Why your LLM app needs FinOps to succeed

Addressing the complexities of AI expenditure for long-term impact

TL;DR: FinOps is key to turning your LLM projects into sustainable, revenue-generating assets! With AI expenses soaring from unpredictable token usage and multi-cloud dependencies, traditional budgeting tools fall short—and existing tools still have a long way to go to cover the complex needs of LLM applications. To stay ahead, organizations must start investing in FinOps practices now, identifying their specific cost management needs and building the capabilities required to take full control over AI expenses, ensuring long-term success and scalability.

Recap

In our previous post, we highlighted the difficulties many LLM projects encounter in demonstrating real business value, often remaining stuck in the proof-of-concept (PoC) phase. While LLMs have the potential to revolutionize industries, many applications focus on internal improvements that don't justify their high costs.

The true strength of LLMs lies in their ability to transform businesses and boost revenues. By integrating these models into essential business functions, companies can create new income streams and improve their competitive position.

As we discussed previously, achieving success requires early ROI validation through strategic experimentation. This means evaluating operational expenses against potential revenue or savings, implementation feasibility, and overall business impact.

In this post, we will specifically examine operational expenses, particularly cloud costs, while the other aspects will be explored in future discussions.

The challenge

Managing cloud expenses is becoming increasingly complex due to intricate cloud environments, rapid technological advancements, and the need for cross-functional collaboration. Generative AI adds even more challenges, introducing variable resource usage, complex pricing models, and specialized infrastructure requirements.

Take, for example, a company using LLMs (like GPT-4 or PaLM-2) via a cloud API for customer service chatbots. Costs can spike unexpectedly during peak traffic or complex queries, and traditional cloud management tools struggle to track these AI-specific expenses. As a result, companies often resort to building custom dashboards to monitor API calls, GPU usage, and data processing across multiple providers like OpenAI, AWS, and Google Cloud—only to face difficulties in accessing, integrating, and analyzing this data effectively.

Organizations must now manage advanced technologies while navigating the intricate relationships between model types, usage patterns, performance impact, and costs. The lack of comprehensive tools to handle these complexities only heightens the challenge.

This evolving landscape demands new strategies, tools, and expertise to manage and optimize costs effectively, ensuring sustainable success in the AI-powered era of cloud computing.

Understanding the costs behind LLMs

The complexity of managing LLM costs stems from the variety of models available, their deployment options, and corresponding pricing structures. When developing LLM applications, organizations can choose from several pricing models based on their preferred deployment method (cloud, on-premises, or edge):

• Pay-per-token model: Common for cloud-based API services, costs are based on the number of tokens processed. It offers scalability and transparency but can lead to unpredictable expenses.

• Subscription-based model: Organizations pay a fixed fee for a specific usage quota, providing more predictable costs but risking over- or under-utilization.

• On-demand compute: Relevant for self-hosted models on cloud infrastructure, charges are based on computational resources used (e.g., GPU hours). It offers flexibility but can be costly for high-volume usage.

• Provisioned Throughput Units (PTUs): This reserved capacity model ensures more predictable performance and costs, suitable for consistent, high-volume workloads.

• Self-hosting model: Organizations deploy LLMs on their own infrastructure, incurring upfront hardware costs and ongoing operational expenses. It offers greater control and data privacy but requires significant initial investment.

• Hybrid models: These combine different pricing approaches for more flexible cost management.

• Edge deployment: This approach involves running LLMs directly on edge devices, such as smartphones, IoT devices, or specialized hardware. LLMs for edge deployment vary in size and complexity, and are designed for resource-constrained devices (e.g.,DistilBERT, or MobileBERT).

When selecting a pricing model, organizations should consider their expected usage patterns, performance requirements, data privacy needs, and budget constraints. It's crucial to perform this analysis early in the project and estimate expected monthly or yearly costs.

However, organizations may still face unexpected cost challenges when running LLM applications in production environments. For example, in a pay-per-token model, it's often difficult to predict the number of input/output tokens in a request and the impact of peak times.

To address these challenges, organizations need to adopt effective tools and practices for managing LLM costs.

What is FinOps and why is it important?

FinOps (short for Financial Operations) is an emerging framework designed to optimize cloud financial management by fostering collaboration between finance, engineering, and business teams. It emphasizes cost visibility and accountability in cloud spending.

By implementing FinOps practices, organizations can:

1. Gain insights into their cloud usage

2. Understand where money is being spent

3. Align spending with business objectives

4. Encourage stakeholders to take ownership of cloud resources

5. Make more informed decisions

One of the primary benefits of FinOps is its ability to drive cost optimization while maintaining the agility needed for innovation. For example, a company that has recently migrated to the cloud might experience unexpected costs due to underutilized resources. Through FinOps, the organization can analyze usage patterns and identify inefficiencies, allowing teams to scale down or shut off unnecessary resources. This approach reduces waste and empowers technical teams to make cost-conscious decisions without hindering innovation.

Moreover, FinOps fosters a culture of continuous improvement and collaboration across departments. As finance and technical teams work together to analyze cloud expenditures and forecast future costs, they create a feedback loop that enhances accountability and performance.

The role of FinOps in LLM projects

FinOps is crucial for LLM applications due to their resource-intensive nature and complex deployment landscape. With a multitude of models, cloud providers, and hosting approaches available, organizations face significant challenges in managing costs and optimizing performance. FinOps principles help navigate this complexity by providing visibility into expenses across different platforms and deployment methods.

