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Small Language Models for enterprise AI: Challenges, benefits, and deployment strategies

Big AI promises often come with big problems. Costs are high. Data privacy is uncertain. And many tools are too general to solve specific business needs​.

Small Language Models (SLMs) may be a good alternative to Large Language Models (LLMs). They cost less, keep data safe, and work well for focused tasks.

In this article, we’ll explore how SLMs help overcome the challenges of adopting AI.

Table of contents

Adoption of GenAI in business

In 2024, more businesses are using Generative AI, but adoption is uneven. According to a McKinsey survey, 65% of companies regularly use GenAI tools. However, only 8% have integrated them into more than five functions​.

Major LLM providers in 2024 leading enterprise adoption efforts
Major LLM providers in 2024 leading enterprise adoption efforts

This gap exists because adopting GenAI at scale is hard. Many companies struggle to move beyond experiments.

What are the challenges they face? Mainly:

  • High costs
  • Privacy concerns
  • Tools that don’t meet specific needs​

Instead of (just) transforming workflows, GenAI often adds another layer of complexity. Businesses face more work adapting AI to their systems while managing security and compliance.

Why GenAI adoption is a challenge

Despite the excitement around Generative AI, businesses face several barriers to widespread adoption. Many report challenges that block full adoption, even after initial experimentation.

These challenges include:

  • Experimentation > Integration. Most projects remain GenAI proof of concept.  They struggle to scale these solutions into everyday processes.
  • High costs. Many tools rely on cloud-based LLMs. The problem is they require expensive computing power and incur recurring API costs. For many tasks, the cost outweighs the benefits.
  • Privacy. Using third-party GenAI tools raises questions about where sensitive data goes. Businesses in regulated industries face extra risks due to strict regulations.
  • Performance. General-purpose AI models struggle to meet the demands of industry-specific or task-specific workflows​.

These barriers prevent companies from realizing the full potential of GenAI. It leaves many stuck in the exploration phase instead of moving forward.

At the heart of GenAI adoption challenges are the limitations of Large Language Models. Despite their power and popularity, LLMs present specific issues that complicate their use.

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Challenges of Large Language Models

At the heart of GenAI adoption challenges are the limitations of Large Language Models. Despite their power and popularity, LLMs present specific issues that complicate their use.

Data privacy risks

Many LLMs are cloud-based, meaning your data is processed externally. It raises serious privacy concerns.

  • In 2023, Samsung banned employees from using ChatGPT after a data breach exposed internal source code.
  • Regulations like GDPR, HIPAA, and the new EU AI Act require strict control over sensitive data. Cloud-based solutions make compliance more difficult​.

These concerns are not hypothetical. Companies fear legal penalties, reputational damage, and loss of sensitive information.

High costs

LLMs may be expensive. Costs include:

  • API fees: Businesses pay for every token processed—both input and output.
  • Training costs: Fine-tuning models for specific tasks requires massive datasets and computing power.
  • Infrastructure: Self-hosting LLMs to avoid privacy risks requires advanced hardware, which adds to operational costs.

For companies working in cost-sensitive environments, these expenses can be a deciding factor.

Generalized, not specialized

LLMs are trained on massive datasets from the internet. While this makes them universal, it also makes them useless for specific industries.

  • These datasets include content from sources like PubMed, GitHub, StackExchange, and Wikipedia.
  • Broad datasets lack the depth required for context-heavy or industry-specific tasks. What’s more, relying on publicly available data can amplify existing biases, reducing the accuracy and reliability of outputs.
  • For example, legal teams need tools that understand industry-specific jargon and context. LLMs often require extensive fine-tuning* to meet these demands.

*  Even with fine-tuning, results are not guaranteed, making it hard to justify the investment.

Composition of the pile by category
Source: PAPERS WITH CODE | The Pile Dataset

The plot above is an overview of the Pile dataset used to train GPT-Neo-X, one of the first GPT models created in the past. While the dataset contains some medical data (PubMed), coding stuff (GitHub), legal (FreeLaw), and Wikipedia or even subtitles, it’s only just a general overview of a given category.

