The question is no longer whether to use artificial intelligence, but what type of model fits each problem. Over the past two years I have seen technical teams spend budget on generative model APIs for tasks where a custom-trained classifier would have cost a fraction and been more predictable. And the opposite too: trying to solve an information extraction problem in natural language with rules and decision trees that an LLM solves in an afternoon of work. This comparison is intended for those who have to decide between the two options with real budgets and deadlines.
What really separates an LLM from a traditional model
A traditional model (logistic regression, random forest, gradient boosting, SVM, specific convolutional networks) is trained for a specific task with a labeled dataset and produces bounded outputs: a class, a number, a probability. An LLM is a language model pre-trained on massive corpora that predicts the next token and, from there, generalizes to tasks for which it was not explicitly fine-tuned.
The most important practical difference is not the architecture, but the usage contract. With a classic model you know exactly what input it expects and what output range it produces. With an LLM the input is free text and the output is free text, which shifts the engineering effort from training toward prompt design, output validation, and continuous evaluation.
Cost: it’s not just the price per token
The cost of an LLM is measured per input and output token, and scales with usage. That of a traditional model is concentrated in the labeling and training phase, and then inference is cheap and local. In a real case of support ticket classification into five categories, with tens of thousands of tickets per month, inference with a small fine-tuned model costs cents per thousand requests on your own infrastructure, while calling a large LLM for each ticket multiplies the expense and adds network latency. If the volume is high and the task is stable, the classic model usually wins on total cost.
Data: the bottleneck moves
A traditional model requires quality labeled data. If you don’t have it, the project stalls. An LLM lets you start without labels, but you need evaluation examples to know whether it gets it right. In practice, many teams start with an LLM to prototype, generate labels with it, and then train a classic model with those human-reviewed labels. This distillation strategy works well when the domain is narrow; it fails when the classes are ambiguous or the language is highly variable.
Scenarios where each option wins
When a traditional model is the right choice
- Structured tabular data: risk scoring, churn prediction, fraud detection with numerical and categorical variables. Gradient boosting models remain the benchmark in these cases according to the documentation of the main libraries in the sector.
- Strict latency requirements: if you need responses in milliseconds without depending on an external API, a small local model is more reliable.
- Regulatory auditability: in sectors with an obligation to explain decisions, a model with documented feature importance is easier to defend than the output of an LLM.
- Repetitive and well-defined tasks: product image classification, anomaly detection in time series, sentiment labeling with a very specific domain.
When the LLM contributes something the classic model cannot
- Information extraction from unstructured text: converting emails, contracts, or reports into structured fields without defining a rigid schema in advance.
- Tasks with changing instructions: summarizing in a different tone, reformatting outputs, or adapting responses to a context that varies without retraining.
- Content generation and transformation: assisted writing, nuanced translation, rewriting.
- Rapid prototyping: validating whether a use case has value before investing in labeled data.
The mistake I see repeated: treating the LLM as a database
A pattern that appears again and again is asking the model to remember business information (prices, policies, catalog) and trusting that it will return it correctly. It doesn’t do so reliably: it hallucinates plausible but false data. The solution is not to change the model, but to add information retrieval (RAG) with a consultable source of truth. And even then, validation of the final response is still necessary for sensitive data.
Another frequent mistake: evaluating an LLM with ten manual examples and considering it good. Without a representative and repeatable evaluation set, there is no way to detect regressions when you change the model version or modify the prompt. According to the documentation of the main providers, versions are updated frequently and behavior can vary without clear notice.
Practical comparison by decision criteria
| Criterion | Traditional model | LLM |
|---|---|---|
| Data needed to start | Labeled and representative | None for prototyping; examples for evaluation |
| Inference cost | Low and predictable | Variable depending on tokens and provider |
| Latency | Very low locally | Depends on network and model size |
| Explainability | High with the right techniques | Limited; the reasoning chain is not always reliable |
| Adaptation to new tasks | Requires retraining | Changing instructions is usually enough |
| Robustness to unusual inputs | Degrades predictably | May invent or ramble |
Hybrid architectures: what usually works in practice
In most mature projects the answer is not to choose one of the two, but to combine them. A common pattern: a classic model filters and classifies the bulk of the volume (for example, separating urgent queries from informational ones), and only the ambiguous or complex cases go to an LLM that writes or extracts information. This reduces cost, keeps latency under control, and reserves the generative model’s capacity for where it adds value.
Another pattern: using the LLM to generate synthetic training data or to label at scale, and then training a classic model with human review. It works well in closed domains; it is advisable to review a sample to detect biases inherited from automatic labeling.
Limitations to accept before starting
Neither option solves poorly defined data problems. If the labels are inconsistent, the classic model learns the inconsistency and the LLM reproduces it with more confidence. Nor is there a guarantee of one hundred percent accuracy in either case: any system in production needs monitoring, confidence thresholds, and an escalation path to human review.
LLMs also have context limits, cost that grows with input length, and dependence on external providers if they are not deployed locally. Traditional models, for their part, do not generalize outside the distribution they were trained on: a change in the format of the input data can degrade performance without any alarm going off.
How to decide in practice
A sequence I usually recommend: first define the success metric and the expected volume; then check whether a labeled data source exists or can be built; if the task is natural language and there is no data, prototype with an LLM to validate the value; if the volume is high and the task is stable, evaluate training a classic model with the generated data; and in all cases, set up an evaluation set before moving to production. The decision between LLM and traditional model is not ideological: it depends on volume, latency, required explainability, and data availability. Changing your mind when the numbers justify it is part of the job.