To understand this reduction in the cost of artificial intelligence, it is necessary to distinguish between training and inference. Training is the process through which an artificial intelligence model is created, enormous amounts of data are processed, and its parameters are adjusted using a gigantic amount of computing capacity. Inference, on the other hand, is what happens when a user asks a question or an application requests an artificial intelligence model to generate a response.<\/p>\n\n\n\n
Training the most advanced artificial intelligence models remains extremely expensive. Companies need thousands of specialized processors, enormous data centers, cooling systems, and large amounts of electricity to develop cutting-edge artificial intelligence models. The price decline mainly affects the cost of using these artificial intelligence systems once they have been trained, not the investment required to build them.<\/p>\n\n\n\n The difference is important because technological improvements make it possible to spread the cost of artificial intelligence infrastructure across an ever-growing number of queries. At the same time, artificial intelligence models are learning to use resources more efficiently, which makes it possible to progressively reduce the cost of each operation. The result is an unusual situation in technology: artificial intelligence systems are more capable than they were a few years ago and, at the same time, their cost per unit of use can be much lower.<\/p>\n\n\n\n Competition is one of the main drivers of this reduction in the cost of artificial intelligence. The major artificial intelligence labs compete to attract developers and companies that integrate their artificial intelligence models into applications, services, and internal processes. To achieve this, it is no longer enough to boast about having the smartest artificial intelligence model, because customers also compare speed, availability, quality, and price.<\/p>\n\n\n\n During the early years of generative artificial intelligence, the differences between models focused mainly on their capabilities. Users wanted to know which artificial intelligence system wrote better, which reasoned more accurately, or which was more useful for programming. Now the market is evolving toward a much more economic comparison: how much it costs to obtain a given result with artificial intelligence and what value that result provides.<\/p>\n\n\n\n This competition puts pressure on all artificial intelligence providers. If a company can obtain similar capabilities by using a much cheaper artificial intelligence model, it has little reason to pay a higher fee. That is why each cost reduction can become a tool for gaining users, increasing the volume of operations, and forcing artificial intelligence competitors to respond with new price cuts.<\/p>\n\n\n\n One of the keys to making artificial intelligence cheaper is that the industry has stopped focusing solely on creating increasingly larger models. For years, the idea prevailed that increasing parameters, data, and training capacity was the main path to improving artificial intelligence results. That strategy worked, but it also caused enormous growth in the costs associated with artificial intelligence development.<\/p>\n\n\n\n Now there is growing interest in achieving artificial intelligence models capable of obtaining similar results while using fewer resources. Some architectures make it possible to activate only part of a model's components depending on the task it has to perform. In this way, an artificial intelligence system can have a large total capacity without needing to use all of that capacity in every query.<\/p>\n\n\n\n Architectures known as Mixture of Experts are an example of this strategy applied to artificial intelligence. Instead of having all parameters participate in every operation, the system can select certain specialized components. This way of working makes it possible to reduce the amount of computing required and, consequently, lower the cost of generating responses through artificial intelligence.<\/p>\n\n\n\n The evolution of hardware is another fundamental element in understanding the decline in the price of artificial intelligence. The processors used for artificial intelligence have considerably increased their capacity, while manufacturers and data center operators have developed increasingly optimized systems. The improvement is not only about having faster chips, but about making each artificial intelligence operation consume fewer resources.<\/p>\n\n\n\n Memory, connections between processors, and the organization of data centers also play an important role in artificial intelligence infrastructure. Infrastructure specifically designed to run artificial intelligence can make better use of accelerators and reduce idle time. When a company operates on a gigantic scale, small efficiency improvements can turn into millions of dollars in savings and contribute to reducing the cost of artificial intelligence services.