Balancing AI Innovation and Cloud Costs: the AI FinOps Perspective

AI offers companies exciting new ways to innovate, like improving customer interactions, speeding up processes and entering new markets. But while innovation sounds great, it can be expensive. The budget for the newest AI learning model is burned in no time, and the value is limited to non-existent. This is where AI FinOps comes in. Finding the right balance between innovation and cost control is crucial for businesses looking to leverage AI without overshooting their budgets.

The Innovation Trap: How AI Cloud Costs Can Skyrocket

AI workloads, like training machine learning models and processing large datasets, consume much compute power and storage. Without a clear plan to manage costs, AI can quickly become very expensive. Part of this challenge comes from the use of “tokens”, which represent processing units, storage and througput in cloud environments. In AI, this could mean how many tokens a model requires to process a certain amount of text or analyse several images. Tokens are often an abstraction of actual cloud usage, making it harder to track costs without well-developed monitoring tools.

The analogy underneath will help you understand the problem better.

Imagine buying tokens at a public event. For €50, you get a set amount of tokens to exchange for food and drinks. Over time, you lose track of how much a slice of pizza costs in euros because you’re dealing with tokens, not cash. Similarly, tokens can hide the true cost of cloud usage, making it hard to track AI expenses. Teams might not realise how much they spend, leading to surprisingly high cloud bills.

However, tokens aren’t the only billing mechanism. Some cloud services offer package pricing, such as a fixed price for processing a certain number of images or transactions. This makes it even harder to understand the cost breakdown, as consumption units sometimes directly reflect resource usage, and combinations with tokens are always possible in the real world, which adds to the complexity.

The Goldilocks Principle for Cloud Capacity

Another challenge is figuring out how much cloud capacity you need. Because of the way tokens work, it can be hard to know how much technology you need to process a certain model or request. The “Goldilocks Principle” – which comes from astronomy and describes how a planet needs to be just the right distance from a star to support life – is a useful way to think about optimising cloud costs. Just like a planet shouldn’t be too far (too cold) or too close (too hot) to its star, cloud capacity needs to be just right to ensure good performance without wasting resources.

Capacity	Low	Just Right	Excess
Batch Processing	OK	OK	Resource wastage
Non-critical apps	May impact user experience	OK	Unnecessary costs
Mission-critical real-time apps	Delays, loss of value	OK	Expensive, but sometimes acceptable

Insufficient capacity can have catastrophic consequences for performance-sensitive applications, such as real-time AI systems. On the other hand, too much capacity leads to waste, particularly for applications that are not critical. Companies need to evaluate this balance to avoid unnecessary spending continuously. 

Optimising Resources: Balancing Spikes and Steady Workloads

AI workloads are often unpredictable, with spikes in usage during training phases or when analysing large amounts of data. Balancing these spikes with more constant workloads is tricky. This variability makes it difficult to determine a “steady state” and how much capacity should be reserved versus kept on-demand.

Companies need to analyse usage patterns and cost models in detail to optimise this. Historical data can reveal recurring spikes, allowing businesses to match their cost models accordingly. Smart capacity planning—reserving resources for steady needs while relying on the on-demand capacity for spikes—ensures the best ROI. This requires constant monitoring and adjustment, which is where FinOps practices come in.

A Chicken-and-Egg Problem: Building a Culture of Accountability

Analysing usage patterns can be tricky, mainly when a team hasn’t fully utilised its resources. How can you analyse usage based on non-use? This creates a chicken-and-egg problem: Teams need data to plan, but gathering that data requires resource use. This is why bidirectional communication between FinOps and technical teams is essential. By working closely together, teams can generate the necessary usage data, analyse it, and use that insight to optimise future resource allocation.

This highlights the importance of accountability in FinOps culture. AI FinOps is not about cost control and about creating a culture where teams are responsible for their cloud costs and actively work together to optimise spending. Regular follow-up helps to reinforce this accountability within teams.

The Role of FinOps in AI Cost Management

FinOps plays a crucial role in controlling AI cloud costs. FinOps aims to make cloud usage and costs transparent so businesses can strike a balance between innovation and efficiency. Here are a few ways FinOps helps:

1. Token Usage Transparency: FinOps ensures that companies understand how many tokens they are using and how this translates into actual costs. This prevents teams from losing sight of their spending and helps them work more consciously.
2. Capacity Management and Scaling: As shown by the Goldilocks Principle, finding the right balance of capacity is crucial for AI workloads. Too much or too little capacity has direct implications for performance and costs. FinOps helps businesses maintain this balance by continuously monitoring capacity and usage.
3. Resource Optimisation: Instead of always relying on on-demand resources, which are flexible but costly, FinOps helps businesses optimise their AI workloads by using more cost-effective reserved or dedicated resources. This can lead to significant long-term savings.
4. Collaborative Accountability: Good communication between FinOps and technical teams ensures that usage patterns are analysed in real-time, and future resource needs are adjusted accordingly. This culture of accountability is key to finding the perfect balance between cost control and AI innovation.

Innovation and Cost Control in Balance

AI holds enormous promise, but without the right approach, costs can quickly get out of hand. Companies need to learn how to use the power of AI without blowing their budgets. FinOps provides the insights, tools, and collaboration needed for cost management and optimisation, allowing businesses to innovate without risking their financial stability.

The need to combine AI innovation with FinOps Controls

AI offers powerful ways to innovate, but it can quickly become too expensive if you’re not careful with costs. This can prevent your organisation from getting the benefits it needs. By adopting FinOps, companies can manage AI cloud costs effectively while continuing to innovate. Whether managing token usage, balancing capacity, or optimising resources, FinOps ensures long-term success through transparency and collaboration.

At Devoteam, we specialise in helping businesses align AI strategies with cost efficiency through AI Finops best practices. Contact our team today to find out how we can optimise your AI setups while keeping costs under control.

References: YouTube – FinOps Foundation – Example cost estimator