AI build vs buy decision tool

The three paths

• Buy. Use an existing AI product, such as a SaaS tool or built-in assistant, and configure it rather than building anything.
• Blend. Buy the model or platform, then build your own workflow, prompts, and integrations around it. This is where many teams land.
• Build. Develop a custom solution, which may include training or fine-tuning your own models and running them on your own infrastructure.

How to use it

1. Pick one project. Answer for a single AI project, not your whole strategy. A support assistant and a pricing model may call for different answers.
2. Answer the eight questions. Choose the answer closest to your situation on fit, advantage, data, team, timing, integration, scale, and upkeep.
3. Read the recommendation. See the path that fits best, why it won, what to watch out for, and the next steps. Copy it to share with your team.

Build vs buy for AI, explained

Should we build or buy AI?

For most organizations, buy or blend. Buy when a product already does the job and the task isn’t what sets you apart. Build only when nothing fits, the capability is core to your edge, and you have a team to maintain it for years. Many teams land in between, buying the model and building the workflow around it.

What does “blend” mean?

Blending means buying the AI model or platform and building your own workflow, prompts, and integrations around it. You get tailored results without training or hosting models yourself, which is why it fits so many projects.

How does this tool decide?

Each of the eight questions gives each path, buy, blend, and build, 0 to 3 points depending on your answer. The path with the most points is recommended. If the top two are within two points, the result calls it a close call and shows both.

What are the hidden costs of building AI?

Mostly the work after launch: preparing and cleaning data, building tests to measure quality, monitoring, retraining, security reviews, and the team to keep it all running. The model call itself is often the smallest part of the cost.

What are the risks of buying AI?

Vendor lock-in, pricing that grows with use, limited control over how the product behaves, and questions about how your data is stored and used. Check the contract and data terms before you commit, and keep your data portable.

Can we start with buy and move to build later?

Yes, and it is often the safest route. A purchased or blended prototype shows what the solution really has to do, and what it costs at scale. That is far better evidence for a build decision than a forecast made in advance.

I’m Courtney Stephenson, an AI strategy and implementation leader, and I built this tool. If you’re weighing a build or buy decision, let’s talk it through.