<\/p>\n\n\n\n There is also an important scale effect in artificial intelligence. A data center that operates continuously can distribute its costs across millions of queries and make better use of each processing unit allocated to artificial intelligence. The greater the demand for artificial intelligence, the more profitable it becomes to optimize infrastructure and reduce the cost of each operation.<\/p>\n\n\n\n Here we encounter one of the great paradoxes of artificial intelligence. It might seem that if each artificial intelligence query costs less, companies should ultimately spend less money, but technological experience shows that a reduction in price can lead to an increase in consumption. When a technology such as artificial intelligence becomes more accessible, applications that previously were not economically viable begin to appear.<\/p>\n\n\n\n A company may start by using artificial intelligence to answer customer questions and end up also using it to analyze contracts, write code, study documents, prepare reports, process data, and automate internal tasks. Each individual artificial intelligence operation may be cheaper, but the number of operations increases considerably. The savings per query can be offset by a much higher volume of use.<\/p>\n\n\n\n This situation helps explain why major companies continue to invest enormous amounts in infrastructure. The goal is not only to reduce the cost of artificial intelligence, but to prepare for a world in which the amount of artificial intelligence used will be much greater. The paradox is that the cheaper artificial intelligence becomes, the more likely it is to be used in practically any process.<\/p>\n\n\n\n Artificial intelligence agents are taking this trend one step further. A traditional chatbot receives a request and generates a response, while an artificial intelligence agent can divide a goal into multiple steps and use external tools to try to complete it. It can consult information, execute code, review results, detect errors, and try again before finishing.<\/p>\n\n\n\n Each of these steps requires computing capacity to run artificial intelligence. Therefore, a reduction in the price per token does not necessarily mean that a complex artificial intelligence task will cost proportionally less. An agent can use a much larger number of tokens than those needed to answer a simple question.<\/p>\n\n\n\n This is changing the way companies must calculate the cost of artificial intelligence. It is no longer enough to look at the price of one million tokens, because what really matters is how much it costs to complete a task using artificial intelligence. The relevant metric will increasingly be the cost per useful result, and not merely the processing cost.<\/p>\n\n\n\n Artificial intelligence reasoning models introduce another difficulty. These systems can dedicate a considerable amount of resources to analyzing a problem before providing a definitive answer. This process can improve the quality of certain artificial intelligence solutions, but it also increases computing consumption.<\/p>\n\n\n\n Two artificial intelligence models with similar prices can generate very different costs if one needs to perform many more operations to solve a task. This is especially important in programming, mathematics, research, and other activities in which artificial intelligence must analyze different possibilities before reaching a conclusion.<\/p>\n\n\n\n For this reason, the price of artificial intelligence must always be analyzed in relation to the utility obtained. A more expensive artificial intelligence model can be the economical option if it correctly solves a task that a cheaper system cannot complete. Likewise, using an extremely powerful artificial intelligence model for a simple task can represent an unnecessary expense.<\/p>\n\n\n\n International competition is also accelerating the reduction in artificial intelligence prices. Chinese laboratories have demonstrated that they can develop capable artificial intelligence models and offer them with highly competitive cost structures. The emergence of alternatives such as DeepSeek has forced Western artificial intelligence providers to pay even more attention to efficiency.<\/p>\n\n\n\n This competition changes the rules of the artificial intelligence market. Companies no longer have to choose only between a few models from large American companies, but can compare a growing variety of artificial intelligence systems. If a model offers sufficiently good quality for a fraction of the price, it can quickly become an attractive alternative for certain artificial intelligence applications.<\/p>\n\n\n\n Competitive pressure also benefits developers who use artificial intelligence. A company that depends on a single artificial intelligence provider may find itself in a vulnerable position if prices increase or commercial conditions change. The availability of alternative models makes it possible to distribute risk and choose different artificial intelligence systems depending on the type of task.