FinOps TechniquesFinOps Techniques
Artificial Intelligence and Machine Learning

11 Proven FinOps Techniques to Keep Cloud Bills in Check

Explore the top contributors to Gen AI costs and proven techniques to optimize your spending.
FinOps TechniquesFinOps Techniques
Artificial Intelligence and Machine Learning
11 Proven FinOps Techniques to Keep Cloud Bills in Check
Explore the top contributors to Gen AI costs and proven techniques to optimize your spending.
Table of contents
Table of contents
Introduction
What is a Large Language Model?
Top 4 Cost Drivers in LLM Development
11 Proven FinOps Strategies to Optimize LLM Costs
6 Real-World Case Studies Using FinOps for LLM Cost Optimization
Conclusion
FAQs

Introduction

Large Language Models (LLMs) have revolutionized the tech landscape, powering breakthroughs in natural language understanding, content generation, and enterprise automation. 

Organizations now use LLMs to reimagine customer service, accelerate R&D, and create hyper-personalized experiences. However, this innovation comes with a steep price. Businesses often underestimate these expenses until cloud bills skyrocket within months. 

A Vanson Bourne study commissioned by Tangoe found that Cloud spending is up 30% on average, mainly due to the Adoption of Generative AI and traditional AI technologies. Additionally, 72% of IT and financial leaders agree that GenAI-driven cloud spending is becoming unmanageable.

This blog examines the key cost drivers associated with LLM development. It provides practical strategies, ranging from implementing FinOps principles to leveraging more innovative workload management, to ensure sustainable and cost-effective LLM development.

What is a Large Language Model?

“Large Language Models or LLMs are a type of Artificial Intelligence program that are trained on massive data sets, helping them understand and generate content, amongst other tasks.” 

LLMs have become extremely popular since the introduction of generative AI to the forefront of public interest. Its influence is such that almost all businesses across the globe are planning to adopt AI across business functions and use cases.

Cloud Vs On-Premise LLMs

On-premise LLMs require servers and other infrastructure that necessitate timely maintenance. Cloud-hosted LLMs eliminate the need for costly infrastructure.

While on-premise deployments offer increased control over data, they often don’t provide the scalability and flexibility of cloud solutions. Additionally, cloud-based LLMs are well-suited for businesses that require agility, as they operate on a pay-as-you-go model. 

Top 4 Cost Drivers in LLM Development

LLMs are primarily categorized into two types: Open-Source LLMs and Managed LLMs. Each offers specific benefits. However, it’s essential to examine the various costs associated with creating LLMs. This plays a critical role in LLM cost optimization.

Here are the top costs associated with building LLMs.

1. Direct Costs

Direct charges depend on an LLM's token usage. A critical factor to consider for this is the inference. For instance, OpenAI charges $0.03 for 1000 inputs and $0.06 for 1000 outputs for GPT-4, which is significantly higher than the GPT-3.5 model.

Choosing the right deployment model here offers scalability and better workload predictability. Here are the top deployment models for you to choose from.

  • API Access: API access offers ease of use with scalability. However, the variability depends on the usage-based costs.
  • In-House Deployment: In-house deployment is costlier due to cloud computing, GPU investments, and other infrastructure costs. Operational costs also add up over time, which can disrupt your cost-optimization strategies.

2. Indirect Costs

Customizations account for most of the indirect costs with LLMs. Here are the top indirect costs involved with the LLM development process.

  • Fine-Tuning: Fine-tuning an LLM demands significant computational resources. There will be an increased need for high-quality data assets to accommodate the required customizations. The customizations may also demand additional time from engineers.
  • Integration: Backend development and API integration are the primary focus of this project. Additionally, you may need to incorporate extra security features to ensure compliance with relevant regulations. To maintain the model’s efficiency, the integrations must be aligned with the existing models.

Customization costs may vary depending on the frequency of updates and the complexity of deployment.

TOP 4 COST DRIVERS IN LLM DEVELOPMENT

The key contributors to operational costs include latency, scalability, and inference. With an increase in user demand, operational costs will need to be optimized according to business requirements. At the same time, it’s necessary to look for techniques that would reduce LLM operational costs.

LLMs incur higher cloud costs over time, demanding more computing power and increased latency. Here are some key factors that can hamper your operational expenses.

  • Auto-Scaling: Auto-scaling is most suitable for handling unexpected spikes. Consider dynamic scaling for this situation, as it offers load balancing while keeping up with infrastructural complexity.
  • Real-Time Constraints: Due to low-latency responses, chatbot apps may present some real-time constraints, which resultantly increase computing demands.

4. Hidden Costs

Companies generally overlook certain hidden costs when considering LLM cost optimization. These can significantly increase your long-term spending and impact your total cost of ownership of the LLM.

Consider the following costs to avoid surprises in the long run.

