Businesses’ rush to adopt generative AI has followed a pattern familiar to those of us who have been around the tech industry long enough: you invest heavily in expensive infrastructure (in this case, GPUs to power large language models), then hope the benefits justify the costs.
But with individual GPUs now commanding prices well into six figures—and cloud computing costs rising in lockstep—something’s gotta give. Pressure is mounting to show ROI on AI investments, with companies also eyeing the enormous energy consumption and environmental impact of training and running these massive models.
I recently spoke with a CIO who exemplified the challenge here. His mid-sized company had done everything “by the book” in their generative AI rollout—investing in GPU infrastructure ($200,000 just to start), managing escalating cloud costs that are hitting five figures monthly, and hiring a team to run it all. “The technology is transformative,” he told me, “but the current infrastructure model probably isn’t going to be sustainable.” He’s right—while this approach might make sense for tech giants, most will need a more practical path forward.
A *smarter* path forward
Enter small language models (SLMs), which flips the “bigger is better” AI narrative on its head. While traditional (can we call them traditional already?) LLMs operate like sledgehammers, requiring massive GPU farms in the cloud and processing trillions of calculations, an SLM is a precision instrument. They can deliver comparable results by focusing specifically on what businesses actually need.
Where traditional AI models might process a trillion parameters, an SLM can achieve comparable results with less than 1% of that computational power. That can cut AI training times from months to weeks, deliver faster day-to-day performance, and significantly lower infrastructure costs.
The reduction in computing requirements isn’t just about cost savings. It also makes AI actually sustainable for business use cases. Companies can finally move past the endless cycle of infrastructure investments and start focusing on getting real value from their AI initiatives. With SLMs, businesses can bring their AI budgets back to earth while getting better results for their specific needs.
Companies implementing SLMs are already seeing tangible benefits. A mid-market retailer recently deployed an SLM for customer support that reduced response times by 40% while cutting computing costs in half compared to their previous cloud-based solution. Similarly, a manufacturing firm used domain-specific SLMs to optimize their supply chain, resulting in 15% efficiency gains without the privacy concerns that had previously stalled their AI adoption.
Keep your AI advantage private
The catch with using cloud-based LLMs from major vendors is that you have to share your sensitive business data—which they then use to train their models. While you’re leveraging an AI system informed by your best data, so are your competitors. The result is general outputs that can’t really reflect your unique business edge, not to mention questions about data security and compliance risks.
Consider customer service applications, internal knowledge bases, or product development insights. These are all areas where your company’s unique data and processes create competitive advantage. When this sensitive information feeds into public LLMs, you’re essentially diluting your edge while taking on unnecessary risks.
SLMs are different. Because they don’t require massive infrastructure investments, businesses of any size can run them securely behind their own firewalls. This private approach dramatically reduces cybersecurity risks while keeping your data in-house.
Additionally, when you fine-tune an SLM with your company’s specific data and use cases, the results are uniquely yours. This ensures your proprietary insights drive innovation for your business alone, while simplifying compliance and data licensing requirements compared to more complex cloud solutions. As we move into an era where AI capabilities become table stakes for business competitiveness, the ability to deploy these technologies efficiently and securely will separate winners from the pack.
Right-size your AI strategy
One misconception I hear that is worth making clear: small language models aren’t about compromise—they’re about making smarter choices. SLMs can handle many business needs while offering relief from the crushing costs and complexities of larger models.
Think of it as right-sizing your AI approach. Start with SLMs where they make sense, and scale up only when specific use cases demand more computational power. The key is building an AI strategy that’s sustainable and practical, one that delivers real results without unnecessary overhead. That’s not just smart business—it’s a competitive advantage.
As AI technology continues to evolve, those who find the balance between capability and practicality will be best positioned to leverage these powerful tools without breaking the bank or compromising security. SLMs represent that balance for many organizations ready to move beyond the hype and into sustainable AI implementation.
