Three Ways to Handle AI Model Routing in 2026
The article discusses three approaches to managing AI model routing: manual selection, self-hosting a routing layer, and using a managed routing service. It outlines the trade-offs and considerations for each approach.
Why it matters
Effective AI model routing is crucial as organizations increasingly leverage multiple AI models for various use cases. The trade-offs discussed in the article help teams make informed decisions about their routing strategy.
Key Points
- 1Manual model selection per request or endpoint
- 2Self-hosting a routing layer for full control and no markup
- 3Using a managed routing service for automated cost optimization
Details
The article explores three ways to handle the challenge of routing requests to the appropriate AI model when you have hundreds of models available. The first approach is manual model selection, where you explicitly choose the model per request or endpoint. This provides full control but requires constant maintenance as the model landscape changes. The second approach is self-hosting a routing layer, which allows you to define classification rules and route requests accordingly without paying a markup. However, this comes with operational overhead. The third approach is using a managed routing service, which automatically selects the cheapest capable model for each request based on your quality preferences. This offloads the routing logic but incurs a markup on model costs.
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