Cohere Multi Cloud Independence

Cohere is selling independence from the hyperscaler stack as much as it is selling model quality. For a bank, government agency, or large manufacturer, that means the model can run in AWS, Google Cloud, a private cloud, or inside the company’s own data center, so sensitive data does not have to be moved into a rival cloud vendor’s managed AI environment. That flexibility turns deployment choice into a buying reason, not just a technical detail.

• This is a direct contrast with OpenAI’s enterprise path, where building with GPT models has historically pulled customers toward Azure based workflows. Cohere’s pitch is that the same core models and apps can sit on whichever infrastructure the customer already trusts and has approved.
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• The deployment model changes the business model too. Cohere shifted toward private cloud and on premises deals in regulated industries, with about 85% of revenue tied to private deployments, typically sold as multi year software licenses to customers like Oracle, RBC, Fujitsu, and LG. Customers run the models on their own infrastructure, which lets Cohere avoid owning as much inference capacity itself.
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• This flexibility now shows up in product and distribution. North is positioned like an enterprise AI assistant that can be privately deployed in secure customer environments, and recent partnerships with SAP and Dell extend that into both enterprise software and on premises hardware. In practice, the strategy is to meet the customer where their data already lives.
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Going forward, multi cloud and on premises support should become even more central as enterprises push for sovereign AI and tighter control over where data is stored and processed. That favors providers like Cohere and Mistral that can fit into existing infrastructure, while pushing more cloud tied model vendors to prove that convenience matters more than control.