Build, Buy, or Call an API: How We Actually Decide

Clients often inquire about why Shanti Infosoft doesn't build its own AI models, and the straightforward answer is that doing so would be the most time-consuming and costly path to achieving results that are only slightly inferior to what an API can provide immediately. Engineers find building appealing, and it sounds impressive in a pitch, but these capabilities must pass three tests before we commit: whether it's our true advantage, how rapidly it's evolving, and what it costs at our actual scale.

When the capability is a commodity—such as text generation, transcription, translation, OCR, embeddings, or general chat—calling an API from providers like OpenAI, Anthropic, or Google is preferable, as a hosted API offers a better result on day one than a small team could build in a quarter, and someone else bears the cost of maintaining it.

We opt for an API when the capability isn't our differentiator, and the vendor improves at a faster pace than we could. The trade-off involves paying per call, depending on their availability, and having data leave our systems. However, these risks are manageable: we encapsulate the provider in a single interface to switch easily, and review the data-handling terms before processing any sensitive information.

We purchase when the solution is already a company-level offering rather than just a feature. When a vendor's entire business revolves around the thing we need and it's a well-established, unexciting problem, buying is more cost-effective than developing a subpar version and maintaining it indefinitely. The question then becomes whether owning it would distract the team from delivering the client's actual product.

Building only makes sense when the capability is our unique selling proposition, when our proprietary data influences the outcome, or when the per-call cost at our volume surpasses a subscription model. Even then, building rarely involves training a model from scratch; it typically entails orchestration, our data pipeline, retrieval tailored to the domain, and evaluation—areas no one else handles.

The foundational model is still sourced from an API. The edge lies in the underlying infrastructure. Before committing to any approach, we design the switch. The provider is behind an interface, and our data remains in a controlled format. Costs are monitored for changes, as what was cheap at 10,000 calls a day may become expensive at a million.

Decisions to build or buy aren't permanent; they can evolve as volumes increase, prices fluctuate, and vendor terms change. The goal isn't just to choose wisely once, but to ensure we can adapt quickly if needed. At Shanti Infosoft, most AI capabilities ultimately become API calls wrapped in a thin, switchable layer, with only the true differentiator built in-house.

For practical examples of how we apply this build-vs-buy decision, visit https://shantiinfosoft.com . If you want to understand how we arrived at this line on a recent project, please explore the case study.