The question we hear most often
A client wants "AI" in their product. Great - but that word covers everything from calling OpenAI's API to spinning up a custom-trained model on your own infrastructure. Those are wildly different projects with wildly different costs, timelines, and risks, and picking the wrong one wastes months.
Here's the framework we actually walk clients through.
Start with three questions
1. Is your problem general or specific to your data?
If you need summarization, translation, classification of everyday text, or conversational Q&A - a general-purpose LLM API already does this well. You're not going to out-train GPT or Claude on general language understanding with a smaller budget and less data.
If your problem depends on patterns unique to your business - fraud signatures specific to your transaction data, defect types specific to your manufacturing line, churn patterns specific to your user base - a general API has never seen your data and won't capture what matters. That's where custom modeling earns its cost.
2. How much labeled data do you actually have?
Custom models need data — a lot of it, and it needs to be labeled correctly. Before considering a custom build, we ask clients bluntly: do you have thousands of clean, labeled examples of the exact thing you're trying to predict? Most companies think they do. Most don't, once you actually audit it.
If the honest answer is "a few hundred messy examples," an API-based approach (potentially fine-tuned lightly on top of what you have) beats a from-scratch model every time.
3. What's your actual latency and cost ceiling at scale?
APIs charge per call. That's fine at low volume and can become the dominant cost line at high volume. Custom models have high upfront cost (data prep, training, infrastructure) but a much flatter cost curve once built.
"We had a client running 2 million API calls a month for a simple classification task. A custom model paid for itself in four months - but we wouldn't have known that without doing the math first, before writing any code."
A simple decision table
| Situation | Recommendation |
|---|---|
| General language task (summarize, translate, chat) | Use an API |
| Domain-specific pattern, limited data | Use an API, fine-tune lightly if needed |
| Domain-specific pattern, large labeled dataset | Custom model likely worth it |
| Extremely high call volume, simple/stable task | Custom model likely worth it |
| Need to iterate fast, validate product-market fit first | Use an API - don't build infrastructure before you know the feature matters |
The mistake we see most
Teams reach for a custom model too early - before they've validated that the AI feature is even worth having. Building a training pipeline, labeling data, and standing up inference infrastructure is weeks of work minimum. If the feature might get killed after user testing, that's weeks you can't get back.
Our default advice: start with an API, ship it, see if people actually use it. Only invest in a custom model once you have real usage data proving the feature matters and real evidence that a general model's limitations are actually costing you - not before.
Where this leaves most teams
If you're unsure which camp you're in, the fastest way to find out isn't more research - it's a small, time-boxed pilot. Two weeks with an API-based version tells you more than two months of architecture debate.
Tech Valley Corp
Nikunj Kumar, CTO & COO
