and a product roadmap shaped by real-world complexity. Your internal team will be starting from scratch and learning those lessons one at a time. That's not a knock on your engineers. It's just the reality of how expertise is built. 

Getting to V1 is harder than it looks. A prototype that works in a research environment and a solution that runs reliably at scale, meeting SLAs, handling edge cases, and producing consistent results across thousands of real transactions, are two entirely different things. That gap takes months to close, not sprints. And once it’s closed, the knowledge of how it works lives with the team that built it. When they move on, the organization is left maintaining something it can’t fully understand, fix, or extend. 

There's also the opportunity cost. Every sprint your engineers spend building and maintaining internal tooling is a sprint they're not spending on what actually differentiates your company.

The AI Question Is a Separate Decision 

Here's where the conversation gets more complicated: if you’re building a solution you must ask yourself, which type of AI is right, for this problem? There’s an incorrect assumption that if you patch enough AI systems together you can solve any problem. Not true. This matters more than most teams realize going in.

Some problems need numerical, purpose-built models. These are models that are built to give you the same result every time. Use cases like pricing decisions, contract compliance checks, financial calculations, fraud detection, etc. These use cases require consistent, repeatable, auditable outputs. They need explainability. They need governance. General-purpose large language models (LLMs) are architecturally unsuited for these requirements, not because of implementation quality, but because of how they're designed. LLMs are probabilistic. They generate different outputs from identical inputs. They can't reliably explain their reasoning. For high-stakes, precision-dependent workflows, that's not a gap you close with better prompting. 

Other problems are genuinely well-suited to LLMs: drafting, summarization, extracting structure from unstructured data, generating first drafts of contracts or proposals, handling natural language queries. These are areas where LLM flexibility is a feature, not a liability. 

The mistake isn't using AI. It's assuming one type of AI can do all of it. The right architecture for most enterprise workflows is a blend: numerical models where precision and governance are non-negotiable, LLMs where language flexibility and scale matter, and human judgment for the decisions that shouldn't be delegated to either. 

This is true even for mature, purpose-built AI solutions. A well-designed pricing solution, for example, doesn't rely on a single model. It layers multiple AI techniques, each chosen because it's the right tool for a specific task, whether that's forecasting demand, generating recommendations, or helping users understand what's driving a particular outcome. That layered architecture is independently configured to each customer's business, and it's exactly what a homegrown LLM-first approach skips. It's not something you arrive at in V1.

The Right Answer Is Usually a Blend 

A vendor-provided platform might handle the governance, execution, and compliance layer through CLM or CPQ software, while your internal team builds the proprietary models or business logic that reflect your specific competitive advantage. The infrastructure gets bought; the differentiation gets built on top. 

This isn't a hedge. It's recognition that different parts of the same workflow have genuinely different requirements, and forcing a single answer across all of them produces a worse outcome than matching the solution to the need. 

A Framework for Making the Call 

Before committing to any path, run through these questions: 

On the core build vs. buy decision: 

• Is this capability part of what makes us competitive, or is it infrastructure that enables the business? 
• Does a vendor already solve this at the maturity level we need? 
• What does the three-year total cost look like: build + maintain + staff vs. licensing? 
• What are our engineers not building while they build this? 
• Do we have the domain expertise to build this well, or would we be learning as we go? 
• Who owns this when the people who built it leave? 

If you want to use AI to build, also ask: 

• Does this use case require numerical, repeatable output? (If yes, a general LLM is the wrong tool) 
• Do we need to explain or audit the AI's decisions? (LLMs are black boxes by design) 
• Is real-time response required? (LLM inference latency may disqualify it) 
• Is factual accuracy non-negotiable? (LLMs hallucinate) 
• Where in the workflow does language flexibility help vs. where does precision govern? 
• Are we trying to use AI because it's the right tool, or because it's the current expectation? 

The forcing question: Could you decompose this problem into parts, some of which should be built, some bought, and some handled by different types of AI? If yes, that's probably the right architecture. 

What You're Really Deciding 

The build vs. buy question is really a question about where to invest scarce resources: engineering time, budget, and organizational attention. Getting it right doesn't mean picking a side. It means being honest about what each part of the problem actually requires what type of solution, and having the discipline to match the solution to the need rather than forcing a single answer across the board. 

The companies making the best technology decisions right now aren't the ones that always build or always buy. They're the ones that know the benefits of each path and are willing to make the right call for the problem they’re trying to solve. 

The companies that regret the build decision rarely regret it in year one. They regret it in year three, when the vendor has shipped four major releases and their internal tool is exactly where they left it. You’re not just deciding whether you can build this. You’re deciding whether you can keep building it, indefinitely, against an industry that never stops moving.