Building an LLM Gateway That Learns Which Model to Use
The article describes a platform that acts as a gateway for using various large language models (LLMs). It automatically detects the task type and complexity, and selects the most appropriate model to use based on quality feedback and continuous learning.
Why it matters
This platform provides a flexible and intelligent way to leverage multiple LLMs, optimizing performance and cost for different use cases.
Key Points
- 1Adaptive router selects the best-performing LLM for each task
- 2Provides features like request logging, analytics, A/B testing, and data security
- 3Supports multiple LLM providers like OpenAI, Anthropic, Google, and more
- 4Learns which model to use for each task type through quality feedback
Details
The platform works by receiving requests at an OpenAI-compatible endpoint, detecting the task type and complexity using a classifier, and then routing the request to the highest-scoring LLM model for that task. It continuously improves the routing through quality feedback from user ratings and an LLM-based judge. Over time, the router stops sending complex prompts to the cheapest model and starts selecting the best-performing one. The platform also offers additional features like request logging, time-series analytics, A/B testing, PII redaction, and prompt template versioning. It can be self-hosted with Docker, and supports multiple LLM providers including OpenAI, Anthropic, Google, and others.
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