FinOps is more than just a tool for validating ROI; it is a vital practice for managing and optimizing the financial aspects of LLM projects. By closely monitoring cloud usage and controlling costs, FinOps ensures that the return on investment remains positive throughout the project lifecycle.

In the context of LLM applications, where operational expenses for API calls and data processing can quickly escalate, FinOps becomes indispensable. It provides organizations with the means to track expenses in real-time, forecast budgets accurately, and optimize resource allocation. This level of financial oversight is essential for keeping LLM costs in check and maintaining project viability.

Moreover, FinOps helps align LLM initiatives with broader business objectives by providing clear visibility into the financial performance of these projects. This alignment is crucial for making informed decisions about scaling, modifying, or potentially discontinuing LLM applications based on their financial impact.

Current challenges in adopting FinOps for LLM applications

Implementing FinOps practices for LLM applications is essential for maintaining financial control while harnessing the full potential of these AI models. However, this practice is still developing, and there is much room for improvement and innovation. Here are the five most significant challenges organizations face when adopting FinOps for LLM applications:

1. Complex pricing models: The intricate and often opaque pricing structures of cloud providers make it challenging to predict costs accurately, leading to potential budget overruns.

2. Non-deterministic resource usage: LLMs can have varying resource requirements based on input and task, complicating consistent cost optimization and forecasting.

3. Balancing cost and performance: Organizations face the challenge of minimizing costs while ensuring high-quality performance from LLMs, which requires deep engineering expertise.

4. Cross-departmental collaboration: Effective FinOps requires strong cooperation between finance, engineering, and data science teams, which can be hindered by siloed organizational structures.

5. Skill gap and tool limitations: There is a shortage of professionals who understand both LLM technology and FinOps principles, along with a lack of specialized tools for managing LLM costs effectively.

Addressing these challenges is crucial for organizations looking to leverage LLMs while maintaining financial control and maximizing value.

Current tooling landscape and limitations in LLM FinOps

The tooling landscape for LLM FinOps is still evolving, with various tools and approaches aimed at managing costs and optimizing LLM deployments. Major cloud providers like AWS, Google Cloud, and Azure offer native cost management tools that can be adapted for LLM workloads, such as AWS Cost Explorer, Google Cloud Cost Management, and Azure Cost Management. Additionally, general-purpose FinOps platforms like Apptio Cloudability or CloudHealth provide insights into overall cloud spending, including resources allocated to LLM applications.

Specialized tools are emerging to tackle the unique challenges of LLM deployments. For example, TensorOps focuses on monitoring and optimizing LLMs, while LLMstudio offers a vendor-neutral gateway service for monitoring and cost management.

Many organizations also create custom dashboards using tools like Grafana or Tableau to visualize their LLM usage and costs. Tracking API usage is essential for managed LLM services, often done through the provider's dashboard or via custom tracking solutions.

New players like DigitalEx are developing specialized solutions for LLM cost management, offering features such as unified views of expenses across multiple LLM vendors, detailed cost allocation per team and application, advanced budgeting and forecasting, and analysis of cost-performance trade-offs.

As the field of LLM FinOps evolves, we can anticipate the emergence of more tailored tools to address the specific challenges of managing and optimizing LLM costs. Currently, organizations rely on a mix of existing cloud cost management tools, custom solutions, and emerging LLM-specific platforms. There is a pressing need for more capable tools to effectively manage LLM costs.

What do we expect?

How do we foresee the evolution of FinOps tools and practices for Large Language Models (LLMs) in the future?

As LLMs gain prominence, we can expect the development of more specialized FinOps tools tailored specifically for LLM use cases. These tools will focus on the unique cost structures and resource requirements associated with LLMs, offering features like improved mechanisms for tracking costs linked to specific models, teams, and projects, and usage forecasting through advanced algorithms that analyze usage patterns.

As organizations adopt multiple LLM providers, there will be a demand for cross-platform cost management solutions that offer unified dashboards for monitoring and managing costs across various LLM vendors, as well as comparative analyses for evaluating costs and performance between different LLM providers to aid informed decision-making.

With the growing reliance on APIs for LLM services, we can expect enhanced tools for managing API usage and costs, enabling more granular tracking of API calls and their associated costs, allowing engineering teams to optimize usage and reduce unnecessary expenses.

Final thoughts

The integration of FinOps practices into Large Language Model (LLM) applications is no longer optional; it has become essential for organizations aiming to deliver on the ROI of their AI investments. As discussed, LLMs hold immense potential to transform business processes and drive revenue, but this potential comes with significant challenges in managing operational costs.

The complexity of cloud environments and the non-deterministic nature of LLM resource usage complicate financial management, making it imperative for organizations to adopt a strategic approach. FinOps provides a framework for collaboration across finance, engineering, and business teams, promoting accountability and transparency in cloud spending. By leveraging FinOps, organizations can gain valuable insights into their LLM expenditures, enabling them to optimize resource allocation and align their AI initiatives with broader business objectives.

Looking ahead, we can expect a more robust tooling landscape specifically designed for LLM FinOps. As the industry matures, specialized tools will emerge to address the unique cost structures and operational needs associated with LLMs. This evolution will empower organizations to better manage their AI investments, ultimately unlocking the full potential of LLM technology.

Now is the time for organizations to act. Embrace FinOps to transform your LLM initiatives from costly experiments into strategic assets that drive measurable business outcomes. Taking practical steps to optimize your cloud spending will help ensure that your LLM projects yield value and contribute to your overall business success.