Small vs Large Language Model: Choosing the Right AI for Your Enterprise

Small Language Models vs Large Language Models: Choosing the Right AI for Your Enterprise

Every enterprise evaluating AI faces a fundamental question: what size of model do we need? The instinct is to reach for the largest, most capable model available — GPT-4, Claude, Gemini Ultra — on the assumption that bigger is always better. But in practice, choosing the right model is an engineering and business decision with profound implications for cost, performance, privacy, and operational complexity.

Small language models (SLMs) — models with fewer than 13 billion parameters, such as Microsoft’s Phi-3, Mistral 7B, Meta’s Llama 3 8B, and Google’s Gemma — have reached a level of capability that makes them viable for a wide range of enterprise use cases. Understanding when an SLM is sufficient and when a large language model (LLM) is necessary is one of the most important decisions in enterprise AI strategy. The development of language models is rooted in computer science, which provides the theoretical and technical foundation for advances in AI.

This guide provides a practical framework for making that decision.

Defining the Spectrum of Neural Networks

The distinction between small and large language models is not binary — it is a spectrum:

Category	Parameter Range	Examples	Typical Deployment
Small Language Models	1B – 13B	Phi-3 Mini (3.8B), Mistral 7B, Llama 3 8B, Gemma 7B	On-premises, edge, local GPU
Medium Language Models	13B – 70B	Llama 3 70B, Mixtral 8x7B, Qwen 72B	Private cloud, dedicated GPU clusters
Large Language Models	70B+ (or proprietary)	GPT-4, Claude, Gemini Ultra	Cloud API
Each step up the spectrum brings greater capability but also greater cost, latency, infrastructure requirements, and operational complexity.

Deploying and customizing language models often involves using programming languages such as Python, which are widely adopted in AI development.

Techniques Behind Language Models: Deep Learning and Neural Networks

Modern language models are built on the foundation of deep learning and neural networks, two pillars of artificial intelligence that have transformed how machines process and generate human language. These AI models are designed to understand, interpret, and generate natural language by learning from vast amounts of training data, enabling them to perform a wide range of language tasks with remarkable fluency.

At the core of these systems are artificial neural networks—computational structures inspired by the human brain. Much like the brain’s network of neurons, artificial neural networks consist of interconnected nodes arranged in layers. This deep architecture allows language models to capture intricate patterns, semantic meaning, and relationships within complex data, making them highly effective for natural language processing.

Deep learning models come in several forms, but two architectures stand out in the evolution of language models: recurrent neural networks (RNNs) and transformer models. RNNs are adept at handling sequential data, such as sentences or paragraphs, by maintaining context across sequences—an essential capability for understanding human language. However, the real breakthrough in generative AI came with transformer models, which use self-attention mechanisms to analyze the importance of each word in a sentence relative to others. This enables transformer-based language models to generate text that is contextually accurate and human-like, even for complex tasks such as language translation or summarization.

The training process for these AI models involves exposing them to massive datasets—ranging from books and articles to code samples and other forms of unstructured data. Through supervised learning, the models learn to predict the next word or phrase, gradually improving their ability to generate coherent and relevant responses. This process requires significant computational resources, as deep neural networks with billions of parameters must be optimized for both accuracy and efficiency.

Generative AI tools powered by these language models are now integral to enterprise AI applications. From virtual assistants and chatbots that handle customer queries, to AI agents that automate document processing or analyze user data, the versatility of these models is driving digital transformation across industries. Enterprises can further enhance model performance by fine-tuning language models for specific tasks, such as sentiment analysis, code generation, or solving math problems, ensuring that AI systems are tailored to their unique requirements.

Despite their capabilities, language models face ongoing challenges. Ensuring fairness and minimizing bias in model outputs is a priority for AI researchers, as models can inadvertently reflect biases present in their training data. Additionally, the energy consumption and infrastructure demands of training and deploying large-scale deep learning models are significant considerations for enterprises.

Looking ahead, the field of AI research is rapidly advancing, with a growing focus on multimodal models that can process and generate not just text, but also images, audio, and other data types. These innovations promise to unlock new generative AI capabilities, enabling even more sophisticated AI tools for enterprise use.

By leveraging the latest advances in deep learning, neural networks, and natural language processing, organizations can harness the full potential of artificial intelligence to automate complex tasks, generate human-like text, and drive meaningful business outcomes. As AI technologies continue to evolve, partnering with experienced consultants like McKenna Consultants ensures that your enterprise stays at the forefront of AI development and digital transformation.

Capability Trade-Offs

Where Large Language Models Excel

Large language models (LLMs) are foundational to many generative AI models, powering advanced applications across industries.

LLMs have clear advantages in tasks that require:

• Complex reasoning: Multi-step logical reasoning, mathematical proofs, and nuanced analysis of ambiguous scenarios.

