AIRouter's intelligent load balancing achieves optimal distribution of tasks by dynamically evaluating model performance and cost. Its core mechanism is as follows:
- Assessment of indicators: Synthesize response time, invocation cost, and task success to update model priorities in real time.
- strategic pattern: Three selection strategies are supported:
– fast_first: Prioritize the fastest responding models for real-time demanding scenarios.
– cost_first: Select the least costly model, suitable for budget-sensitive projects.
– balanced: Balancing speed and cost, filtered by a Pareto-optimal algorithm. - implementation method: The developer can be reached through the
generatemethodologicalmodeparameter to specify a policy, or use thegenerate_fromTHEbestAutomatically selects from a list of candidate models.
For example, callingmode="cost_first"When it does, the system prioritizes low-cost models such as Anthropic or DeepInfra.
This answer comes from the articleAIRouter: Intelligent Routing Tool for Calling Multiple Models with Unified API InterfaceThe
May not be reproduced without permission:AI productivity tools " How does AIRouter implement intelligent load balancing? What selection strategies are supported?
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