The real expertise is being fed on during the later steps of training a model. However the bigger the model, the greater the dataset that’s needed to do so correctly.

Dependency on the provider

LLMs are often tied to major cloud providers like OpenAI, Google, or Microsoft. Businesses rely on these providers to maintain access. But this comes with risks:

  • If the provider experiences outages, operations are disrupted.
  • Providers can change pricing or remove models, forcing businesses to adjust their workflows.
  • Companies cannot fully customize or adapt cloud-based models.

This dependency creates uncertainty and limits flexibility.

Resource and energy demands

LLMs require significant hardware and energy to operate, especially for self-hosting. This leads to other problems:

  • Running large models consumes a lot of power, increasing costs.
  • Companies aiming for sustainability face challenges balancing AI innovation with carbon reduction goals.
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SLMs: A growing trend in AI

Businesses seek alternatives to traditional Large Language Models. That’s why the adoption of Small Language Models is accelerating.

For a moment, let’s go back to 2023 when Gartner released the report. They predicted the rise of SLMs – or light LLMs as they’re called on the plot.

Source: GARTNER | Understand and exploit GenAI with Gartner’s New Impact Radar

And here we are, in 2024 where SLMs are a thing to consider while thinking about GenAI solutions. And yes, it was only a year ago when Gartner stated this theory. However, in the AI world, 1 year is like a decade in the world of the living.

Small Language Models bridge the gap between high-performance AI systems and the need for secure, cost-efficient solutions.

Flexibility of Small Language Models

Small Language Models are not only cost-effective but also highly adaptable. They are able to deliver quality results with minimal resource requirements. This makes them an attractive choice for businesses.

“SLMs provide a balance of high quality and low cost, making them an efficient choice for focused applications compared to traditional LLMs.”

Source: OCTOAI | In Defense of the Small Language Model

Take a look at the plot comparing Llama 8B with the state-of-the-art model being GPT-4o. Straight out of the box, GPT beats the small model by over 20% in terms of quality which is the outcome we’ve all expected. 

However, after enhancing our small Llama model, it turns out that it can go toe-to-toe with the LLM and outperform it in this task while retaining the lower usage cost per request. The difference is so big that it cannot go unnoticed.

The comparison between Llama 3.1-BB and GPT-4o demonstrates the strengths of SLMs in terms of both cost and performance:

  • SLMs operate at a fraction of the cost compared to LLMs. In zero-shot setups, Llama 3.1-BB incurs significantly lower costs than GPT-4o.
  • With minimal additional training, SLMs can reach quality levels close to or even surpass LLMs in specific tasks. These tasks may include customer support or legal research.
  • Llama 3.1-BB achieves over 96% task quality in a fine-tuned setup. As a result, proving its capability for high-accuracy outputs in focused use cases.

Why Small Language Models for businesses?

  • SLMs require fewer resources per inference, reducing expenses for large-scale deployments.
  • With fine-tuning, SLMs can be optimized for niche applications, delivering better results for specialized tasks.
  • Lower costs also translate to reduced energy consumption, making SLMs a more sustainable option.

Closing the performance gap

One of the main arguments against SLM is its perceived inferior performance compared to LLM. However, recent advancements show that this gap is rapidly shrinking.

The performance gap between SLMs and LLMs has shrunk from 20% to 2% in recent years, proving SLMs’ potential in enterprise applications.

Talking about LLMs and SLMs it’s hard not to mention open and closed models. While closed models are usually ones behind some kind of paid access mechanism (like paid API in OpenAI for developer use), open models can be downloaded on your device and used forever. You can play with them, and tweak them to your needs. 

Looking at the plot, red dots are closed models while green ones are open ones. When open models emerged beginning of 2023, the performance gap was as wide as over 20% in the MMLU benchmark shown. However fast forward to today, the gap is as wide as a few percent. This shows that open models, as well as small models, are on the rise and have to be taken into consideration while experimenting.

