langchain-cost-tuning
LangChain Cost Tuning
Contents
Overview
Strategies for reducing LLM API costs while maintaining quality in LangChain applications through model tiering, caching, prompt optimization, and budget enforcement.
Prerequisites
- LangChain application in production
- Access to API usage dashboard
- Understanding of token pricing
Instructions
Step 1: Track Token Usage and Costs
Implement a CostTrackingCallback that records input/output tokens per request and estimates cost based on model pricing.
Step 2: Optimize Prompt Length
Use tiktoken to count tokens and truncate long prompts. Summarize lengthy context with a dedicated chain when it exceeds the token budget.
Step 3: Implement Model Tiering
Route simple tasks to cheap models (gpt-4o-mini at $0.15/1M tokens) and complex tasks to powerful models (gpt-4o at $5/1M tokens) using RunnableBranch.
Step 4: Enable Response Caching
Use RedisSemanticCache with high similarity threshold (0.95) to avoid duplicate API calls for similar queries.
Step 5: Set Budget Limits
Implement a BudgetLimitCallback that tracks daily spend and raises RuntimeError when the budget is exceeded.
See detailed implementation for complete callback code and pricing tables.
Output
- Token counting and cost tracking
- Prompt optimization utilities
- Model routing for cost efficiency
- Budget enforcement callbacks
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Cost overrun | No budget limits | Enable BudgetLimitCallback |
| Cache misses | Threshold too high | Lower similarity to 0.90 |
| Wrong model selected | Routing logic error | Review task classification |
Examples
Basic usage: Apply langchain cost tuning to a standard project setup with default configuration options.
Advanced scenario: Customize langchain cost tuning for production environments with multiple constraints and team-specific requirements.
Resources
Next Steps
Use langchain-reference-architecture for scalable production patterns.