Off-the-shelf AI models (also known as foundation models or pre-trained models) are the heavyweights you've probably heard of. Think ChatGPT from OpenAI, Google’s Gemini, or Meta’s LLaMA. These models are already trained on vast datasets (we’re talking terabytes of web pages, books, code, and more), and they’re designed to handle a wide range of tasks out of the box, including language generation, image recognition, summarisation, search, and more.

For many businesses, these models are a great place to start. You can tap into them via API, pay as you go, and get results within hours, not months.

• Speed to market: You could be prototyping within a day, and live within weeks.
• Lower upfront cost: You avoid the big investment in compute power, engineering, and data needed to train a model from scratch.
• Built-in intelligence: These models have already been trained on huge datasets, so they perform surprisingly well with very little extra input.

In short, they’re ideal for most general use cases, and they’re powering everything from AI-powered customer service bots to smart search features in apps like Shopify, Slack, and Duolingo.

In fact, OpenAI’s GPT models are now used by over 92% of Fortune 500 companies.

But there are times when “good enough” doesn’t cut it. Maybe your app needs to analyse highly technical language, medical records, or proprietary internal documents. Or maybe you're working in a regulated industry where data privacy is non-negotiable.

In those cases, a custom-trained model, or fine-tuning an existing one, can give you the edge.

Custom AI models are either trained from scratch (which is complex, costly, and resource-intensive) or built by fine-tuning a pre-trained model on your specific data. This allows you to control tone, accuracy, vocabulary, and context with much greater precision.

• You have niche data that generic models won’t understand.
• Your app requires domain-specific language, like legal, medical, or engineering jargon.
• Privacy is a priority, and you don’t want to send your users' data to third-party APIs.
• You need more control over how the AI behaves or responds.

Custom models are increasingly used in healthcare apps (like Babylon Health), legal research tools, and financial services platforms where detail and nuance matter. They can help reduce hallucinations, tighten up responses, and give your AI features a tone that matches your brand.

Just be warned: training or fine-tuning models can cost thousands (sometimes millions), especially when you factor in engineering time, infrastructure, and ongoing maintenance.

One of the most common misconceptions is that you need to pick one route and stick to it. But hybrid models, where an app blends pre-trained models with custom logic or domain-specific tuning, are often the smartest route.

You might use an off-the-shelf model for general language tasks, then layer on retrieval-augmented generation (RAG) techniques to pull in results from your private database. Or you might use a pre-trained vision model and fine-tune just the last few layers for a specific task like identifying your own product catalogue.

Think of it like buying a suit: an off-the-shelf one might work well, but sometimes you just need a bit of tailoring to make it fit like a glove.

Let’s break it down. While we won’t use bullet points here (we promised), consider the following comparison:

• Off-the-shelf models will get you from idea to MVP fast. You pay per API call, so your upfront investment is minimal, but long-term costs can add up depending on usage.
• Custom models demand more time, more engineering, and potentially dedicated AI infrastructure. But you gain long-term cost control, privacy, and precision.
• A hybrid approach gives you flexibility. You get the speed and power of existing models with a sprinkle of custom logic to boost performance where it matters most.

If you’re wondering about raw costs, training a model from scratch could run anywhere from £50,000 to over £1 million, depending on scale. Fine-tuning a smaller model or using open-source frameworks like HuggingFace can be significantly more affordable, but still not a weekend job.

If you’re trying to figure out if AI is worth building into your project, don’t worry, you’re not alone. This is one of the most common questions we get asked. The answer depends entirely on your goals, your budget, and your user base.

• Want a chatbot that can handle 80% of customer queries? An off-the-shelf model is a great starting point.
• Need an AI that understands your brand voice down to the nuance? Fine-tuning might be the better route.
• Need the best of both worlds? That’s where app developers like us can help you blend the two.

We’ve helped businesses across travel, retail, and health sectors figure out the best solutions for their app, not by selling them the most expensive option, but by helping them make smart, scalable choices. Sometimes that means using GPT out of the box. Other times, it could mean building a unique AI engine from the ground up.

The most important thing we tell our partners is this: don’t get distracted by the tech. Start with what you want your app to achieve. Then work backwards.

AI models are tools, not silver bullets. But in the hands of the right team, they can make your app smarter, faster, and more valuable to your users.

And if you’re still staring down the AI decision tree and feeling a little overwhelmed, speak to app developers who understand the balance between innovation and pragmatism. Whether you’re a global brand or a local business, it’s worth finding a team that’ll treat your product like it’s their own.