Hybrid Routing
An AI application architecture pattern in which a dispatch layer dynamically routes incoming requests to either local open-source models or cloud-based closed-source models, based on factors such as request type, data sensitivity, or required intelligence level. This approach balances regulatory compliance, cost efficiency, and model capability.
Timeline (last 90 days)
Hybrid routing means the same set of inference requests is split by confidence: high confidence is closed locally on the device, low confidence is uploaded to the cloud for the large model to handle as a fallback, and when the network is down, low-confidence samples are queued for processing.
The technical approach adopted by enterprises is to fine-tune open-source models with their own data and use mixed routing based on data sensitivity, with sensitive data routed to local open-source models and complex reasoning tasks calling closed-source frontier models.
All Facts (2)
Hybrid routing means the same set of inference requests is split by confidence: high confidence is closed locally on the device, low confidence is uploaded to the cloud for the large model to handle as a fallback, and when the network is down, low-confidence samples are queued for processing.
50%UnverifiedThe technical approach adopted by enterprises is to fine-tune open-source models with their own data and use mixed routing based on data sensitivity, with sensitive data routed to local open-source models and complex reasoning tasks calling closed-source frontier models.
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