Most finance teams struggled to set accurate AI budgets in 2025, resulting in an average of $15,000 in unexpected costs per quarter. This was largely due to the lack of visibility into AI spend, making it difficult to forecast costs and plan accordingly. The primary challenge was that AI costs are often tied to Large Language Model (LLM) usage, which can be unpredictable and prone to sudden spikes. For instance, a single chatbot feature can go from costing $500 to $5,000 in a matter of weeks, catching finance teams off guard.
Why AI budgets in 2025 were mostly guesswork
The main reason AI budgets were often inaccurate was the lack of granular data on LLM usage. Most teams relied on high-level cost reports from AI providers, which didn't provide enough detail to inform budget decisions. For example, a report might show that the team spent $10,000 on AI in a given month, but it wouldn't break down which features or departments were responsible for that spend. This made it impossible to identify areas of waste or opportunities for optimization. With Cognocient, teams can access detailed reports on LLM usage, including the specific features and departments driving costs. This level of visibility enables finance teams to make informed budget decisions and avoid surprises.
The lack of visibility into AI spend also made it challenging for finance teams to identify cost drivers. For instance, a team might know that their chatbot feature is driving up costs, but they might not know which specific aspects of the chatbot are responsible for the increased spend. Is it the number of user interactions, the complexity of the conversations, or something else entirely? Without this level of insight, teams were forced to make educated guesses when setting their AI budgets. Cognocient solves this problem by providing a detailed breakdown of LLM costs, including the number of tokens (the unit AI providers charge by — roughly ¾ of a word) used by each feature and department. This enables teams to identify the root causes of cost increases and make data-driven decisions to optimize their AI spend.
The impact on budget planning
The lack of visibility into AI spend had a significant impact on budget planning. Teams often found themselves either over- or under-allocating resources for AI, which can have serious consequences. For example, if a team under-allocates resources for AI, they may be forced to cut back on other important initiatives or absorb the excess costs, which can be detrimental to the business. On the other hand, if a team over-allocates resources for AI, they may be wasting money on unnecessary or underutilized features. Cognocient helps teams avoid these pitfalls by providing a clear and accurate picture of their AI spend. With this information, teams can make informed decisions about how to allocate their resources and ensure that their AI budget is aligned with their business goals.
The data you need before setting an AI budget
To set an accurate AI budget, teams need access to detailed data on their LLM usage. This includes information on the number of tokens used by each feature and department, as well as the cost of those tokens. Cognocient provides this data in real-time, enabling teams to make informed decisions about their AI spend. For example, a team might use Cognocient to identify which features are driving up costs and adjust their budget accordingly. They might also use Cognocient to identify opportunities for optimization, such as reducing the number of tokens used by a particular feature or switching to a more cost-effective LLM.
The data provided by Cognocient also enables teams to track their AI spend over time. This is critical for identifying trends and patterns in LLM usage, which can inform budget decisions. For instance, a team might notice that their AI spend tends to increase during certain times of the year or in response to specific business initiatives. With this information, they can adjust their budget to ensure that they have sufficient resources to support their AI needs. Cognocient makes it easy to track AI spend over time, providing teams with a clear and accurate picture of their LLM usage.
Using Cognocient to inform budget decisions
Cognocient provides teams with the data and insights they need to make informed budget decisions. For example, a team might use Cognocient to identify which features are driving up costs and adjust their budget accordingly. They might also use Cognocient to identify opportunities for optimization, such as reducing the number of tokens used by a particular feature or switching to a more cost-effective LLM. By providing teams with a clear and accurate picture of their AI spend, Cognocient enables them to make data-driven decisions about their budget.
Cost drivers: model pricing, token volume, feature growth
There are several cost drivers that can impact an AI budget. One of the most significant is model pricing, which refers to the cost of using a particular LLM. Different models have different pricing structures, and some may be more cost-effective than others. For example, a team might find that using a more advanced LLM increases their costs by 20%, while using a more basic model reduces their costs by 15%. Cognocient helps teams navigate these trade-offs, providing them with detailed information on the costs associated with different models.
Another key cost driver is token volume, which refers to the number of tokens used by a particular feature or department. The more tokens used, the higher the costs. Cognocient provides teams with detailed information on token volume, enabling them to identify areas where they can reduce their token usage and lower their costs. For example, a team might find that their chatbot feature is using a large number of tokens due to the complexity of the conversations it's handling. By optimizing the chatbot to use fewer tokens, the team can reduce their costs and improve their overall efficiency.
