langchain-cost-tuning

LangChain Cost Tuning

Contents

• Overview
• Prerequisites
• Instructions
• Output
• Error Handling
• Examples
• Resources

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

• OpenAI Pricing
• Anthropic Pricing
• tiktoken

Next Steps

Use langchain-reference-architecture for scalable production patterns.