EDITOR’ S QUESTION
you send them. That means proprietary information could end up contributing to responses for a competitor using the same model. With self-hosted SLMs or private LLM deployments, CIOs retain full control over their data, preserving it as a competitive asset rather than commoditizing it. This control also simplifies compliance. Whether it’ s GDPR, HIPAA or AI regulations that continue to crystalize, enterprises need to know exactly where data lives, how it’ s used and who has access. Private models offer a cleaner path to compliance, especially for industries like healthcare, finance and government, where data governance is non-negotiable.
Importantly, choosing private infrastructure doesn’ t mean sacrificing performance. Today’ s SLMs are surprisingly capable, especially when fine-tuned with domain-specific data. And thanks to advances in tooling and optimization, many enterprises can run them without dedicated AI engineering teams. With the right framework in place, even modest IT departments can begin experimenting and delivering results.
Of course, some applications will still benefit from the scale and flexibility of LLMs. Use cases like multilingual chatbots, creative content generation or multi-document analysis across unstructured data sets may require larger
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