LLM or ML? Choosing the right tool for the problem
What should you know about LLM vs machine learning?
Use ML for prediction, classification and detection on structured data; use LLMs for language; many systems need both. The LLM vs machine learning choice comes down to the shape of the input, the cost of a wrong answer, the volume of decisions and the budget per decision; most production systems route between the two.
The LLM vs machine learning question comes up in almost every scoping conversation we have, usually phrased as "can't we just use GPT for this?" Sometimes the answer is yes. Often it is no, because the problem is a prediction on rows of structured data, where a gradient-boosted model trained in an afternoon will be more accurate, thousands of times cheaper per decision and far easier to explain. And frequently the answer is both: an LLM to read the unstructured input, a classical model to make the decision, and an LLM again to explain it. This article gives a buyer a practical way to tell the cases apart, with the costs and risks of getting it wrong.
What each tool is actually good at
Classical machine learning learns a function from examples: given these features, predict this number or this class. It excels when inputs are structured, examples are plentiful, and the output is a score, a category or a forecast. Large language models are trained to predict text and, as a result, understand and generate language across almost any topic without task-specific training. They excel when the input is prose, documents, conversation or code, and when the output is language or a structured extraction from language. Neither is a superset of the other. An LLM asked to forecast next month's demand from a table is guessing; a churn model asked to summarise a support ticket cannot begin.
When to use ML vs LLM: the comparison
| Question | Points to classical ML | Points to an LLM |
|---|---|---|
| What is the input? | Rows, features, time series, sensor data, transaction logs | Text, documents, images with text, conversation, code |
| What is the output? | A number, a probability, a class, a ranking | Prose, a summary, an answer, a structured extraction, a plan |
| How many decisions per day? | Millions; unit cost must be near zero | Thousands to hundreds of thousands; a few paise to a few rupees each is acceptable |
| How much labelled data exists? | Plenty of historical outcomes | Little or none; the model brings general knowledge |
| How wrong can it be? | Errors must be measured statistically and bounded | Errors are tolerable when a person or an evaluation can check |
| Does it need to explain itself? | Feature importance and monotonic constraints are available | Explanations are plausible text, not proofs |
| How fast must it answer? | Milliseconds | Hundreds of milliseconds to seconds |
Classical ML use cases that LLMs should not replace
- Demand and revenue forecasting from sales history, seasonality and promotions
- Churn, propensity and uplift scoring from behavioural and account data
- Fraud and anomaly detection on transactions and events at high volume
- Credit and risk scoring where regulators expect explainability and monotonic behaviour
- Recommendation ranking over large catalogues within a page's latency budget
- Predictive maintenance and quality control from sensor readings
- Pricing and inventory optimisation with constraints
Each of these has structured inputs, a numeric or categorical output, high volume and a need for measured accuracy. A tree-based model or a small neural network will beat an LLM on all four counts, and it can run on a modest server for the cost of electricity. We cover the first of these in demand forecasting with machine learning and the second in churn prediction.
Where LLMs are the right tool
- Reading documents: extracting fields from invoices, KYC papers, contracts and claims
- Answering questions over a knowledge base, with retrieval to ground the answer
- Customer conversation on chat, WhatsApp and voice, with actions gated by policy
- Summarising tickets, calls, meetings and long threads
- Classifying free text into categories with little or no labelled data
- Turning natural language into SQL, API calls or structured requests
- Drafting: replies, reports, product descriptions, code
In each case the input is language and the model's general understanding replaces a training set you do not have. The quality is checked with an evaluation suite rather than a confusion matrix, and the cost per call is real, so volume matters. For a buyer-level view of what these systems look like, see what is an AI agent.
Predictive vs generative AI: why most systems need both
The interesting systems combine the two. A KYC pipeline uses an LLM to read a scanned document and extract fields, then a classical model to score risk from those fields and the applicant's history, then rules to decide. A support agent uses an LLM to understand the customer's message and a classical model to predict which resolution is most likely to work. A personalisation engine uses embeddings from a language model to describe products and a ranking model to order them in milliseconds. In each, the LLM handles the part that is language and the ML model handles the part that is a decision at volume. Designing this split is the core of what we do under AI/ML development, and the KYC document intelligence build for an NBFC is a concrete example of the pattern.
