LLM Routing for Cost and Latency

LLM routing is a layer that sits between an application and several language model providers, deciding which model handles each request instead of hardcoding a single provider into the product. It fits products that already call more than one model, or plan to, and need to control spend, response time, and output quality without rewriting application code every time a provider changes pricing or has an outage. FreyreSoft builds this layer to score incoming requests against rules or lightweight classifiers, then send each one to the model that fits its cost and latency budget, with cheaper or faster models handling simple requests and stronger models reserved for tasks that need them. The layer also monitors provider health and response times continuously, so it can fail over to a backup model automatically when a primary provider is slow, rate-limited, or unavailable, rather than surfacing an error to the end user.

Why does calling one model directly break down?

Products that depend on a single LLM provider inherit that provider's pricing, latency, and uptime as fixed constraints on the whole application. A model that is excellent for complex reasoning is often needlessly expensive for simple lookups or classification, so paying its per-token rate for every request wastes budget at scale. Latency is also uneven: some requests need a fast, lightweight response and others can tolerate a slower, higher-quality one, but a single hardcoded provider treats every request the same way. And when that provider has an outage, is rate-limited, or degrades in response time, the entire product goes down or slows to a crawl with it, because there is no fallback path built into the application to route around the problem automatically.

How does the routing layer work?

FreyreSoft implements routing as a thin service that sits in front of the LLM calls, classifying each incoming request by complexity, required capability, and latency tolerance before selecting a model, using either rule-based logic for well-defined request types or a lightweight classifier model for cases that vary more. Requests get scored against configurable budgets for cost and response time, and the routing table maps each score to a specific provider and model, so the same product can send simple summarization to a smaller, cheaper model and reserve a larger model for requests that genuinely need deeper reasoning. Health checks and response-time tracking run continuously against each provider, and when one degrades or errors out, the router retries against a configured fallback model rather than surfacing the failure to the user. The whole layer is built with observability from the start, logging which model handled each request, at what cost and latency, so routing rules can be tuned against real traffic instead of guesswork.

Technology we typically use

• Multi-provider LLM APIs
• Rule-based and ML classifiers
• Request-level cost tracking
• Latency and health monitoring
• Fallback and retry logic
• PyTorch for classifier models

What a project like this usually involves

1. Mapping request types to cost and latency budgets per provider
2. Building or training a lightweight request classifier
3. Implementing fallback and retry logic across providers
4. Adding per-request logging for cost, latency and model choice
5. Tuning routing rules against real production traffic

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