Building Scalable Software Products: The Role of Small and Large Language Models

Your roadmap for software development has possibly changed more in the last eighteen months compared to the previous five years. No doubt, the reason behind this is AI advancements like Agentic AI, machine learning (ML) algorithms, and language models. Today, users are looking for personalized features more than ever. As such, your organization is determined to build custom software using AI.

As per the Stack Overflow Developer Survey, 84 percent of developers are currently using AI in software engineering. This statistic clearly explains the growing significance of AI-powered software development. However, the real challenge for you as a CTO, CEO, or an IT decision-maker is:

How do we build a scalable software product using AI agents, ML, and language models – without compromising quality, exceeding budget, or extending the delivery timeline?

In simple terms, ML is the foundation of ‘language models’ that help AI agents to think, strategize, and perform specific tasks. This blog will help you explore the true potential of small and large language models and why they are the modular building blocks of modern-day software. We’ll walk you through their differences, key benefits, use cases, and practical ways for effective implementation. Let’s begin

Why SLMs and LLMs Have Become Central to Scalable Software Products

According to McKinsey’s State of AI survey, over two-thirds of business professionals state that their organizations are using AI in more than one function. Whereas, half of the other stakeholders disclose AI implementation across three or more business functions. One of the most important key drivers of this AI transformation is language models – SLM and LLM.  

For technology heads and product leaders looking for custom software product development, leveraging small and large language models has become critical. Why? Because they positively impact three critical business-focused KPIs:

• Customer experience and engagement

• Long-term business profit and ROI

• Faster time to market at scale  

That’s why a MarketsandMarkets report states that more organizations are planning to invest in language models. The report explains that the global SLM market is projected to reach a whopping $5.45 billion by 2032, with a CAGR of 28.7 percent during the forecast period 2025 - 2032.

On the other hand, Persistence Market Research predicts the global LLM market size is to be valued at $60.2 billion by 2032 with a 34.6 percent CAGR during the forecast period 2025 - 2032.

Instead of replacing human expertise, small and large language models act as an ally for reducing errors and automating complex workflows that delay innovation cycles. They help you ship faster, amplify engineering, streamline delivery pipelines, and improve decision-making capabilities. Some specific advantages to be taken into consideration include:

• Consistent coding by maintaining uniform coding standards that decrease review cycles.

• Accelerated software development with automated test creation, code recommendations, and debugging support.

• Faster feature delivery by reinforcing predictable system patterns.

• Better utilization of developer time and effort by unloading repetitive tasks impacting daily workflows.

SLMs vs LLMs: Deciding the Right One for Your Software

Whether you are a business owner looking to build software from scratch or an IT decision-maker seeking assistance to modernize your application, you need to start recognizing the impact SLM and LLM can make. You need to identify which one is better for my business use case – SLM or LLM? However, the answer is not that simple.

According to IBM, SLMs are smaller in scope and scale compared to LLMs. But they are more compact and efficient otherwise. Moreover, they need less computing power and memory. This makes SLM a plausible solution to ensure cost-effectiveness, better resource optimization, and modularity in implementing winning AI functionalities.

For instance, IBM’s Granite model consists of both SLMs and LLMs. However, the company experienced 3X – 23X lower costs for smaller models in comparison to larger models. The smaller models even outshone their competitors for certain crucial aspects.

Here are some use cases of small language models for software development to give you a better idea.

• Creating software with edge and on-device deployment capabilities without extending cloud server infrastructure and bandwidth.

• Delivering privacy-centric solutions by processing data locally to scale and support enterprise-grade applications.

• Making advanced AI models accessible and affordable for lean teams and emerging companies.

• Testing new features in controlled environments quickly to accelerate development and deployment cycles.

Unlike SLMs, which cater to task-specific functionalities, LLMs prove beneficial across the entire software development lifecycle. They enforce multiple forms of reasoning in a single AI model pertaining to natural language recognition, documentation, debugging, code synthesis, and architectural framework. For example, LLMs can fine-tune vague business requirements into exhaustive user stories, convert those into battle-tested code faster, identify atypical discrepancies, and suggest potential feature enhancements.

Gartner predicts that over 55 percent of software development teams will be actively engaged in the creation of LLM-based features by 2027. On the flip side, GitHub’s Copilot study states that developers using an LLM-driven assistant finished a coding task 55.8 percent faster.

For better understanding, here are some use cases of large language models for software development.

• Enabling faster development of complex features and functionalities with system-level reasoning and architecture planning.

