Many AI applications begin with one provider and then need more flexibility: fallback models, model experiments, or different models for different product workflows.
RouterBase gives developers an OpenAI-compatible API shape at https://routerbase.com/v1, so the first experiment can stay small: change the base URL, set a RouterBase API key, and choose a model id.
Minimal curl request
curl https://routerbase.com/v1/chat/completions \
-H "Authorization: Bearer $ROUTERBASE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "google/gemini-2.5-flash",
"messages": [
{ "role": "user", "content": "Explain RouterBase in one sentence." }
]
}'
Node.js example
const response = await fetch("https://routerbase.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.ROUTERBASE_API_KEY}`,
"Content-Type": "application/json"
},
body: JSON.stringify({
model: process.env.ROUTERBASE_MODEL || "google/gemini-2.5-flash",
messages: [
{ role: "user", content: "Draft a short product update." }
]
})
});
if (!response.ok) {
throw new Error(`RouterBase request failed: ${response.status}`);
}
console.log(await response.json());
Practical rollout advice
Start with a low-risk workflow, keep the model id configurable, and compare output quality, latency, cost, and error rate before routing more traffic through the new setup.
Good first workflows:
- Internal release note drafts
- Support ticket summaries
- Model comparison prompts
- Non-critical classification tasks
Useful links
- RouterBase
- RouterBase docs: https://docs.routerbase.com/
- npm quickstart package: https://www.npmjs.com/package/routerbase-quickstart
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