Entrepreneurship with Frugal AI: Low-Cost Innovation for SMEs

Algorithms that are lightweight and accessible, allowing SMEs and entrepreneurs to apply artificial intelligence without relying on large models or expensive infrastructure.

When talking about artificial intelligence, the conversation often revolves around giants like ChatGPT, Gemini or Claude—models with billions of parameters that require powerful data centers and budgets only accessible to large corporations. However, there is a less visible but equally transformative side: frugal AI.

This concept describes lightweight algorithms trained with small datasets and capable of running on affordable hardware. It offers a pathway for SMEs and entrepreneurs to apply AI in sectors such as retail, agriculture or manufacturing without needing prohibitive infrastructure. In a context where 99% of the European business ecosystem is made up of small and medium-sized enterprises, the potential is enormous.

In this sense, the OECD has emphasized the importance of promoting “inclusive AI” that reaches all business levels, not only large multinationals. And 2025 marks a turning point in Europe: with the entry into force of the AI Act, the European Commission aims not only to limit risks but also to generate trust and encourage SME access to these technologies.

From the Cloud to Frugality: Another Face of AI

To understand the relevance of frugal AI, it is useful to compare it with other approaches. While edge AI focuses on running algorithms on local devices—sensors, industrial controllers, gateways—frugal AI is about doing more with less: fewer data, fewer parameters, lower computational cost.

The parallel with frugal innovation in healthcare or engineering is clear. Just as low-cost medical devices designed in India have enabled basic diagnostics in rural areas without large hospitals, frugal AI seeks to extend artificial intelligence to those who cannot afford to train or deploy large models.

The global market for lightweight AI in embedded devices is estimated to grow at an annual rate close to 20% through 2030, driven by demand for accessible solutions for SMEs. The logic is clear: if the cloud represents unlimited power, frugality represents engineering for efficiency—solutions capable of solving specific problems with very few resources.

Practical Use Cases

One of the most widespread examples of frugal AI is chatbots trained with internal FAQs. In Spain, several SaaS startups offer platforms that allow SMEs to upload manuals, common emails or technical documents to train a lightweight bot to answer customer or employee questions. Compared to costly licenses for general-purpose models, this alternative runs on in-house servers or even standard laptops.

In light manufacturing or workshops, low-cost computer vision is another promising application. With standard cameras and compact machine-learning models, SMEs can detect basic defects in parts, verify component presence or measure tolerances without large inspection systems. Meanwhile, recent research in agriculture has shown that computer-vision models can be trained with very small datasets—just a few hundred images—to detect crop diseases, suggesting similar logic for industrial quality control.

Precision agriculture offers perhaps the clearest examples of applied frugality. In Argentina, the startup Kilimo has developed algorithms that optimize irrigation by combining satellite and climate data, allowing farmers to reduce water consumption by up to 30%. The key is that the models work even in rural areas with limited infrastructure, making them viable for small farms.

In Mexico, projects such as Aiflow work with satellite data and accessible drones to generate maps of water stress or early pest detection. The algorithms, trained with reduced datasets, can run on local devices, avoiding reliance on the cloud or high-speed connections. Specific prototypes are beginning to appear, such as AgroLLM, a conversational model specialized in agriculture that integrates lightweight models with agricultural databases and is designed to operate in environments with limited connectivity.

Opportunities and Dilemmas

Frugal AI offers clear advantages: it reduces costs, democratizes access, and improves resilience by depending less on large data centers. However, it also presents challenges. With small datasets, the risk of bias and generalization errors increases. Accessible hardware limits model complexity, requiring continuous optimization. And the lack of specialized talent in SMEs may hinder adoption.

This is where public policies and support programs come into play. The OECD warns that the concentration of AI development in large corporations is an obstacle to global productivity, and recommends specific measures so SMEs are not left behind. In Europe, initiatives such as embrAIsme or cascade funding programs channel resources directly to SMEs looking to experiment with AI. These mechanisms are more agile than traditional calls and reduce bureaucracy for smaller players.

In Spain, Fundación Innovación Bankinter aims to play a facilitating role by connecting frugal-AI startups with programs such as ScaleUp Spain Network or Cre100do, which already support entrepreneurs in validating and scaling their projects.

Still, frugality does not mean improvisation. Even when dealing with lightweight solutions, transparency, explainability and privacy guarantees are still required. At the same time, frugal AI offers an additional advantage: technological resilience. By being able to run on local hardware, SMEs reduce their dependency on external providers and exposure to connectivity failures or cloud-price increases.

In short, frugal AI is the path toward making artificial intelligence truly inclusive—not a luxury for tech giants, but a practical tool at the service of the majority.