A Buyer’s Guide to AI: Build vs. Buy (and the Dread of Evaluating 100 Startups)
What to build yourself, what to buy, and how to avoid the vendor-selection headache.
Buying AI: From Finding the Right Tool to Getting It Approved
Over 17,800 AI startups were founded between 2023 and 2025.1 Many are actually building valuable solutions that can deliver enormous ROI. But when a promising idea lands in your inbox, you’re probably already wondering how it compares with the 100 other startups that might be doing the same thing.
Buying means more than answering that email. You need to scout other vendors, sit through demos, run evaluations, and eventually get everything through IT, security, and procurement. Just the thought can be exhausting. And with AI making software easier to build than ever, another question quickly comes to mind: How hard could it really be to build something that gets us 90% of the way there?
When to Build, When to Buy
To weigh building against buying, it helps to look at how an AI application comes together. It starts with a business problem and an idea. Once you’re set on it, you start building: connecting the relevant data and tools, implementing the business logic, and thinking through how people will use it. Then you deploy it. Once it’s running, you need to monitor performance, keep audit trails, catch subtle mistakes, continuously improve the agent, etc.
The building part is fun: coming up with the idea, wiring things together, and thinking about how people will use it. It’s even more fun with AI coding tools, because you can make progress so quickly. The annoying part is operating it: getting into the weeds of evaluating live agent runs, spotting failures that even manual reviews miss, improving performance, etc. It’s tedious work, without the excitement and visibility of building a new feature.
To strike a balance, we’ve seen many separate their application logic from the operational plumbing underneath. Monitoring, evaluation, audit trails, and agent improvement recur across applications, so they can be provided by a shared platform. In-house teams can then focus on the workflows and integrations they want to build, drawing on their understanding of the business requirements and internal systems.
The quality of that platform matters because improvements can benefit many applications, compounding as more applications use it. We see many teams buy the platform but build the applications. This gives them advanced tooling that reduces manual work and saves them the substantial research and engineering needed to develop and improve those tools.
At Sovara, We Do the Research So You Don’t Have To
At Sovara, we scout and evaluate both end-to-end AI solutions and platforms to help companies find the right approach for their use cases. We help you determine what is worth building, what is worth buying, and which underlying capabilities to adopt.
Our team has worked with and evaluated many AI solutions and platforms. Given your requirements, we will search for and shortlist the most promising candidates and evaluate what we can get our hands on. We will compile this into a report so you can see all your options in one place, make an informed build-vs-buy decision, and get to AI ROI without the usual vendor-selection headache.
If you’d like help finding the right AI solutions and deciding what to build or buy, reach out to us at hello@sovara-labs.com.