The customization of large-scale language models (LLMs) has become a differentiating factor for companies looking to maximize the performance of their artificial intelligence applications. However, the path to optimal adaptation is not unique or simple: a key trade-off arises between two approaches – Supervised Fine-Tuning (SFT) and In-Context Learning (ICL) – whose effectiveness is conditioned by the congestion of shared computational resources. In this article we analyze the technical, economic and strategic implications of this decision, offering a guide for professionals and companies that integrate AI into their processes.
To understand the problem, it is worth remembering that LLMs, such as GPT or LLaMA, are deployed in shared infrastructures where multiple users compete for computing power. The SFT requires additional training on the base model, which consumes valuable time and resources but offers deep and permanent customization. On the contrary, the ICL allows the behavior of the model to be adapted in real time through examples included in the prompt, without modifying the weights, resulting in a lighter use but limited in scope and consistency. When aggregate demand saturates servers, every millisecond and every GPU core counts, and the choice between SFT and ICL becomes critical.
From a technical point of view, the decision depends on two main variables: the coverage of the model's pretraining and the signal-to-noise ratio of the task data. If the model has already seen enough similar examples during its initial training, the ICL can be surprisingly effective; on the other hand, for tasks with very specific or noisy patterns, SFT is usually imposed. However, congestion reverses these preferences: when resources are in high demand, SFT—by consuming more resources intensively—can become disadvantageous even for tasks where it would technically be superior. The answer is not trivial and requires dynamic analysis.
Platforms that offer AI services face an additional dilemma: should they support both forms of personalization or specialize in one? Our review of the top platforms reveals a clear trend: the proportion offered by both SFT and ICL has risen from 9.5% in 2021 to 71.4% in 2025. This move responds to the need to capture different customer profiles and the realization that, under congestion, offering both methods never harms the maximum benefits of the platform, even if it increases the computational load. The key is to design pricing strategies and resource allocation that encourage the efficient use of each technique.
How can these reflections be transferred to the business world? For a company developing artificial intelligence for enterprises, personalizing LLMs opens up huge opportunities in areas such as customer service chatbots, recommendation engines, or internal virtual assistants. For example, when implementing an incident classification system, fine-tuning can achieve 97% accuracy, but if the base model is already proficient and the infrastructure is congested, perhaps in-context learning is more cost-effective. The decision should be based on objective metrics and continuous monitoring of performance and costs.
And congestion isn't an abstract phenomenon: in cloud environments, GPU and CPU sharing causes latency spikes that degrade the user experience. That's why many organizations choose to deploy their models on dedicated infrastructures or through managed AWS and Azure cloud services , which allow them to scale on demand. Q2BSTUDIO, as a software and technology development company, accompanies its clients on this journey, integrating AI solutions that balance performance, cost, and personalization. Our team designs custom applications that incorporate everything from autonomous AI agents to Power BI dashboards to monitor the behavior of models in production.
One aspect that is often overlooked is safety. When an LLM is customized using SFT, the resulting model may inherit vulnerabilities or biases from the training data. Cybersecurity then becomes a fundamental pillar: it is necessary to audit datasets, apply adversarial training techniques and protect inference endpoints. Similarly, business intelligence and analytics with Power BI allow you to visualize congestion, performance, and cost metrics, making it easier to make informed decisions about when to use SFT or ICL.
From a strategic perspective, the customization of LLMs under congestion forces us to rethink the business model of platforms. It is not only about offering the best technology, but about designing rules of the game that align user incentives with the overall efficiency of the system. For example, subscription plans can be introduced that prioritize access to resources during off-peak hours, or differentiated rates depending on the customization technique chosen. Companies that develop custom applications and custom software can benefit from these strategies to optimize their own AI deployments.
On the horizon, research points to hybrid methods that combine the best of both worlds: a slight fine-tuning on specific parameters (LoRA) along with learning in dynamic context. However, congestion will continue to be a limiting factor as long as the demand for computing exceeds the supply. The solution is not only technical, but also organizational and economic. Companies that manage to master this balance will gain a significant competitive advantage.
At Q2BSTUDIO we help organizations navigate this complexity. Our artificial intelligence services range from initial consulting to implementation and monitoring, always adapting to the specific context of each client. We work with AI agents, integrate Power BI for performance dashboards, and ensure that each solution meets the highest cybersecurity standards. If your company is exploring LLM customization, we invite you to contact us to find out how to turn congestion into a strategic opportunity.
