How to Reduce 78%+ of LLM Costs: Proven Strategies for AI Startups

Discover proven strategies to reduce 78%+ of LLM costs for AI startups. Learn how to optimize model selection, reduce token usage, and leverage techniques like model cascading and LLM routers. Get insights from real-world examples to boost your AI product's profitability.

July 18, 2024

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Discover the real cost of using large language models (LLMs) and learn effective strategies to reduce your costs by up to 78%. This blog post provides practical insights and techniques to optimize your AI application's performance and profitability, drawing from the author's hands-on experience in building AI-powered sales agents and companion apps.

Reducing the Cost of Large Language Model Applications through Smarter Model Selection

The best way to reduce the cost of large language model applications is not only through technical know-how, but also a deep understanding of the business workflow. By analyzing the actual needs and data requirements, you can choose the most suitable models and optimize the input/output to significantly reduce the overall cost.

Here are the key tactics to consider:

  1. Change Models: Leverage the cost differences between various language models. For example, GPT-4 is around 200 times more expensive than Minstrel 7B. Start with a powerful model like GPT-4 to launch your initial product, then use the generated data to fine-tune smaller models like Minstrel or LLaMA for specific tasks. This can deliver over 98% cost savings.

  2. Model Cascading: Implement a cascade of models, using cheaper smaller models first to handle simple requests, and only invoke the more expensive powerful models like GPT-4 for complex queries. This can leverage the dramatic cost differences between models.

  3. Large Language Model Routing: Use a cheaper model to classify the request complexity, then route it to the appropriate specialized model for execution. This allows you to leverage the strengths of different models while optimizing costs.

  4. Multi-Agent Architecture: Set up multiple agents with different models, allowing cheaper models to handle requests first. Save successful results in a database to leverage for future similar queries.

  5. Prompt Engineering: Reduce the token input and output by using smaller models to preprocess and extract only the relevant information before passing it to the expensive model. This can lead to 20-175x reduction in token consumption.

  6. Memory Optimization: Optimize the agent's memory usage by using techniques like conversation summary instead of keeping the full history. This prevents the token consumption from growing infinitely.

By combining these techniques, you can often achieve 30-50% cost reduction for your large language model applications without sacrificing performance or user experience. Continuous monitoring and optimization are key to managing these dynamic costs effectively.

Leveraging Prompt Engineering and Memory Optimization to Minimize Token Consumption

The key to reducing large language model (LLM) costs lies in two main strategies: 1) Choosing the right model for the task, and 2) Optimizing the input and output to minimize token consumption.

Choosing the Right Model

  • Compare the costs between powerful models like GPT-4 and smaller models like Mistra 7B. GPT-4 can be 200x more expensive per paragraph.
  • Start with a powerful model like GPT-4 to launch your initial product, then use the generated data to fine-tune smaller models for specific tasks. This can deliver over 98% cost savings.
  • Explore model cascading, where cheaper models are used first, and only escalate to more expensive models if needed. This leverages the dramatic cost differences between models.
  • Implement a large language model router that can classify requests and route them to the most appropriate model.

Optimizing Input and Output

  • Use smaller models to preprocess and summarize data before passing it to expensive LLMs. This "prompt engineering" can reduce token consumption by 175x or more.
  • Optimize agent memory by using techniques like conversation summary memory instead of keeping the full chat history. This prevents memory from growing infinitely.
  • Monitor and analyze costs using tools like Anthropic's Langchain. This allows you to identify the most expensive components and optimize accordingly.

By combining model selection and input/output optimization, you can achieve 50-70% reductions in LLM costs without sacrificing performance. Continuously monitoring and iterating on these techniques is key to building cost-effective AI applications.

Monitoring and Analyzing Large Language Model Costs with Tools like Anthropic's Lantern

Observability is critical for building AI products and understanding the costs associated with large language models. Tools like Anthropic's Lantern can help you monitor and analyze where the costs occur in your AI applications.

Here's a step-by-step example of how to use Lantern to optimize the costs of a research agent:

  1. Install the necessary packages: Install the deta and openai packages, which include the Lantern SDK.

  2. Set up environment variables: Create an .env file and define the required environment variables, including your Lantern tracing key, Lantern endpoint, and OpenAI API key.

  3. Instrument your code: Wrap the functions you want to track with the @traceable decorator from the Lantern library.

  4. Run your application: Execute your Python script, and the Lantern SDK will start logging the execution details, including the time taken and the token consumption for each function call.

  5. Analyze the cost breakdown: In the Lantern dashboard, you can see the detailed breakdown of the token consumption for each large language model used in your application. This information can help you identify the areas where you can optimize the costs.

  6. Implement cost-saving strategies: Based on the Lantern insights, you can implement various strategies to reduce the large language model costs, such as:

    • Swapping to a less expensive model (e.g., GPT-3.5 Turbo instead of GPT-4)
    • Implementing a model cascade or router to use the most appropriate model for each task
    • Optimizing the prompts and reducing the token input to the large language models
  7. Iterate and monitor: Continuously monitor the costs using Lantern and make adjustments to your application to further optimize the large language model usage and costs.

By using tools like Lantern, you can gain visibility into the large language model costs in your AI applications and make informed decisions to balance performance and cost-effectiveness.

Conclusion

In this article, we explored various techniques to reduce the cost of large language model (LLM) usage in AI applications. The key takeaways are:

  1. Model Selection: Carefully choose the right model for each task, as the cost can vary significantly between models like GPT-4 and smaller models like Mistra 7B.

  2. Model Cascading: Use a cascade of models, starting with cheaper ones and only escalating to more expensive models if needed, to optimize costs.

  3. Model Routing: Leverage model routing techniques like Hugging Face's Hugging GPT to route requests to the most appropriate model based on the task complexity.

  4. Prompt Engineering: Optimize the prompts and inputs sent to LLMs to reduce the number of tokens consumed, using techniques like LLM Lingua from Microsoft.

  5. Agent Memory Management: Optimize the agent's memory usage by using techniques like conversation summary memory instead of keeping the full conversation history.

  6. Observability and Monitoring: Use tools like L Smith to monitor and analyze the cost breakdown of LLM usage in your application, which is crucial for identifying optimization opportunities.

By applying these techniques, you can significantly reduce the LLM costs in your AI applications while maintaining the desired performance and user experience.

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