Machine Learning
Custom AI Model Development vs Existing AI Models
Use the smaller system that can do the job
An existing model, called through an API, is the right tool when the job is drafting, summarizing, or answering from text you supply. A custom model is the right tool when the decision depends on your history: fraud flags, demand, a defect in an image, or a label your staff already apply. Buying a custom model for a FAQ is how budgets disappear.
Aqlithos lists both. The machine learning page covers training, evaluation, deployment, and monitoring. The generative AI page covers retrieval and hosted language models. The choice is made in Discover, after the data is described.
How the two options differ in practice
NIST's AI Risk Management Framework is a useful external reference for either path: know the use, measure it, and assign an owner. It is not a certification Aqlithos claims.
Existing model or custom model.
| Existing model API | Custom model | |
|---|---|---|
| You need | A prompt, documents, and a limit | Labeled examples and a baseline |
| You maintain | Prompts, retrieval, and cost | Data pipeline, model, and monitoring |
| Typical Aqlithos stack | Python, a model API, PostgreSQL | Python, scikit-learn or PyTorch |
| Wrong use | Memorizing a private handbook | Replacing a spreadsheet nobody has labeled |
A decision you can make this week
If you cannot show fifty correct examples, you do not have a custom-model project yet. You have a labeling problem or a document problem. Say so early. Aqlithos would rather ship retrieval, or a reviewed extraction workflow, than train a model on a tidy demo set that does not match production.
When the labels exist, the delivery is a pipeline that can be run again, a metric against the human process, and a place the prediction is served. A notebook on a laptop is not the delivery. That sentence is also on the machine learning service page, because it is where these projects fail.
Questions buyers ask
- Is a custom model more accurate?
- Only on a task where your labeled data beats a general model. Accuracy is measured against your baseline, not claimed in a sales page.
- Do we have to pick PyTorch or TensorFlow first?
- No. Aqlithos picks the library after seeing the data. scikit-learn is often enough for tabular work. PyTorch or TensorFlow is for a neural net you actually need.
- Where is this service on the site?
- Custom models sit under machine learning and artificial intelligence. Language over your documents sits under generative AI.
Sources
Looking to build an AI-powered solution for your business? Explore Aqlithos engineering services or contact our team to discuss your project.
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