  • Security Risks: LLMs possess many security risks. They are prone to data misuse, data leaks, and cyberattacks. You must conduct timely security audits to mitigate these risks and avoid reputational damage.
  • Compliance: Your LLM must always adhere to all the regulatory requirements. Adhering to laws like GDPR and CCPA can increase your operational and legal costs, but it is essential.
  • Model Drift: Selecting a cost-effective LLM model is critical. Ensure relevance by regularly fine-tuning these models to maintain their accuracy and effectiveness. As data is an evolving entity, it’s essential to consider a model drift that drives cost savings.

11 Proven FinOps Strategies to Optimize LLM Costs

Optimizing the cost of LLMs demands a multi-faceted approach. Here are the top 11 FinOps strategies you can leverage to reduce expenses with LLMs.

1. Tailored Chunking

LLMs process data in ‘Chunks’. This could increase costs while impacting accuracy. Opting for default chunking methods that often include overlaps results in inefficiencies, adding to latency and expenses.

Here’s how one can mitigate this challenge:

  • Customized Chunking: Curate a chunking process based on different types of content and what users typically ask. This decreases content size, aiding resource utilization. 
  • Thoughtful Chunking: Ensure each chunk is meaningful, aligned with the overall task, and incorporable into the logical structure of the content. This would lower costs, decreasing the total number of process tokens.

2. Efficient Caching

Costs can add up unexpectedly if LLMs process repeated interactions. It’s suggested that one leverages semantic caching, which would help store and retrieve frequently shared responses.

Let’s observe some tools and techniques that can help solve this problem.

  • Utilize GPTCache: Tools like GPTCache can help save common responses, improve response times, and reduce LLM calls. 
  • Langchain Caching: Langchain offers numerous LLM tools that can optimize performance and reduce costs after integration with your LLM system.

3. Optimizing Search Space

Passing a varied range of contexts to LLMs without relevant filtering can increase computational costs while decreasing accuracy. Optimizing the search space ensures only relevant information is processed.

Here are some techniques that can help you optimize your search space.

  • Metadata Filtering: Before passing the context to the LLM, one can narrow down the search space with metadata filtering.
  • Re-Ranking Models:  Reduce the computational load on LLMs by prioritizing the most relevant chunks with re-ranking models.

4. Concise Summarization

With LLMs, conversational interactions can directly affect the costs by accumulating tokens. One can retain essential context and minimize token usage by summarizing chat histories.

Let’s observe some practices that can help this cause.

  • Model Summarization: Implement smaller language models (SLMs) or budget-friendly LLMs to create concise summaries from lengthy chats. 
  • Token Reduction: Optimize resource usage, particularly when handling multiple question-answer pairs, by summarizing chats before exceeding your token limit.

5. Prompt Compression

The introduction of prompting techniques, such as chain-of-thought (CoT) and in-context learning (ICL), has increased prompt lengths, leading to higher API costs and computational requirements.

Here is a technique to mitigate this challenge.

  • LLMLingua: A tool like LLMLingua can retain the effectiveness of prompts while compressing them by up to 20x, especially with reasoning tasks. It enables inference from smaller prompts by eliminating unnecessary tokens using an SLM.

6. Selecting the Right Model

LLM costs are primarily dependent on the model you choose. Though LLMs are highly useful, they can disrupt your budgets, especially if you implement large models instead of SLMs.

  • Analyzing Use Cases: Examine your specific use cases to determine whether an LLM or an SLM is more suitable.
  • Model Analysis: When choosing between a SaaS or open-source model, analyze factors such as usage patterns, operational costs, and data security.
11 Proven FinOps Strategies to Optimize LLM Costs

7. Model Mimicking

One can achieve apt performance while reducing computational costs by training a smaller model to mimic the outputs of a larger model.

Here’s a technique that can help you achieve this.

  • Google’s Stepwise Distillation: The effectiveness of distillation, combined with cost reduction, was demonstrated when a small model with 770 million parameters outperformed a larger model with 540 billion parameters.

8. Strategic Fine-Tuning

Offering few-shot examples in prompts can be costly, especially in complex use cases. The need for these models can be eliminated by fine-tuning them for specific tasks.

Here is a list of strategies to do this.

  • Task-Specific Fine-Tuning: Decrease the number of tokens required per request by fine-tuning your model on specific and relevant use cases.
  • Eliminating Examples: Your model can offer high-quality outputs while minimizing costs by removing the need for multiple examples in prompts using fine-tuning. 

9. Model Compression

Deployment is often cumbersome as LLMs require high GPU computational costs. Leveraging techniques like quantization can enhance accessibility, consuming fewer resources and decreasing model size.

These are some tools you can use for this process.

  • Quantization Techniques: With tools like GPTQ and GGML, the size of model weights can be reduced, facilitating deployment on devices with limited resources, shrinking the model size.
  • Bitsandbytes Library: This tool optimizes models for budget-friendly deployments by quantization of LLMs.