What’s the point? With comparable performance, SLMs become a practical choice for businesses looking for secure and cost-effective AI solutions.

This shift in performance highlights the growing viability of SLMs as a competitive option in the enterprise AI landscape.

SLMs vs. LLMs: Key differences

Understanding the differences between SLMs and LLMs is essential for businesses evaluating AI solutions. While LLMs have dominated the AI landscape, SLMs are designed to address many of their limitations.

Large Language ModelSmall Language Model
> 12B parameters< 12B parameters
Need extensive computing capabilitiesCan fit on a single GPU
Trained on broad datasetsExpert in given matter
Usually cloud-basedUsually on-premises

Benefits of Small Language Models (SLMs)

Small models for big problems. SLMs address many of the issues businesses face with LLM adoption.

Data privacy and compliance

SLMs are usually deployed on-premises, giving companies complete control over their data. SLMs ensure sensitive information stays within the organization. This is particularly important for industries that operate under strict regulations like GDPR, HIPAA, or the EU AI Act.

Cost efficiency

One of the biggest advantages of Small Language Models is their ability to reduce costs while maintaining performance. This is especially evident in scenarios like synthetic data generation.

Recently, our R&D had a case where we needed to generate 1 million synthetic data samples to train an email classification tool for further client use. We did a comparison and decided to utilize SLM for this purpose. Why?

Synthetic data generation
1M samples
Input / Output tokens
550M / 700M
ModelGPT-4oOn-premise SLM (1 GPU)
$ Input tokens (OpenAI)$1 375--
$ Output tokens (OpenAI)$7 000--
Total cost$8 375--
Generation time56 days70 days
  • This table shows a cost comparison between using a single-GPU SLM and GPT-4o, one of the most popular large models in the world.
  • As you can see, by choosing the SLM, we save over $8,000 on token processing, with only a slightly longer generation time.
  • We didn’t factor in the SLM infrastructure costs here, as the setup was already in our facility, but this is worth mention
  • With SLM under our control, we could further optimize the generation time by adding multithreading for instance to lower it to just a couple of days.

SLMs are designed to be lightweight and resource-friendly. They require fewer computational resources, which translates to:

  • Lower infrastructure costs
  • No token fees
  • Energy savings

Tailored to specific needs

SLMs excel at handling specialized tasks. Unlike LLMs, which are trained on vast, generalized datasets, SLMs can be fine-tuned with minimal data. This makes them ideal for industries requiring precise, context-aware outputs.

Independence from providers

SLMs offer full control over the model, from deployment to updates. Once downloaded and implemented, companies are not tied to external vendors. This eliminates risks like:

  • Unexpected downtime or service interruptions.
  • Forced migrations when a cloud provider deprecates a model.
  • Rising costs due to pricing changes.

Optimized performance-to-size ratio

SLMs are smaller in size but highly efficient. They provide fast and reliable performance for targeted tasks without the computational overhead of LLMs. This makes them ideal for businesses looking for scalable AI solutions.

Small Language Models (SLMs) case studies

Small Language Models (SLMs) are already transforming industries by addressing specific business challenges. Below are some examples of how SLMs provide targeted solutions in various sectors.

SLM in healthcare: Secure patient data and faster responses

Epic Systems, a U.S.-based healthcare provider, integrated SLMs to improve patient support while ensuring HIPAA compliance. By deploying SLMs on-premises, they kept patient data secure and achieved faster response times to inquiries​.

This workflow is just a high-level presentation of how the architecture of such system could look like.

  • Epic has integrated Microsoft’s PHI-3 small model into their workflows.
  • Their priority was ensuring all sensitive patient data remained internal
  • With SLM deployment, they achieved faster response times to patient inquiries while maintaining data privacy.
  • Epic management claims that they’ve chosen a small model since it has robust yet efficient reasoning capabilities 

SLMs operate within internal systems, making them ideal for handling sensitive information without external dependencies.

SLMs are well-suited for industries requiring precision and context-specific knowledge.

  • Legal teams could fine-tune an SLM with case law or contract templates. The model could then assist with tasks like clause extraction, compliance checks, or document summarization.

