A simple way to test model fallbacks with RouterBase

RouterBase provides an OpenAI-compatible API that enables developers to test model fallback logic with a simple two-model chain. By using environment variables to configure primary and fallback models, teams can prototype fallback behavior before deploying to production. The approach allows teams to evaluate latency, cost, and output quality through structured logging.

Fallback logic is easier to reason about when the application has one request shape and the model choice stays configurable. That is especially useful for teams testing different AI providers, latency profiles, or cost envelopes. RouterBase https://routerbase.com gives developers an OpenAI-compatible API surface at https://routerbase.com/v1 , which makes it a good place to prototype fallback behavior before changing a larger production system. js const baseUrl = "https://routerbase.com/v1/chat/completions"; async function runPrompt model, prompt { const response = await fetch baseUrl, { method: "POST", headers: { Authorization: Bearer ${process.env.ROUTERBASE API KEY} , "Content-Type": "application/json" }, body: JSON.stringify { model, messages: { role: "user", content: prompt } } } ; if response.ok { throw new Error Model ${model} failed with ${response.status} ; } return response.json ; } async function runWithFallback prompt { const primary = process.env.ROUTERBASE PRIMARY MODEL || "google/gemini-2.5-flash"; const fallback = process.env.ROUTERBASE FALLBACK MODEL || "openai/gpt-4.1-mini"; try { return await runPrompt primary, prompt ; } catch primaryError { console.warn primaryError.message ; return runPrompt fallback, prompt ; } } Start with a workflow where a fallback is useful but not risky: For each run, record which model responded, whether fallback was used, total latency, and whether the output needed manual correction. That turns a routing experiment into something the team can evaluate instead of a vague model preference debate.