Tools based on Large Language Models (LLMs) have become part of the development workflow. GitHub Copilot, ChatGPT, Cursor, and other assistants have began suggesting code, reviewing functions, and accelerating tasks that previously required hours of manual labor.

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But as these tools became established, a new debate began to emerge within technical teams: which billing model makes more sense, a monthly subscription or pay-per-use?

The discussion goes beyond price and involves cost predictability, usage efficiency, data security, and matching the IA model to the development context.

In this article, we analyze the differences between subscription-based and token-based LLMs, how they impact development teams, and why this billing model has become one of the most debated topics in the market of AI applied to code.

Monthly subscriptions

Most programming assistants available today follow a traditional enterprise software model: a monthly license per user. Each developer has an active account, and the company pays a fixed fee to maintain access to the tool.

This format offers some operational advantages. Onboarding is simple, monthly costs can be easily predicted, and license management follows a familiar model for IT departments.

At the same time, the development workflow rarely occurs in a linear fashion. Projects go through different phases throughout the month, alternating between periods of intense coding and moments dedicated to planning, architecture, code review, or technical meetings.

This dynamic causes the use of AI tools to oscillate significantly over time. On some days, the assistant is used constantly; on others, it is barely accessed.

When the cost remains fixed, regardless of usage, part of the contracted capacity might remain underutilized.

How the token-based model changes cost logic

The evolution of LLM APIs has paved the way for a billing model based on processed tokens. In this format, each interaction with the model consumes a number of tokens corresponding to the text sent and the response generated.

Instead of paying a fixed monthly license, the cost directly reflects the volume of tool usage.

This model features some important characteristics:

• billing proportional to actual consumption;

• automatic adaptation to periods of more intense use;

• elimination of idle licenses;

• greater transparency regarding the cost of each interaction.

Teams that use assistants in a variable manner throughout the month tend to notice significant differences with this format. Projects with irregular development cycles or teams alternating between technical and strategic tasks can benefit from this flexibility.

AI costs and the impact on ROI

As code assistants begin to be adopted by entire teams, the financial impact of these tools starts to appear more clearly in technology budgets.

The individual cost of a license is usually relatively low, but the sum of different tools and users can become significant over time. In many development environments, multiple assistants are tested or used in parallel, increasing the total cost.

Several factors contribute to this scenario:

• irregular use of tools throughout the month;

• multiple active licenses within the same team;

• prolonged periods during which developers do not use AI assistants.

When the billing model remains fixed, these variations in usage do not alter the final cost. This behavior draws the attention of technical managers seeking to better align spending with the actual level of tool utilization.

Consumption-based models emerge precisely in this context, offering a closer match between usage and cost.

The limitations of generic assistants

Most of the code assistants available today were trained on data from different industries at the same time. This approach broadens the general capability of the models, allowing them to answer a wide variety of technical questions.

In environments requiring specific architecture, security, or compliance rules, this generalization may demand additional validation steps by development teams.

Various industries work with their own technical standards, specific data formats, or regulatory requirements that must be considered during software development.

Some examples include:

• interoperability protocols in enterprise systems;

• integration standards in financial systems;

• structured data formats in industrial platforms;

• regulatory requirements in sectors such as healthcare, insurance, or government.

When the AI assistant does not fully recognize these technical contexts, code suggestions tend to undergo additional reviews before being incorporated into the project.

This process adds steps to the development workflow and increases the attention required for aspects such as security, data consistency, and regulatory compliance.

In environments with high technical demands, this additional validation becomes a natural part of the engineering process.

The rise of specialized assistants

With the maturity of the LLM market, a new category of tools is beginning to emerge: domain-specific specialized AI assistants.

Instead of seeking universal coverage, these solutions are designed to handle specific development contexts. The training and operational logic consider technical standards, regulatory requirements, and common practices of that industry.

This approach can bring some operational advantages:

• finer alignment with the technical standards used in the industry;

• reduction of manual validation effort;

• better understanding of specific data structures;

• closer compliance with security and regulatory standards.

Specialization does not replace general assistants, but it expands the range of tools available to teams working in environments with tighter technical constraints.

Discover CodeAdvisor

The CodeAdvisor was developed following this specialization logic. The platform functions as a code assistant integrated into the IDE (Integrated Development Environment), designed to support teams developing solutions across various industries.

Integration is done via a plugin installed directly in the development environment. From there, the assistant accompanies the workflow of technical and non-technical professionals, offering real-time suggestions.

Among the features aimed at developers are:

• code generation and review during writing;

• contextualized technical suggestions;

• support in refactoring tasks;

• monitoring consumption through a dashboard.

The available plans are based on token quantity, allowing the cost to match the actual volume of platform usage.

This format tends to adapt better to teams with variable use of AI assistants, avoiding the need to maintain fixed licenses for all employees.

Security and infrastructure control

In regulated environments, software development frequently involves sensitive data and strict information protection requirements. For this reason, infrastructure architecture takes on a central role in choosing the tools used by the team.

The CodeAdvisor was designed to operate on AWS infrastructure located in Brazil, ensuring that data remains within the national territory and complies with LGPD requirements.

The architecture includes:

• hosting in an AWS environment with certifications;

• encryption at rest and in transit;

• complete audit logs;

• access control and traceability of operations.

In addition, codes and data used by the teams are not employed for training public models, preserving the confidentiality of the developed applications.

Token vs. subscription: which is cheaper?

The difference between the models becomes clearer when placed in a practical usage scenario:

Monthly cost comparison:

Based on this comparison, the main difference lies in the billing logic.

In the monthly fee per user model, the cost grows linearly according to the number of collaborators. Each developer needs an active license, regardless of how much they use the tool throughout the month.

In practice, this means maintaining a fixed cost even in scenarios where usage is irregular—something common in development teams, who alternate between periods of high coding productivity and moments focused on planning, architecture, or validation.

Additionally, as these are international services, there may still be extra fees and currency exchange variations impacting the final cost.

In the token-based model, on the other hand, the cost directly follows the volume of usage. Instead of paying for access, the company pays for the actual processing carried out by the models.

This format eliminates the problem of idle licenses and adapts better to environments with fluctuating demand.

Another relevant point is technical flexibility. Different language models (Claude, Qwen, and others) have distinct costs per token, allowing usage to be adjusted according to the type of task.

In practice, this enables more efficient strategies, such as using cheaper models for simple tasks and directing more robust models to highly complex demands.

This level of control does not exist in the traditional subscription model, where the cost is fixed and decoupled from how the tool is utilized.

When combining cost proportional to usage, the elimination of idle licenses, and the possibility of technical optimization, the token-based model tends to offer a more balanced relationship between investment and return.

In teams with multiple projects and variable demand paces, this difference is even greater and directly impacts the financial efficiency of the operation.

The evolution of the code assistants market

The use of LLMs in software development is in an evolutionary phase. As teams accumulate experience with these tools, factors such as cost, security, and technical specialization become more relevant in adoption decisions.

Two trends appear frequently in current discussions in the industry:

1. Billing models aligned with actual consumption.

2. Assistentes specialized in specific technical contexts.

It is in this context that solutions designed to handle these new demands are beginning to emerge. Specialized assistants, with consumption-based billing models and greater control over infrastructure, represent a natural evolution from the first general-purpose tools.

Initiatives like CodeAdvisor reflect this movement by proposing an IA agent integrated into the development environment, with a focus on real use, cost transparency, and adaptation to more demanding technical contexts.