A Polish legal research tool used an SLM fine-tuned on legal texts. It matched or even outperformed LLMs in narrow legal applications. The model provided secure, cost-effective insights while maintaining high performance​.

 Benchmark 1Benchmark 2
 LLM: gpt-4SLM: bielik-11b LLM: mixtral-8x7bSLM: bielik-11b
Precision1.000.990.640.93
Recall0.810.910.980.96
F10.890.950.790.94

SLM in automating ticket management in Customer Service

SLMs simplify repetitive customer service tasks while improving efficiency.

  • A company could implement an SLM to analyze incoming support tickets, categorize them by urgency, and generate draft responses for routine issues. This reduces the workload for human agents and speeds up response times.

SLM in internal knowledge management / AI knowledge bot

SLMs can help businesses improve knowledge retrieval and organization.

  • An internal AI assistant could allow employees to query the company’s knowledge base for policy updates, training materials, or product documentation. The SLM could combine text and visual search capabilities for comprehensive support.
Source: Internal use case

The schema you can see is an implementation for one of our clients (in progress), who required on-premise processing. In the diagram, you can see what the simplified chat architecture looks like. What is important, it’s fully modular, using different models for text and visual data, along with advanced RAG for knowledge-based responses.

How it works?

  • The system uses a Retrieval-Augmented Generation (RAG) framework to retrieve and process information from a knowledge base.
  • Separate SLMs handle textual and visual data, ensuring optimized performance for each type of input.
  • Lightweight and modular design, allows on-premise deployment

“SLM-driven knowledge bots streamline information retrieval and user interactions while ensuring data security through on-premise deployment.

Workflow overview

  1. User query: A user inputs a question or request through the chatbot interface.
  2. Knowledge retrieval: The RAG framework searches the database for relevant information.
  3. Decision point:
    • Found? The system generates and structures a response or saves unanswered questions for manual processing.
    • Not found? The system uses safeguards to prevent hallucinations, maintaining the quality and reliability of the outputs.
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SLMs as specialized task agents

Small Language Models (SLMs) shine when deployed as specialized task agents. Their modular nature allows them to tackle specific jobs efficiently. It makes them ideal for enterprise environments where precision and adaptability are key.

Since SLMs are lightweight, instead of one model we can set up multiple agents that are used based on the task at hand. This diagram illustrates how this could be done in an organization

  • Users interact with an AI tool through a unified interface, unaware that their requests pass through a routing layer
  • Here’s where the real magic happens – tasks are dynamically directed to the most appropriate SLM, whether it’s for text or speech analysis, vision AI, or something else.

This approach allows you to stay model-agnostic, meaning you can choose model also from different providers based on the user needs and pick the best one for a given task. 

  • This ‘mixture of agents’ setup also balances performance and saves on infrastructure costs as we only employ the model as needed, not running them in parallel. 

Advantages of the multi-agents approach

  • Each SLM is fine-tuned for its specific role, ensuring high accuracy and efficiency.
  • Multiple SLMs can be deployed and orchestrated simultaneously, enhancing overall system performance.
  • The system remains modular, allowing new models to be integrated as needs evolve.

This architecture demonstrates how SLMs can work together to streamline operations and handle diverse tasks.

How to deploy Small Language Models (SLMs)

Adopting Small Language Models can feel complex, but breaking it into clear steps simplifies the process. Here’s a roadmap to help you implement SLMs effectively, from exploration to full-scale integration.

Step 1: Start small – Explore SLM potential

Begin by testing whether an SLM fits your needs. This phase requires minimal investment and resources.

  • Hardware: Use a basic PC or laptop with 16GB+ RAM. A consumer-grade GPU can speed things up.
  • Software: Try open-source tools like Hugging Face or LM Studio for experimentation.
  • Action plan:
    1. Choose a simple business case, such as automating FAQ responses.
    2. Test open, pre-trained SLMs locally to see if they meet your needs.
    3. Adjust and refine outputs with lightweight prompting techniques​​.

