When To Use Small Language Models Over Large Language Models

Large language models (LLMs) continue to run on the tightrope between efficiency and trust. Users consider it effective, but doubt its accuracy.

It can also be an overkill for some use cases. For example, using LLMs may not be the best choice for all internal HR tasks, given their high computational costs.

In all these conflicts, a newer type of model is picking up: small language models (SLMs). These are simpler models trained on a smaller dataset to do a very specific function. It ticks all boxes on high efficiency, more trust, and low cost.

Some recent studies also say small language models are the future of Agentic AI. In this article, I’ve listed use cases where an SLM would be more efficient than an LLM.

Top SLM use cases across different business functions

If you’re wondering where to get started with your SLM journey, I've compiled the best SLM use cases across common business functions below. 

Customer service

LLM models can be helpful for customer service, but with major caveats. These models are pre-trained on a vast dataset, often scraped from the internet. Some of this knowledge may or may not be applicable to your customer service, especially when company policies are specific. You become at risk of having customer-facing chatbots that hallucinate. For example, a customer service chatbot on Air Canada’s website promised a bereavement refund to a customer against a policy that never existed.

SLMs make more sense for customer chatbots and complaint portals. These portals often deal with highly repetitive issues/queries and have a limited repository of company policies to refer to. The model can be trained easily on past customer ticket data and company policies. That‘s enough for the model to answer customers.

Of course, SLM can’t handle everything, and where the bot can’t answer the query, you can always involve a human. If it’s a chatbot, you can provide a support number for the customer to call. If it’s a ticket management platform, the ticket can be auto-resolved if it’s a known problem to SLM, or else be assigned to a customer support executive. At least, you can rest assured that automation is not promising something to a customer that’s not possible.

​Sales/Marketing 

LLMs definitely excel for some use cases in sales and marketing, especially content creation. The larger training data helps to handle different topics. But using LLMs for more niche tasks like lead qualification/nurturing and personalized outreach may not be the best choice. Its generalized responses will not give a good impression to your potential customers.

SLM helps you create more personalized outreach messages. It can be trained on your proprietary dataset to qualify leads. You can draft some outreach messages that have worked for you in the past and use SLM models to generate further outreach messages based on them. SLMs help you move away from generic AI outreach messages.

Finance 

LLMs can be used for general market analysis. But it lags behind for high-risk tasks like fraud detection and compliance monitoring. Fraud rates are rising in both consumer and business accounts. Despite companies building fraud-detection systems, fraudsters keep finding new ways to bypass them. The model needs continuous retraining. This is where SLM shines and LLM takes a back seat.

It takes more time and resources to retrain an LLM compared to an SLM. SLM can be continuously updated with the latest fraud data to make the system more robust.

Likewise for compliance data. LLMs can even have outdated compliance information, resulting in misses. SLM trained on a small dataset is easy to review and refine to ensure only the latest regulations are available in the knowledge base.

Human Resources 

LLMs are great for drafting general job descriptions, employee communication, or training content. Tasks with high compliance risks (example: creating policy documents, employment agreements, and immigration documents) are where things get tricky.

Countries or even states keep updating their labor laws.  For example, the Australian government increased parental leave to 24 weeks in 2025, and it will be extended by another two weeks starting in 2026. New York increases the minimum hourly wage for gig workers recently. Japan started promoting work-life balance and flexible work arrangements for new parents.

Using LLMs means continuously checking that the knowledge base in the backend is accurate and up to date. Leaving out any old policy file by mistake in the database would result in hallucinations.  

Small language models mean way more control on the knowledge base and more assurance for compliance. For example, Deel AI is a small language model curated by its compliance experts. These experts continuously update the knowledge base so you get the most up-to-date and accurate answers.

Business operations

A new AI adoption survey from G2 shows that nearly 75% of businesses use multiple AI features in daily business operations. AI is driving operational efficiency and improving productivity. Both SLM and LLM have a part to play in it.

LLMs shine in strategic tasks like risk management, demand forecasting, supplier review, and more. Its vast knowledge base helps it to consider all angles before making a suggestion. On the other hand, SLM works best for repetitive gruntwork. Think of invoice management, shipment tracking, route optimization, background checks, or predictive maintenance. The tasks can run on a limited set of rules and company's past data.

Companies are benefiting from using SLM in routine, repetitive tasks. For example, Checkr, an employee background screening platform, shifted from LLM to SLM to automate background checks and saw better accuracy, faster response times, and a 5X reduction in costs.

SLM vs LLM: Who wins the battle?

In the comparison of SLM and LLM, the answer is not to choose between SLM and LLM. The better approach is to use them together as a hybrid model. Both SLM and LLM have their own strengths and weaknesses. SLM does a good job in tasks with well-defined scopes and limited datasets. But for tasks demanding reasoning, LLM is a much better choice.

Let’s take supply chain management for example. A hybrid approach is better for supply chain management where:

• LLM takes on strategic tasks like risk analysis, demand forecasting, and more
• SLM automates high-volume and repetitive operational tasks, such as route management, invoice processing, etc.

Using both SLM and LLM together creates a complete model to handle all the nuts and bolts of the supply chain. ​

Top SLM models ready for custom training

One good thing about getting started with your SLM implementation is that there are models available for fine-tuning. You can choose one of these depending on your use case:

1. Meta Llama 3.1 (8B parameters): A high efficiency model right which stands out for use cases requiring multilingual support
2. Microsoft Phi-3 (3.8B parameters): A tiny model perfect when you have a super-specific task requiring strong reasoning.
3. Google Gemma 2 (2B parameters): A lightweight model with multimodal capabilities, helping you handle both text and images.

Using SLMs was never this easy

With more SLM models being launched, you don’t even have to create any model from scratch. Just choose an existing model that fits your use case, build a knowledge base of information for it, and you're good to go.  

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