Build vs Buy: AI Features

TL;DR: Most teams default to buying AI features because it feels faster. But off-the-shelf tools rarely fit the way your product works, and the lock-in costs you more down the track. This post walks through when to buy, when to build, and how to tell the difference.

Most teams default to buying AI features because it feels faster. It rarely is. Off-the-shelf tools are built for the average use case, and your product almost certainly is not average. The build vs buy AI decision is worth getting right before you spend a dollar.

What does 'buy' actually mean in AI?

Buying AI features usually means one of three things: a plug-in API like OpenAI or Anthropic, a pre-packaged SaaS module (think AI chat widgets or document summarisers), or a low-code AI platform you bolt onto your stack.

These tools move fast. You get something working in days, not months. For generic tasks, that speed is real.

The problem is what comes next. The vendor controls the model, the pricing, and the roadmap. When they change any of those, your product changes with them. Teams often discover this when costs triple after a pricing update, or when a model version shift breaks a workflow they depended on.

Buying also means sharing the same feature set as every competitor who bought the same thing.

When buying makes sense

Buying works well when the AI task is generic and non-core to your product. Here are some cases where it is the right call:

• Spell-check or grammar tools. Not your core value. Buy it.
• Basic chatbots for support. If you are not differentiating on support AI, a commodity tool is fine.
• Translation. Unless language is your product, use a third-party API.
• Document OCR. Solved problem. No reason to build.

The test is simple. Ask yourself: if your competitor used the same AI feature, would it matter? If no, buy. If yes, build.

When building makes more sense

Building is the right call when the AI feature is close to your core product promise. If the AI is what makes your product different, handing that to a third party is a risk.

Building also makes sense when:

• Your data is sensitive and you cannot send it to a third-party API.
• You need fine-grained control over model behaviour and outputs.
• You are doing something the off-the-shelf tools do not do well.
• The feature will need to iterate fast based on user feedback.

For a good primer on what it actually takes to add AI to an existing product, read how to add AI to your existing app or software. It covers the practical groundwork before you write a line of code.

The hidden cost of buying

Buying looks cheaper on day one. That calculation changes quickly.

Most AI APIs charge per token or per call. At low volume, that is fine. At scale, it becomes a significant line on the P&L. And because you do not own the infrastructure, you cannot optimise it.

There is also the integration cost. AI tools rarely slot in cleanly. You end up writing middleware, handling edge cases, and managing quirks in the vendor's API. That work is real engineering time, just on someone else's product.

Then there is vendor lock-in. Switching costs get high fast. Data pipelines, fine-tuned prompts, UI components tied to API responses. Undoing that takes months.

The hidden cost of building

Building is not free either. Be honest about what it takes.

Building a proper AI feature means choosing a model, setting up infrastructure for inference, handling latency, managing prompt versioning, monitoring outputs, and building feedback loops to improve performance over time. If your team has not done this before, the learning curve is steep.

Time to market is the other factor. A buy approach might get a working prototype in a week. A custom build might take two or three months. Depending on where you are in your product cycle, that gap matters.

The real answer for most teams is not a binary choice. It is a staged approach: buy to validate, build to scale.

What Devwiz sees in practice

We have built AI features for clients across government, enterprise, and scale-up products since well before AI became a boardroom topic. With 200+ apps built since 2015, we have watched teams get this decision wrong in both directions.

The teams that overbuy end up with a patchwork of AI tools that do not talk to each other, data scattered across vendor systems, and no real AI capability of their own. When the vendor changes, they are stuck.

The teams that overbuild try to train models from scratch when a fine-tuned open-source model would do the job. They spend six months on infrastructure work instead of shipping product.

The sweet spot is clear data on what you are actually trying to do, a realistic read on your team's AI build capacity, and a decision framework that matches the feature's importance to your product.

If you want to think through the architecture side, AI app development covers how we approach building AI into products from the ground up.

CTOs working through this at the platform level often have a different set of constraints. The tech for CTOs page covers how we work with engineering leaders on AI product decisions.

How to make the call

Here is a practical framework for the build vs buy AI decision:

1. Is this core to your product's value? If yes, lean build. If no, lean buy.
2. Is your data sensitive? If yes, build or use a self-hosted model.
3. How fast do you need to move? If speed to market is critical right now, buy and plan to replace.
4. What is your scale trajectory? If volume will grow fast, model the API costs at 10x current usage before committing.
5. Does your team have AI build experience? If not, factor in the ramp-up time honestly.

None of these questions have universal answers. But they stop you from defaulting to buy because it is easier, or defaulting to build because it is more interesting.

James Killick writes about the commercial side of AI product decisions at jameskillick.co.

If you want to talk through the decision for your product, get in touch with the Devwiz team.

FAQ

Q: Can I start by buying and switch to a custom build later?

Yes, and this is often the right move. Buy to validate that the feature is worth building, then build when you have proven demand. The catch is to keep your integration layer thin so switching costs stay low. If you build tight coupling to the vendor API from day one, the migration will hurt.

Q: What if we need a feature that no vendor offers?

Then you have no choice but to build. This is actually a good sign. If no vendor has solved the problem, there is a chance the feature is genuinely differentiated. That is exactly the kind of AI capability worth owning.

Q: How much does it cost to build a custom AI feature?

It depends heavily on the complexity and your existing infrastructure. A simple AI feature that calls a managed model API and wraps it in custom logic might take a few weeks. A full ML pipeline with custom training, monitoring, and feedback loops could take months. Be specific about scope before you get a quote.

Q: Are open-source models worth considering?

Yes. Models like Llama and Mistral have closed the gap with commercial options on many tasks. Self-hosting an open-source model gives you data control and no per-token costs, at the expense of running your own inference infrastructure. For teams with data privacy requirements, this is often the right path.

Q: What is the biggest mistake teams make in the build vs buy AI decision?

Defaulting to buy without stress-testing the vendor dependency. Teams pick a tool, build around it, and only discover the lock-in when costs spike or the vendor makes a breaking change. Before you commit, map out what it would take to replace the vendor in 12 months. If the answer is painful, that should change how you integrate it.

Frequently asked questions

Can I start by buying and switch to a custom build later?

Yes, and this is often the right move. Buy to validate that the feature is worth building, then build when you have proven demand. The catch is to keep your integration layer thin so switching costs stay low. If you build tight coupling to the vendor API from day one, the migration will hurt.

What if we need a feature that no vendor offers?

Then you have no choice but to build. This is actually a good sign. If no vendor has solved the problem, there is a chance the feature is genuinely differentiated. That is exactly the kind of AI capability worth owning.

How much does it cost to build a custom AI feature?

It depends heavily on the complexity and your existing infrastructure. A simple AI feature that calls a managed model API and wraps it in custom logic might take a few weeks. A full ML pipeline with custom training, monitoring, and feedback loops could take months. Be specific about scope before you get a quote.

Are open-source models worth considering?

Yes. Models like Llama and Mistral have closed the gap with commercial options on many tasks. Self-hosting an open-source model gives you data control and no per-token costs, at the expense of running your own inference infrastructure. For teams with data privacy requirements, this is often the right path.

What is the biggest mistake teams make in the build vs buy AI decision?

Defaulting to buy without stress-testing the vendor dependency. Teams pick a tool, build around it, and only discover the lock-in when costs spike or the vendor makes a breaking change. Before you commit, map out what it would take to replace the vendor in 12 months. If the answer is painful, that should change how you integrate it.