<\/p>\n\n\n\n One consequence of this price war is that some artificial intelligence capabilities could eventually become a commodity. Generating text, translating, summarizing documents, classifying information, or performing certain programming tasks could become widely available artificial intelligence services with very low prices.<\/p>\n\n\n\n When a technology such as artificial intelligence becomes a commodity, possessing it is no longer enough to differentiate oneself. The competitive advantage lies in how artificial intelligence is used, in the data available, in integration with other systems, and in the ability to turn this technology into a useful product. This can profoundly change the strategy of companies developing artificial intelligence-based applications.<\/p>\n\n\n\n It can also benefit small companies that want to incorporate artificial intelligence. If using advanced artificial intelligence models continues to be cheap, a startup will not need to build its own system from scratch. It will be able to use existing models and concentrate its resources on developing specific solutions for particular sectors or needs.<\/p>\n\n\n\n The reduction in costs can also expand access to artificial intelligence. A small company that previously could not afford the cost of processing large amounts of information using artificial intelligence can begin to use models through cloud services. The same can happen with independent developers, educational institutions, and organizations with limited technology budgets.<\/p>\n\n\n\n Cost reductions make it possible to experiment with artificial intelligence applications that were previously too expensive. Analyzing documents, automating certain administrative tasks, creating specialized assistants, or incorporating artificial intelligence functions into a program can become much more accessible when the cost of each operation is reduced.<\/p>\n\n\n\n However, this does not mean that all access problems related to artificial intelligence will disappear. The infrastructure necessary to run the most advanced models continues to be concentrated in large companies that can invest enormous amounts in data centers and specialized processors. Artificial intelligence can be cheap for the end user and still depend on extremely expensive infrastructure.<\/p>\n\n\n\n For companies developing artificial intelligence, falling prices represent an opportunity and also a problem. Charging less for artificial intelligence makes it easier to attract users, but it requires generating much more volume to maintain revenue. Companies need to find a balance between offering competitive artificial intelligence prices and recovering the enormous investments made in research and infrastructure.<\/p>\n\n\n\n The situation may seem paradoxical: artificial intelligence providers need to reduce prices to compete, but they also need to make enough money to finance new models and data centers. The company that manages to reduce its artificial intelligence operating costs faster than its competitors will have a considerable advantage. In such a competitive market, improving efficiency can be as important as developing new artificial intelligence capabilities.<\/p>\n\n\n\n That is why efficiency has become one of the main battlegrounds of the artificial intelligence industry. It is not only about creating the smartest artificial intelligence model, but about ensuring that every dollar invested produces the greatest possible amount of useful capability. In the future, that difference could be as important as the quality of the artificial intelligence model itself.<\/p>\n\n\n\n There is also another risk: using artificial intelligence simply because it is available. A company can generate thousands of responses, reports, or analyses using artificial intelligence without obtaining any real benefit. The reduction in prices makes it easier to experiment with this technology, but it can also encourage unnecessary consumption of artificial intelligence.<\/p>\n\n\n\n True productivity appears when artificial intelligence solves a problem that previously required more time, money, or resources. If an artificial intelligence tool generates a document that nobody uses, the low cost of the process does not make it a profitable investment. That is why companies will have to learn to measure not only how much artificial intelligence they consume, but also how much value they obtain from it.<\/p>\n\n\n\n This difference will be fundamental as artificial intelligence prices continue to fall. The most efficient company will not necessarily be the one that uses the most artificial intelligence, but the one that knows how to use it in the right processes. The technology will be a means of improving productivity and not an objective in itself.<\/p>\n\n\n\n Everything indicates that the pressure to reduce the costs of artificial intelligence will continue. Models will become more efficient, specialized hardware will continue to evolve, and inference techniques will make it possible to make better use of each unit of processing. If competition remains intense, some of these improvements will probably end up being passed on to the prices of artificial intelligence services.