For example, a small e-commerce company can test an SLM to categorize customer emails automatically. This reduces manual effort and provides quick insights.

Step 2: Proof of Concept (PoC) – Validate effectiveness

Once you identify a potential use case, move to a Proof of Concept (PoC). This phase tests SLMs in real-world scenarios.

  • Hardware: Upgrade to a workstation with an enterprise-grade GPU, such as NVIDIA A6000.
  • Software: Use tools like Docker or LangChain for fine-tuning and deployment.
  • Action Plan:
    1. Gather a small dataset relevant to your business.
    2. Fine-tune the SLM to specialize in the task.
    3. Collect feedback from test users to measure effectiveness and identify areas for improvement​.

For instance, a legal firm could use this phase to train an SLM on specific legal texts, ensuring it delivers accurate and relevant outputs for contract analysis.

Step 3: Scale up – Full integration

If the PoC is successful, scale the SLM across your organization. This phase involves integrating the model into daily operations.

  • Hardware: Invest in high-performance infrastructure, such as scalable servers with multiple GPUs (e.g., NVIDIA A100).
  • Software: Use custom solutions to integrate the SLM with your existing systems.
  • Action Plan:
    1. Optimize performance with advanced techniques like model routing or load balancing.
    2. Train employees to use the system effectively.
    3. Monitor performance and make adjustments as needed​.

For example, a bank could deploy an SLM across multiple branches to automate loan application processing, improving efficiency and customer satisfaction.

Key considerations during deployment

  • Ensure the SLM aligns with industry regulations, especially in sectors like healthcare and finance.
  • Track expenses during scaling to avoid overspending on infrastructure.
  • Plan for periodic updates to keep the SLM effective and relevant​.

SLM deployment: Resource overview

Deploying Small Language Models (SLMs) can be broken into three stages. Each stage reflects the level of resources and infrastructure needed based on the scale and complexity of the deployment.

 ENTRY STEPMID STEPFINAL STEP
SOFTWARE- LM StudioOllamaAnything
- LLMGoogle Colab
- Docker
- Vector databases (e.g. ChromaDB, Weaviate)
- Classic databases (e.g., PostgreSQL, MongoDB) 
- Usually custom solutions to integrate with existing systems
HARDWARE- Basic PC (16GB+ RAM)
- Optionally GPU for faster processing (e.g. NVIDIA RTX 30xx, 40xx)
- Workstation with GPU (e.g., NVIDIA L40s, A6000) Custom for production-grade purposes
FRAMEWORK- Hugging Face Transformers & Datasets libraries - LangChain / Llama-Index
FE + BE framework
- vLLM / sglang to serve a model
- Custom for production-grade purposes
TECHNIQUES x- Simple RAG
- Basic prompting techniques like zero-shot prompting
- Advanced prompting (chain prompting, ReAct etc.)
- Load balancing, model routing
- Mixture of agents
- Multimodal / multilingual 

1. Entry step: Exploring possibilities

Ideal for testing and small-scale experiments.

  • Software: Tools like LM Studio, Ollama, and Google Colab make it easy to get started.
  • Hardware: A basic PC with 16GB+ RAM is sufficient. A consumer GPU (e.g., NVIDIA RTX 30xx) can improve performance.
  • Framework: Hugging Face Transformers and dataset libraries offer ready-to-use tools for experimentation.
  • Techniques: Start with simple techniques like basic retrieval-augmented generation (RAG) and zero-shot prompting.

2. Mid step: Scaling up

Perfect for Proof of Concept (PoC) or departmental-level deployment.

  • Software: Integrate databases like Weaviate for vector storage or PostgreSQL for structured data.
  • Hardware: Use workstations with GPUs like NVIDIA L40s or A6000 to handle larger datasets and faster processing.
  • Framework: Advanced frameworks like LangChain or Llama-Index enable seamless integration with backend systems.
  • Techniques: Explore fine-tuning, more advanced prompting techniques, and optimized RAG implementations.

3. Final step: Full-scale deployment

Designed for enterprise-wide deployment with production-level reliability.