• Broad knowledge: Questions spanning diverse domains where the model needs extensive training data, as seen in many generative AI models.

• Creative generation: Generative AI models, powered by large language models (LLMs), are capable of producing high-quality, varied, and contextually appropriate creative content.

• Instruction following: Large language models (LLMs) excel at generating responses to complex, multi-part instructions without fine-tuning.

• Multilingual capability: High-quality performance across many languages, particularly for less common languages.

Where Small Language Models Are Sufficient

SLMs can match or approach LLM performance for:

• Classification tasks: Categorising documents, emails, support tickets, or transactions into predefined categories.

• Extraction tasks: Pulling structured data from unstructured text — names, dates, amounts, product codes.

• Summarisation: Generating concise summaries of documents, meeting notes, or customer interactions.

• Narrow-domain Q&A: Answering questions within a specific, well-defined knowledge domain (when combined with RAG).

• Code completion: Suggesting code completions and snippets for common programming patterns.

• Sentiment analysis: Determining the sentiment of customer feedback, reviews, or social media posts.

Unlike traditional software, which must be explicitly programmed for each task, SLMs learn to perform tasks by training on relevant data.

The critical insight is that many enterprise AI use cases fall into the “sufficient” category. A customer support chatbot that classifies queries and retrieves relevant knowledge base articles does not need the reasoning capability of GPT-4. A document processing pipeline that extracts invoice data does not need broad world knowledge.

Cost Implications

The cost difference between SLMs and LLMs is not marginal — it is often 10x to 100x:

Deployment	Approximate Cost per Million Tokens
GPT-4 (cloud API)	£20 – £50
Claude Sonnet (cloud API)	£8 – £20
Llama 3 70B (self-hosted)	£2 – £5
Mistral 7B (self-hosted)	£0.20 – £0.50
Phi-3 Mini (self-hosted)	£0.10 – £0.30
For an enterprise processing millions of documents, emails, or transactions per month, the cost difference between running Phi-3 Mini on-premises and calling GPT-4 via API is substantial. This is not a theoretical concern — it directly impacts AI ROI.

The cost calculation also includes infrastructure. Self-hosted SLMs require GPU hardware (or cloud GPU instances), but a single NVIDIA A100 can serve a 7B model with high throughput. LLM-grade infrastructure requires multiple high-end GPUs and significantly more operational overhead.

Latency Requirements

For real-time applications — chatbots, autocomplete, in-application AI features — latency matters as much as accuracy. These applications require rapid processing of user inputs to maintain a seamless experience. SLMs have a fundamental advantage:

• A 7B parameter model running on a single GPU can generate tokens at 50-100+ tokens per second.

• A 70B+ model requires more computation per token, resulting in higher latency even on premium hardware.

• Cloud API calls to LLMs add network latency on top of generation time.

For applications where response time directly impacts user experience — such as an Office add-in that summarises a document while the user waits, or an ecommerce search that needs sub-second results — SLMs may provide a better user experience despite lower raw capability.

Data Privacy Considerations

Data privacy is often the decisive factor for enterprises in regulated industries — financial services, healthcare, legal, and government. The key question is: where does the data go?

Responsible data collection practices are essential for maintaining privacy and meeting regulatory requirements in enterprise AI deployments.

Cloud LLM APIs

When using GPT-4, Claude, or other cloud LLM APIs, the enterprise’s data is sent to the provider’s servers for processing. Even with contractual assurances about data handling, this creates a compliance surface:

• Data leaves the enterprise’s network perimeter.

• The processing location may be outside the enterprise’s jurisdiction (relevant for UK GDPR, data residency requirements).

• The enterprise must trust the provider’s security and data handling practices.

On-Premises SLM Deployment

SLMs are small enough to deploy on enterprise-owned hardware. This means:

• Data never leaves the enterprise’s network.

• Processing occurs within the enterprise’s jurisdiction.

• The enterprise has full control over the hardware, software, and data lifecycle.

For use cases involving personally identifiable information (PII), financial data, legal documents, or health records, on-premises SLM deployment may be the only viable approach.

Fine-Tuning vs Prompting

A key architectural decision is whether to use a model as-is with prompt engineering, or to fine-tune it on domain-specific data.

Incorporating human feedback during fine-tuning can help improve the accuracy and relevance of large language model outputs.

Prompting Large Models

LLMs are designed to be used with prompt engineering. Few-shot examples, system prompts, and retrieval-augmented generation (RAG) can steer a general-purpose LLM to perform well on specific tasks without any model modification.

Advantages:

• No training infrastructure required.

• Rapid iteration — change the prompt, get different behaviour.

• Access to the model’s full general knowledge.

Disadvantages:

• Ongoing per-query cost for API-based models.

• Less consistent output than a fine-tuned model.

• Prompt engineering can be fragile — small changes in prompt wording may significantly affect output quality.

