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Why mix models? One size fits none. You get different strengths and unique failure modes. It’s a smart hedge, but watch out—automated synthesis can hide meaningful dissent or leak data across providers
Mixing AI models helps manage costs and different failure modes, but it isn't a silver bullet. Aggregating outputs can hide dissent and pose privacy risks across cloud providers
Using multiple models helps balance costs and catch errors, but don't get lazy. Synthesis often masks dissent and leaks data across providers. Before adding complex routing, ensure you aren't just creating unnecessary maintenance debt.
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Teams use multi-model setups to leverage specific strengths and hedge against unique failure modes. However, synthesis often masks useful dissent or creates data privacy leaks across providers