• Transforming legacy applications and software with automated recommendations and migration best practices.

• Supporting strategic decision-making with accurate forecasting of critical risks, key efforts, and resource needs for future releases.

• Assisting with cross-functional collaboration by delivering solid documentation, API guidelines, and production artifacts.

When it comes to scalable software product, both small and large language models bring their own set of benefits and uniqueness to the table. The best way is to combine their capabilities to support daily tasks and resolve complex development challenges in the long run. This will help you achieve speed, scalability, and precision while eliminating overhead costs.

Scalable Software Development: Practical Ways to Get Started with SLMs and LLMs

Now that you know why you need small and large language models for software development in the first place, you need to understand how to leverage them to the fullest. Here are some practical techniques that will help you maximize product success by answering essential questions, such as:

• How to build scalable software products using small and large language models?

• What is the ROI of implementing SLM and LLM for software development?

• How to integrate SLMs and LLMs into existing enterprise software?

#1 Think ‘Value vs Complexity’ Before Capitalizing

Most organizations jump into experimental stages without understanding how a particular trial can impact their foundational ecosystem. A strategic and more smarter way is to identify 3 to 5 scenarios where language models can help you fast-track delivery, reduce overhead costs, and improve UX. For instance, test case creation, data processing, and risk analysis. You can then segregate each scenario by complexity and value to invest effectively.  

The Scalability Win: You will be able to align these pilots with business objectives and measurable outcomes, ensuring a secure budget and scale later.

#2 Prioritize the Use of SLMs at the Start

SLMs play a vital role in high-performance and predictable workflows. The idea to utilize their capabilities first aims at building a solid foundation for software development at scale. SLMs are faster to integrate and easier to govern. They act as your efficiency engine to reduce cloud infrastructure costs, improve business productivity, and mitigate latency.

The Scalability Win: When SLMs stabilize your operations, you can introduce LLMs as the top layer of intelligence without significant costs and complexities.

#3 Bring LLMs Where Decision and Context Matter Most

Large language models can be a go-to option when your teams require comprehensive reasoning capabilities. You need to focus on areas that influence business outcomes and ROI. These include user communication analysis, business need translation, code restructuring, infrastructure assistance, and decision support. This approach will enable faster release cycles and minimize escalations.

The Scalability Win: When LLMs are involved at high-value processes, they help you keep costs to a minimum while elevating system intelligence.

#4 Combine the Capabilities of SLMs and LLMs

You don’t need to use one model for every possible scenario. Instead, you can create a custom architecture by combining the capabilities of LLMs and SLMs. For instance:

SLM – You can use it for repetitive extraction and routing tasks.

LLM – You can use it for interpretation, summarization, and validation.

This creates a balance between the usability of both models while reducing loopholes, maintaining high reliability, and maximizing the use of available budget.

The Scalability Win: This hybrid approach can be applied to various software engineering situations and use cases.

#5 Introduce SLMs and LLMs as Modular Tools

Most business leaders try to replace the existing system entirely with AI. Concretely, this approach is viable only when the system is not meeting even the targeted objectives. However, it's best to integrate SLMs and LLMs as flexible tools that can be easily plugged into running applications. To put it simply, you will be adding new capabilities without disturbing core functionalities.  

The Scalability Win: With a modular approach. It becomes easier to switch between models, advance competence, and expand use cases.

#6 Focus on Governance and Cost Control from Day 1

You need to ensure that governance and cost control are part of your first deployment. The companies that successfully scale AI don’t prioritize the governance layer at later stages. They build on them from the beginning by focusing on key aspects, such as:

• Business KPIs aligned with measurable outcomes

• Budget estimation that suits selected use cases

• Guardrails that aimed at system safekeeping

• Output validation backed by clear insights

The Scalability Win: The day 1 governance approach helps you ensure enterprise-wide adoption of SLMs and LLMs without any security drawbacks and audit issues.

Time to Collaborate with Specialized AI Software Development Partner

You can take full advantage of the aforementioned best practices, whether you want to build a scalable software product from scratch or modernize legacy applications. You just need to collaborate with a leading software development partner for LLM and SLM integration. That’s where Clarion Technologies can help.

Clarion is a trusted AI software development company specializing in language-model engineering. We deliver custom Agentic AI services and Generative AI solutions to help you build faster and scale better. Our AI industry specialists have hands-on expertise in addressing key pain points and catering to diverse use cases across healthcare, finance, retail, real estate, and other industry sectors. Reach out to technology experts at Clarion to get started right away.