<\/p>\n\n\n\n This does not mean that all artificial intelligence will become practically free. Tasks that require advanced reasoning, large amounts of context, multimodal generation, or agents capable of executing numerous steps may continue to have high costs. What we will probably see is an increasingly larger difference between simple artificial intelligence tasks, which can be performed for very small amounts, and more complex operations.<\/p>\n\n\n\n The 43% drop in the cost of using artificial intelligence represents much more than a simple reduction in fees. It is the result of a combination of competition, better algorithms, more efficient artificial intelligence models, advances in hardware, and infrastructure designed to operate on a gigantic scale. All of this is making it possible to obtain an increasing amount of artificial intelligence capability for every dollar invested.<\/p>\n\n\n\n The phenomenon may accelerate the adoption of artificial intelligence even further. If artificial intelligence models are sufficiently cheap, companies of all sizes will be able to incorporate them into their products and processes, while developers will have more freedom to experiment with new artificial intelligence-based applications. At the same time, increased consumption may keep demand high for the data centers, processors, and energy needed to sustain this expansion of artificial intelligence.<\/p>\n\n\n\n In this new scenario, having the right support can make the difference between incorporating artificial intelligence strategically or simply using it as another tool. ITD Consulting offers technology and consulting services aimed at helping companies take advantage of the new opportunities offered by artificial intelligence, optimize their processes, and advance their digital transformation. If your company wants to analyze how it can apply artificial intelligence efficiently and in a way adapted to its needs, you can contact the ITD Consulting team by writing to <\/strong>info@itdconsulting.com<\/strong><\/a>.<\/strong><\/p>","protected":false},"excerpt":{"rendered":" El coste de usar inteligencia artificial cae un 43%: \u00bfPor qu\u00e9 la IA es cada vez m\u00e1s barata? ITD Consulting te mantiene informado.<\/p>","protected":false},"author":5,"featured_media":36847,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[459,559,89,248],"class_list":["post-36843","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-noticias","tag-459","tag-costo","tag-ia","tag-inteligencia-artificial"],"_links":{"self":[{"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/posts\/36843","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/comments?post=36843"}],"version-history":[{"count":1,"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/posts\/36843\/revisions"}],"predecessor-version":[{"id":36848,"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/posts\/36843\/revisions\/36848"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/media\/36847"}],"wp:attachment":[{"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/media?parent=36843"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/categories?post=36843"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/itdconsulting.com\/en\/wp-json\/wp\/v2\/tags?post=36843"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"ITD](\"https:\/\/itdconsulting.com\/wp-content\/uploads\/2026\/08\/itd-consulting-backup-acronis-vds-vps-ciberseguridad-microsoft-365-inteligencia-artificial-costos-1.webp\")
<\/figure>\n\n\n\nThe price war between AI models<\/strong><\/h2>\n\n\n\n
More efficient models are changing the market<\/strong><\/h2>\n\n\n\n
Hardware is also making artificial intelligence cheaper<\/strong><\/h2>\n\n\n\n
Cheaper AI can lead to an increase in consumption<\/strong><\/h2>\n\n\n\n
Artificial intelligence agents may increase consumption even further<\/strong><\/h2>\n\n\n\n
![\"ITD](\"https:\/\/itdconsulting.com\/wp-content\/uploads\/2026\/08\/itd-consulting-backup-acronis-vds-vps-ciberseguridad-microsoft-365-inteligencia-artificial-costos-2-1024x683.webp\")
<\/figure>\n\n\n\nAdvanced artificial intelligence reasoning has a price<\/strong><\/h2>\n\n\n\n
Chinese artificial intelligence increases the pressure<\/strong><\/h2>\n\n\n\n
Artificial intelligence is approaching a commoditization phase<\/strong><\/h2>\n\n\n\n
The fall in prices can democratize artificial intelligence<\/strong><\/h2>\n\n\n\n
The great challenge is achieving profitability<\/strong><\/h2>\n\n\n\n
More artificial intelligence does not automatically mean more productivity<\/strong><\/h2>\n\n\n\n
What can we expect from artificial intelligence over the next few years?<\/strong><\/h2>\n\n\n\n
![\"ITD](\"https:\/\/itdconsulting.com\/wp-content\/uploads\/2026\/08\/itd-consulting-backup-acronis-vds-vps-ciberseguridad-microsoft-365-inteligencia-artificial-costos-3-1024x683.webp\")
<\/figure>\n\n\n\n