Small language models/SLM: A smarter fit for business

Unlike LLMs, which are generalists, SLMs are specialists. They excel at domain-specific tasks like customer support, internal search, compliance monitoring, and data classification.

Why SLMs make sense

SLMs are gaining traction because they solve real problems without the overhead. According to Harvard Business Review, SLMs are redefining enterprise AI by enabling real-time decision-making on edge devices, reducing energy consumption, and improving data privacy[1].

The Economist highlights another key point [2]: SLMs are more accessible. They don’t require massive infrastructure or cloud dependency, making them viable for mid-sized firms and teams with limited budgets. In fact, some experts believe SLMs could outperform LLMs in quality when trained on high-value, curated data.

Real-World Examples

Companies like Microsoft, Meta, and Mistral AI have released compact models [3]. Microsoft’s Phi-3, for instance, runs efficiently on consumer-grade hardware and rivals larger models in performance for targeted tasks [4].

Platforms like eesel AI use SLMs to power internal support agents, helping teams retrieve documents, answer FAQs, and automate repetitive tasks [5]. This local processing boosts privacy and reduces latency.

In field operations, Forbes reports that service engineers use SLMs on mobile devices to query manuals and troubleshoot issues without needing internet access [6]. Clinicians use them to analyze patient data securely, keeping sensitive information on-device.

SLMs vs. LLMs: The choice

SLMs aren’t a downgrade. They are a better fit for many business needs, due to several reasons:

• Faster to deploy – no need for massive compute clusters
• Easier to customize – fine-tune them with your own data
• Cheaper to run – lower infrastructure and energy costs
• More secure – ideal for on-premise or edge deployments

But LLMs still have their place. They shine in:

• Complex reasoning and multi-domain tasks – LLMs can handle broader queries and generate more nuanced responses
• Creative generation – for tasks like writing, summarizing, or ideation across varied topics
• Cross-functional integration – LLMs can serve as general-purpose assistants across departments.

Critical trade-offs

Choosing between SLMs and LLMs is about fit. SLMs offer speed, control, and cost-efficiency. But they may lack the depth and flexibility of LLMs in complex scenarios. LLMs, while powerful, come with higher costs, slower deployment, and greater governance challenges.

If the goal is to automate a narrow workflow or improve internal efficiency, SLMs are often the right answer. If the challenge spans multiple domains or requires deep reasoning, LLMs may be worth the investment.

High-level decision making framework

Use an SLM when:

• The task is narrow and well-defined (e.g. internal search, FAQ automation, document classification)
• You need fast deployment with minimal infrastructure
• Data privacy is a priority (on-device or edge deployment)
• You want lower operating costs and faster iteration cycles
• You’re working with curated, domain-specific data

Use an LLM when:

• The task spans multiple domains or requires complex reasoning
• You need creative generation (e.g. summarization, ideation, content creation)
• You’re building a general-purpose assistant across departments
• You have the infrastructure to support high compute demands
• You need flexibility across varied use cases

Here are five practical tips to help organizations deploy SLMs effectively:

1. Start with a narrow use case

Don’t try to solve everything at once. Begin with a well-defined task, like automating internal FAQs or tagging documents. This keeps the scope manageable and helps validate the model’s value quickly.

2. Use curated, high-quality data

SLMs thrive on clean, domain-specific data. Avoid noisy or overly broad datasets. The better the input, the more accurate and useful the output.

3. Deploy locally when possible

If privacy or latency is a concern, consider running the model on-device or at the edge. This reduces reliance on cloud infrastructure and keeps sensitive data in-house.

4. Monitor outputs

Set up a feedback loop to track performance. Watch for drift, bias, or hallucinations. Regular monitoring helps maintain reliability and trust.

5. Iterate fast

SLMs allow for rapid prototyping. Use that to your advantage, but make sure each iteration ties back to a clear business goal.

SLMs are smaller, and can also be smarter when deployed with appropriately. If you’re considering AI adoption, these tips can help you get started the right way.

Footnotes

[1] Harvard Business Review: “The Case for Using Small Language Models” — https://hbr.org/2025/09/the-case-for-using-small-language-models

[2] The Economist: “Breakthrough Technology: SLMs and the Future of AI” — https://impact.economist.com/projects/breakthrough-technology/slm-future-of-ai/

[3] Microsoft, Meta, Mistral AI model releases — https://mistral.ai/solutions

[4] Microsoft Phi-3 Overview — https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/

[5] eesel AI Blog — https://www.eesel.ai/blog/small-language-models

[6] Forbes Tech Council: “The Next Big Thing in AI” — https://www.forbes.com/councils/forbestechcouncil/2025/03/03/the-next-big-thing-in-ai-small-language-models-for-enterprises/