10. Inference Optimization

If you want to maximize throughput, minimize latency while decreasing costs, it’s essential that you optimize LLM inferences.

Here’s how you can do it.

  •  vLLM and TensorRT: Tools like vLLM and TensorRT allow you to process more requests using the same hardware while enhancing inference speed and efficiency.
  • Hardware Usage: Ensure your hardware is utilized to its fullest potential to increase efficiency and decrease costs.

11. Custom Infrastructure

Cost optimization is a direct result of the infrastructure you choose for your LLM. One can only observe significant savings if their LLM’s infrastructure is tailored to their usage patterns.

Here are some strategies to choose the right infrastructure.

  • Usage-Specific Tailoring: Optimize infrastructure based on batch and real-time processing.
  • FinOps Implementation: Leverage Financial Operations (FinOps) strategies to sync cloud costs with LLM usage, ensuring efficient resource allocation.

6 Real-World Case Studies Using FinOps for LLM Cost Optimization

Here are some practical examples that showcase the implementation of cost optimization strategies for running LLMs.

  1. Spot Instances: Spot instances from AWS and Azure offer on-demand pricing for immediate needs. They handle interruptible workloads, such as batch processing or model training. Uber uses spot instances to train ML models while keeping the costs in check.
     
  2. Cloud FinOps: A Cloud FinOps infrastructure can help manage your AI spends. It offers numerous benefits, including monitoring, allocation, and LLM cost optimization. It also detects anomalies in real-time with spikes in AI model inference costs.
     
  3. Model Distillation: Amazon Bedrock’s distilled agent models, Llama 3.1 405B, when compared with Llama 3.2 3B, achieve a 72% reduction in latency and a 140% increase in output speed. The tool achieved this while offering a decent quality of functionality. 
     
  4. Snowflake’s SwiftKV with vLLM: Utilizing the self-distillation and KV cache reuse, SwiftKV from Snowflake AI research was able to reduce inference costs of Meta Llama LLMs up to 75% on Cortex AI. 
     
  5. SpotServe: SpotServe served LLMs on preemptible instances. The platform utilized spot VMs with dynamic graph parallelism to reduce significant costs while maintaining performance and minimizing downtime in the long run.
     
  6. AI-Based Hybrid Cloud Scaling: Hybrid cloud platforms use AI resource allocation for microservices. An RL-based microservices allocator reduced provisional costs by up to 40% while reducing latency and utilizing resources efficiently.

Conclusion

As organizations embrace LLMs to drive transformative AI solutions, FinOps emerges as the cornerstone for balancing innovation with financial sustainability. By bringing visibility, accountability, and collaboration across teams, FinOps ensures Gen AI workloads remain cost-efficient without hampering experimentation. 

It empowers businesses to scale LLM deployments confidently, optimizing compute, storage, and inference costs. With a strong FinOps culture, enterprises can unlock Gen AI’s full potential while adhering to their budgets.

Partner with Maruti Techlabs to accelerate your AI journey. Our expertise in Artificial Intelligence Services and Cloud Cost Optimization helps you innovate faster and smarter, without the surprise cloud bills. Connect today to future-proof your AI investments.

FAQs

1. What is cloud FinOps?

Cloud FinOps is a cultural and operational practice that introduces financial accountability to cloud spending. It enables cross-functional teams to collaborate on data-driven spending decisions, ensuring efficient resource utilization and cost optimization in cloud environments.

2. Which FinOps software is best for cloud cost optimization?

Top FinOps tools include Apptio Cloudability, Flexera, and Finout. The right software depends on your organization’s scale, multi-cloud needs, and integration requirements with existing financial and engineering workflows.

3. What is FinOps for AI?

FinOps for AI focuses on optimizing the costs of AI workloads, such as training large language models and running inference, by improving visibility, resource allocation, and spend efficiency, thereby ensuring innovation without unanalyzed cloud expenses.

4. What is the difference between AIOps and FinOps?

AIOps uses AI to automate IT operations, improving performance and uptime. FinOps manages cloud financials, fostering collaboration between finance, engineering, and operations to control costs. While AIOps focuses on operations, FinOps targets cloud spend governance.

5. What is the DoD cloud FinOps strategy?

The DoD’s cloud FinOps strategy emphasizes cost transparency, governance, and optimization across its multi-cloud environments. It aims to improve financial accountability, avoid waste, and align cloud spending with mission-critical outcomes efficiently.

6. What are the three pillars of FinOps?

The three pillars of FinOps are Inform (create visibility into cloud spend), Optimize (reduce waste and improve cost-efficiency), and Operate (establish processes for continuous financial governance and accountability).

Pinakin Ariwala
About the author
Pinakin Ariwala


Pinakin is the VP of Data Science and Technology at Maruti Techlabs. With about two decades of experience leading diverse teams and projects, his technological competence is unmatched.

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