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Helicone OSS LLM Observability

How to Build a Multi-Model AI Assistant with Vercel AI Gateway and Helicone

Build a customer support assistant that switches between AI models based on query complexity while tracking costs
2 min read

Build a Multi-Model AI Assistant with Cost Tracking#

This guide shows you how to build a customer support assistant that intelligently routes queries to different AI models based on complexity, using Vercel AI Gateway for model access and Helicone for cost tracking and analytics.

Prerequisites#

Setup#

Install the required packages:

Create the AI Client#

Set up a client that routes through Helicone for monitoring:

Classify Query Complexity#

Use gpt-4o-nano with tool calling for precise classification:

Route to Appropriate Model#

Use different models based on query complexity to optimize costs:

Implement Response Caching#

Cache all queries regardless of complexity for maximum cost savings:

Complete Support System#

Here's the full implementation:

Monitor Performance#

View your assistant's performance in Helicone:

  1. Cost Analysis: Compare costs across different models
  2. Response Times: Monitor latency by model and complexity
  3. Cache Hit Rate: Track savings from cached responses
  4. User Analytics: See which customers need the most support
Helicone dashboard showing model usage and costs

Optimize Based on Data#

Use Helicone's analytics to:

  • Identify common queries for caching
  • Adjust model selection thresholds
  • Track cost per ticket complexity
  • Monitor customer satisfaction by model

Next Steps#