Agentic AI is no longer experimental it is actively transforming enterprise workflows. But as organizations rush toward autonomous systems, many are discovering a painful truth: bigger AI models don’t always mean better outcomes.
One of our clients learned this the hard way. Their AI infrastructure costs were skyrocketing, even though they weren’t a technology-first company.
After an in-depth analysis, we discovered the root cause of their chatbot was powered by a large language model (LLM) answering extremely basic customer questions like “When are you open?” or “What’s your return policy?”
They were overspending on intelligence they didn’t need.
This is where the conversation around Small Language Models (SLMs) vs Large Language Models (LLMs) becomes critical especially in Agentic AI development, where performance, cost, and speed directly impact business value.
Why Agentic AI Development Needs a Smarter Model Strategy
Agentic systems are autonomous AI agents that plan, decide, and act across workflows in real time. These agents are expected to be fast, reliable, and cost-efficient qualities that don’t always align with massive LLMs.
While LLMs are powerful, they’re often overkill for predictable, domain-specific tasks. This is where SLMs shine.
Small Language Models are trained on focused datasets to perform niche tasks with precision. In many agentic workflows, especially enterprise automation, SLMs can handle up to 80% of interactions at a fraction of the cost, making them a strong contender for the best technology for agentic AI in structured environments.
What Makes SLMs Ideal for Agentic AI?
SLMs are gaining rapid adoption among enterprises and every modern Agentic AI development company for several reasons:
Lower computational requirements: Fewer parameters mean less memory and compute power.
Cost efficiency: Reduced energy and infrastructure costs.
Edge deployment readiness: Can run offline on mobile devices, IoT systems, or internal servers.
Domain specialization: Easy to fine-tune for specific business use cases.
Low latency: Faster responses critical for real-time decision-making in autonomous agents.
These features make SLMs especially effective in agentic systems where speed and reliability matter more than broad general knowledge.
Read detailed comparison on SLMs and LLMs on our main blog SLM vs LLM: Which one Powers Agentic AI Better?
Case Study: How Swiggy Uses SLMs for Intelligent Search
Swiggy offers one of the best real-world examples of SLMs powering agentic workflows.
The Challenge - With millions of menu items and diverse customer intents, Swiggy needed more than keyword-based search. Users might search for “healthy juice options to drink in summer” and expect highly contextual results.
The Solution - Swiggy adopted a two-stage fine-tuning approach using Small Language Models:
- Unsupervised fine-tuning: The model learns from real customer behavior. For example, when a query leads to an order, the model automatically categorizes intent and dish relevance.
- Supervised fine-tuning: Curated query-item pairs train the model to distinguish relevant and irrelevant results, such as:
- _Relevant: Fried Rice → Noodles
- Irrelevant: Fried Rice → Dosa_
The Result
A dataset with over 5,000+ samples spanning cuisines, dietary preferences, and generic search patterns powered by an SLM capable of delivering fast, accurate results at scale.
3 Workflow Problems SLMs Solve Best
1. Real-Time Responses: SLMs operate without heavy cloud dependency, ensuring near-instant decisions crucial for autonomous agents.
2. Customization & Compliance: Enterprises gain full control over training data, making SLMs ideal for regulated industries like healthcare and finance.
3. Edge & On-Device Deployment: Their compact size allows easy scaling across devices, apps, and distributed systems.
When to Choose SLMs vs LLMs for Agentic AI
Choose SLMs when:
- Low latency is critical (voice assistants, autocomplete)
- Data must stay on-device or behind firewalls
- Deploying on edge or low-power environments
- Tasks are highly specialized or repetitive
Choose LLMs when:
- Long-range reasoning and creativity are required
- Agents operate across multiple domains
- Open-ended conversations or complex language generation are essential
In reality, the best models for agentic AI often involve a hybrid approach using SLMs for execution and LLMs for orchestration.
The Future of Agentic AI Infrastructure
The industry is slowly moving away from the “bigger is better” mindset. For many businesses, SLMs deliver 80–90% of the performance at significantly lower cost and latency.
If you’re building autonomous systems, chatbots, or internal agents, selecting the right model is critical. A trusted Agentic AI development company can help you design, deploy, and optimize the right architecture.
At Infutrix, our Agentic AI development services help enterprises choose the best technology for agentic AI balancing performance, cost, and scalability to drive real-world impact.
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