LLMs are increasingly integrated into AI search solutions, powering features like contextual answers and AI-generated summaries that enhance user experience in enterprise and consumer applications.

Fine-Tuning Small Models

SLMs can be fine-tuned on domain-specific data to achieve performance that rivals or exceeds LLMs for specific tasks. Fine-tuning involves training the model on examples of the specific task you want it to perform.

Advantages:

• Dramatically lower per-query cost after fine-tuning.

• More consistent, predictable outputs for the target task.

• The model can learn domain-specific terminology, formats, and patterns.

• Fine-tuned SLMs can be tailored to analyze data from specific domains, such as sensor or IoT data, for targeted enterprise applications.

• Runs on-premises with full data privacy.

Disadvantages:

• Requires labelled training data (typically hundreds to thousands of examples).

• Fine-tuning infrastructure and expertise.

• The model loses some general capability — it becomes specialised.

• Model must be retrained when requirements change.

The Practical Trade-Off

The decision often comes down to volume. For low-volume use cases (a few hundred queries per day), prompting an LLM via API is simpler and cheaper than fine-tuning and hosting an SLM. For high-volume use cases (thousands or millions of queries per day), fine-tuning an SLM delivers dramatically better economics.

A Decision Framework

When evaluating small language models vs large language models for an enterprise use case, we recommend working through the following criteria. This framework is designed to help enterprises select the most suitable AI algorithms and models for their unique requirements, ensuring that the chosen solution aligns with business goals and technical constraints.

1. Task Complexity

• Does the task require multi-step reasoning? → LLM (LLMs are capable of exhibiting intelligent behavior when handling complex, multi-step logical tasks.)

• Is the task classification, extraction, or summarisation? → SLM likely sufficient

• Does the task require creative, varied output? → LLM

• Is the task narrow and well-defined? → SLM with fine-tuning

2. Data Privacy

• Does the data contain PII, financial, or health information? → On-premises SLM strongly preferred

• Is data residency within the UK required? → On-premises SLM or UK-hosted API

• Is the data non-sensitive? → Cloud LLM API acceptable

Deploying AI systems for data privacy can also minimize the need for human intervention in sensitive data handling processes, helping to maintain compliance and reduce the risk of human error.

3. Volume and Cost

• Fewer than 1,000 queries per day? → Cloud LLM API is simpler

• More than 10,000 queries per day? → SLM cost advantage becomes significant. SLMs can efficiently process large volumes of numerical data, making them ideal for data-intensive enterprise applications.

• More than 100,000 queries per day? → SLM is almost certainly the right choice

4. Latency

• Real-time user-facing application? → SLM or dedicated LLM instance

• Batch processing (reports, document analysis)? → LLM API is acceptable

• Edge or mobile deployment? → SLM only

Both SLMs and LLMs can also be leveraged for other tasks that require either real-time or batch processing, depending on enterprise needs.

5. Available Expertise

• Do you have ML engineering capability? → Fine-tuned SLM is viable

• Is your team primarily application developers? → Cloud LLM API with prompt engineering is more accessible

SLM Enterprise Use Cases

To make this concrete, here are enterprise use cases where small language models deliver excellent results:

• Email triage: Classifying incoming emails by department, urgency, and type. A fine-tuned 7B model handles this with >95% accuracy.

• Invoice processing: Extracting vendor name, invoice number, line items, and totals from PDF invoices.

• Customer feedback analysis: Categorising and summarising customer feedback across multiple channels.

• Internal knowledge search: Powering a RAG-based Q&A system over internal documentation using a locally hosted SLM.

• Code review assistance: Flagging potential issues in code changes, suggesting improvements, and checking against coding standards.

In addition to these, small language models (SLMs) can be applied to computer vision tasks such as defect detection and visual data analysis. Both SLMs and large language models (LLMs) are built on deep neural networks and artificial neural networks, which enable advanced pattern recognition across text and visual domains. Many modern language models are based on architectures called transformer models, with the transformer model serving as the foundation for generative AI. Generative AI applications, powered by these models, are transforming industries by automating content creation, data analysis, and more. Ultimately, the goal of artificial intelligence is to replicate aspects of human intelligence, enabling machines to solve complex problems and adapt to new challenges.

Getting the Decision Right

The choice between small language models and large language models is not a one-time decision — it is an architectural pattern that should be evaluated for each use case within your AI strategy. Many enterprises will use both: LLMs for complex, low-volume tasks and SLMs for high-volume, privacy-sensitive, or latency-critical applications.

McKenna Consultants is a UK-based AI consultancy that helps enterprises make these decisions with rigour. We evaluate your use cases, prototype with different model sizes, benchmark performance and cost, and recommend the architecture that delivers the best return on your AI investment.

If you are evaluating AI model deployment for your enterprise, contact us to discuss your requirements.