  • Software: Custom-built solutions to integrate with legacy systems and existing workflows.
  • Hardware: Scalable servers with high-performance GPUs, such as NVIDIA A100 or GH200, for heavy workloads.
  • Framework: Build custom frameworks tailored for production, ensuring stability and scalability.
  • Techniques: Utilize advanced methods like chain prompting, load balancing, model routing, and multimodal/multilingual capabilities.

Best practices for deploying Small Language Models (SLMs)

Deploying Small Language Models (SLMs) successfully requires thoughtful planning and execution. While they address many challenges posed by larger models, businesses still need a structured approach to maximize their benefits. Below are practical tips and best practices for implementing SLMs.

Identify clear use cases

Before implementing an SLM, define the problem it will solve. Focus on narrow, repetitive, or domain-specific tasks where SLMs excel.

Examples:

  • Automating customer service queries.
  • Extracting insights from industry-specific reports.
  • Categorizing incoming data, such as emails or tickets.

A clearly defined use case ensures the SLM meets specific business goals without unnecessary complexity.

Start with open models

Use pre-trained, open-source models during the exploration phase. These models are cost-effective and allow for quick testing.

How to begin:

  • Choose an open SLM like those available through platforms such as Hugging Face.
  • Test on small datasets that reflect the intended use case.

Open models provide a low-risk way to evaluate SLM capabilities without major investments.

Prioritize data preparation

The quality of data is critical for SLM performance. Ensure your datasets are clean, relevant, and well-structured before training or fine-tuning.

Best practices:

  • Use domain-specific data to make the model task-relevant.
  • Avoid sensitive or regulated data unless deployed on-premises.

High-quality data ensures accurate outputs and reduces training iterations.

Test on a small scale first

Start with a Proof of Concept (PoC) to evaluate the model’s performance under real-world conditions.

Steps:

  • Deploy the SLM on a small set of tasks or departments.
  • Gather feedback from users and refine the model as needed.

Testing on a small scale minimizes risk and provides insights for scaling up.

Optimize for performance and cost

SLMs are resource-efficient, but optimization can further enhance their performance.

Techniques:

  • Use advanced prompting strategies, such as chain or zero-shot prompting, to improve accuracy.
  • Employ load balancing and model routing for seamless integration into workflows.

Optimization ensures the SLM delivers high performance while keeping costs low.

Plan for scalability

If the PoC is successful, develop a roadmap for scaling the SLM across the organization.

Key considerations:

  • Integrate the model with existing systems, such as customer management platforms or knowledge bases.
  • Train staff on how to use the system effectively.

Scalability ensures the SLM adds value across multiple departments or functions.

Ensure compliance and security

Compliance and data security must be top priorities for regulated industries.

Steps:

  • Deploy SLMs on-premises to ensure sensitive data stays within your infrastructure.
  • Regularly audit the model to ensure it complies with industry regulations.

This avoids legal risks and protects sensitive business information.

Common SLM deployment pitfalls to avoid

  • Overloading the model: Focus on one or two tasks per SLM instead of trying to make it a general-purpose tool.
  • Skipping feedback: Regular input from end-users helps refine the model and improve its effectiveness.
  • Ignoring long-term maintenance: Plan for updates and retraining to keep the SLM relevant and effective.

SLMs offer flexibility and efficiency, but their success depends on a clear strategy and careful execution. By following these best practices, businesses can unlock the full potential of these models. 

Conclusion

Small Language Models (SLMs) offer a practical and efficient alternative to Large Language Models (LLMs). They address the common challenges businesses face when adopting AI tools, providing a pathway for organizations to integrate AI into their operations securely and cost-effectively.

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Justyna Gdowik

Justyna is a Content Specialist at Deviniti, where she combines her expertise in SEO optimization with a passion for captivating writing. Her aim is to convert complex technical concepts into reader-friendly language, ensuring accessibility for a diverse readership. Privately, she seeks out new adventures and culinary experiences, continuously feeding her curiosity and enriching her perspective.

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