Multi-Model Routing Strategy

APIQIK node-level failover addresses availability for the same model. Application-level multi-model strategies balance task quality, speed, and cost.

Two different kinds of routingCopy Anchor Link

Platform node routingCopy Anchor Link

• Performed automatically on the server.
• Aims to improve request availability.
• Requires no developer-maintained nodes.
• Keeps the response structure compatible.

Business model routingCopy Anchor Link

• Chosen by the application for each task.
• Balances quality, latency, and cost.
• Requires evaluation and gradual rollout.
• Must account for model capability differences.

Simple selectorCopy Anchor Link

def choose_model(task):
    if task["requires_complex_reasoning"]:
        return "deepseek-chat"
    if task["latency_sensitive"]:
        return "qwen-turbo"
    return "qwen-turbo"

model = choose_model({
    "requires_complex_reasoning": False,
    "latency_sensitive": True,
})

This only illustrates a policy. Configure production routing from your own evaluations rather than hard-coding unverified model preferences.

Degradation orderCopy Anchor Link

1. Reduce unnecessary output length or disable supplementary generation.
2. Queue low-priority requests or return cached results.
3. Switch to a fallback model evaluated on the same task.
4. Fail explicitly if output quality cannot be assured instead of returning unvalidated results.

Evaluation and rolloutCopy Anchor Link

• Maintain fixed evaluation datasets and scoring scripts for repeatable comparisons.
• Account for extra cost and data-processing scope when issuing shadow requests.
• Assign users or sessions to stable rollout groups to avoid frequent style changes within a conversation.
• Include error rates, latency, cost, and business quality in rollback criteria.