The impact of feature growth on AI budgets
Feature growth is another key cost driver that can impact an AI budget. As teams add new features or expand existing ones, their AI spend can increase significantly. For example, a team might find that adding a new feature to their chatbot increases their AI spend by 30%. Cognocient helps teams anticipate and plan for these increases, providing them with detailed information on the costs associated with different features and departments. By using Cognocient, teams can ensure that their AI budget is aligned with their business goals and that they have sufficient resources to support their growing needs.
Building an AI cost model in a spreadsheet
Building an AI cost model in a spreadsheet can be a complex and time-consuming process. Teams must gather data on their LLM usage, including the number of tokens used by each feature and department, as well as the cost of those tokens. They must then use this data to estimate their future AI spend, taking into account factors such as feature growth and model pricing. Cognocient simplifies this process, providing teams with a pre-built AI cost model that they can use to estimate their future spend.
Using Cognocient to estimate AI spend
Cognocient provides teams with a detailed breakdown of their LLM costs, including the number of tokens used by each feature and department. This information can be used to estimate future AI spend, taking into account factors such as feature growth and model pricing. For example, a team might use Cognocient to estimate that their AI spend will increase by 25% over the next quarter due to the addition of new features. They can then use this information to adjust their budget and ensure that they have sufficient resources to support their growing needs.
Forecast scenarios: conservative, base, high-growth
When building an AI cost model, teams must consider different forecast scenarios to ensure that they are prepared for a range of possible outcomes. Cognocient provides teams with three pre-built forecast scenarios: conservative, base, and high-growth. The conservative scenario assumes that AI spend will remain steady or decrease over time, while the base scenario assumes that spend will increase at a moderate rate. The high-growth scenario assumes that spend will increase rapidly, driven by factors such as feature growth and increased adoption.
Using Cognocient to forecast AI spend
Cognocient provides teams with a detailed breakdown of their LLM costs, including the number of tokens used by each feature and department. This information can be used to forecast future AI spend, taking into account factors such as feature growth and model pricing. For example, a team might use Cognocient to forecast that their AI spend will increase by 50% over the next year due to the addition of new features and increased adoption. They can then use this information to adjust their budget and ensure that they have sufficient resources to support their growing needs.
Getting engineering and finance to agree on the number
One of the biggest challenges in AI budget planning is getting engineering and finance teams to agree on the number. Engineering teams often have a deep understanding of the technical requirements of AI projects, but may not have a clear understanding of the financial implications. Finance teams, on the other hand, may have a clear understanding of the financial implications, but may not have a deep understanding of the technical requirements. Cognocient helps to bridge this gap, providing both teams with a clear and accurate picture of AI spend.
Using Cognocient to facilitate collaboration
Cognocient provides both engineering and finance teams with a shared understanding of AI spend, enabling them to collaborate more effectively. For example, a team might use Cognocient to identify areas where they can reduce their token usage and lower their costs. The engineering team can then use this information to optimize their AI models, while the finance team can use it to adjust their budget and ensure that they have sufficient resources to support their growing needs.
# Before
client = OpenAI(base_url="https://api.openai.com/v1")
# After — Cognocient intercepts, logs, and tags every call
client = OpenAI(base_url="https://api.cognocient.com/v1")
Key Takeaways
- Accurate AI budgeting: Cognocient provides teams with the data and insights they need to make informed budget decisions, reducing the risk of unexpected costs and ensuring that AI spend is aligned with business goals.
- Cost drivers: Cognocient helps teams identify the key cost drivers that impact their AI budget, including model pricing, token volume, and feature growth.
- Forecast scenarios: Cognocient provides teams with pre-built forecast scenarios to ensure that they are prepared for a range of possible outcomes, including conservative, base, and high-growth scenarios.
Try Cognocient Free
Most teams struggle to set accurate AI budgets, resulting in an average of $15,000 in unexpected costs per quarter. Cognocient gives finance teams the data and insights they need to make informed budget decisions, reducing the risk of unexpected costs and ensuring that AI spend is aligned with business goals.
Start your 10-day free trial →
No credit card required · Setup in 2 minutes.