The cost dimension
A classical model's cost is almost entirely in building and maintaining it: data preparation, feature engineering, training, monitoring for drift. Serving is nearly free. An LLM's cost is the reverse: little training, but every call has a price that scales with volume and with how much text you send. A million classifications a day through an LLM API is a serious monthly bill; the same through a small classifier is a rounding error. At scale, the sensible pattern is often to use the LLM to label a few thousand examples and then train a small model on those labels for production. Forecasting the LLM side is covered in LLM inference costs.
The risk dimension
Classical models fail statistically: a known error rate that can be measured, bounded and monitored. LLMs fail linguistically: a confident answer that is wrong, an instruction followed that should not have been, a format that drifts. Both are manageable, but with different tools. ML needs holdout validation on time, drift monitoring and calibration. LLMs need an evaluation suite of real cases, output validation, retrieval grounding and policy gates on any action. For regulated decisions, credit, insurance underwriting and medical triage among them, classical models with explainable features remain the defensible choice; an LLM can prepare the inputs and explain the result, but should not make the call. The NIST AI Risk Management Framework is a useful vocabulary for these distinctions when talking to a risk function.
A decision procedure you can run in a meeting
- Write the input and the output in one sentence each. If the input is a table and the output is a number or class, start with ML.
- Count the decisions per day and multiply by a plausible per-call LLM price. If the number is uncomfortable, ML or a small fine-tuned model.
- Ask whether you have historical outcomes. Plenty means ML is trainable; none means an LLM's general knowledge is the shortcut.
- Ask what a wrong answer costs and who checks it. Unchecked, high-cost decisions want a measurable model and often a human.
- Ask whether the problem has a language part and a decision part. If both, plan for both models and design the hand-off.
A worked example
A lender wanted to automate the first review of loan applications and asked for "an LLM that approves loans". Scoping split the problem. Reading the uploaded documents, bank statements, identity papers and salary slips, was a language and vision task with no labelled training set, and an LLM with retrieval handled it well behind an evaluation suite. Scoring the application from the extracted fields and the bureau data was a structured prediction with years of outcomes to learn from and a regulator who expected explainability, so a gradient-boosted model with monotonic constraints did that. An LLM then drafted the explanation for the underwriter from the model's feature contributions. Three components, two kinds of model, each doing the part it is good at. The system ran in shadow mode beside human underwriters before it was allowed to route applications, and the risk team could see the score and the reasons for every case. The fintech industry page describes the data-residency arrangements such builds usually need.
Team and timeline
Choosing correctly is the work of a Sprint Zero discovery: ten working days, $3,250 / ₹2,00,000 credited to the build, in which a data scientist and an AI engineer map the problem into its language and decision parts, check the data for each, and estimate the cost per decision. Building follows under AI/ML development, from $17,500 / ₹11.2L, for the classical side, and LLM applications, from $21,000 / ₹13.6L, for the language side; a combined system is typically ten to fourteen weeks. Models are model-agnostic by design, routed across OpenAI, Anthropic, Google and open-weight options, and you own the code, prompts and models. Care Plans on the pricing page cover drift monitoring and evaluation reruns.
Before you start: a checklist
- State the input and output of the decision in one sentence each
- Estimate decisions per day and the acceptable cost per decision
- Inventory historical outcomes that could serve as training labels
- Identify any regulatory requirement for explainability
- Separate the language part from the decision part if both exist
- Decide how the output will be checked: statistical validation, an evaluation suite, or a person
- Agree the latency budget
- Plan shadow mode before any automated action
Glossary
- Classical ML: models trained on examples to predict a number or class from structured features
- LLM: a large language model that understands and generates text without task-specific training
- Drift: a change in input data or outcomes that degrades a model over time
- Evaluation suite: a set of real cases with expected outputs used to check an LLM system before and after changes
Related reading
See RAG vs fine-tuning for the LLM-side choice and data leakage: the silent killer of ML projects for the ML side. Prices and programmes are on the pricing page.
Match the tool to the shape of the problem, and expect the shape to have two parts.
Frequently asked questions
When should you use machine learning instead of an LLM?
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When the input is structured data, the output is a number or class, the volume is high, historical outcomes exist to train on, and the decision must be measured or explained. Forecasting, churn, fraud and credit scoring are typical cases.
Can an LLM replace a predictive model?
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Rarely. An LLM reasoning over a table is guessing without the statistical grounding a trained model has, and it costs far more per decision. Use the LLM to read language and prepare inputs, and a classical model to predict.
How do you decide between predictive and generative AI for a project?
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Split the problem into its language part and its decision part, then check data, volume, cost and risk for each. A Sprint Zero discovery does this in ten working days and produces a build plan under AI/ML development.