Relying solely on black box models accessible through an API is no longer ideal for mission-critical and commercial applications. Organizations must ascertain model ownership and differentiate their product from competitors, while retaining their AI and data intellectual property. According to a recent survey by my company, three-quarters of respondents would not be comfortable using a commercial LLM in production. These respondents cited ownership, privacy, and cost as their primary concerns.
Ensuring compliance and privacy remains paramount, and germany whatsapp number data developers are faced with the challenge of verifying that end-user data is protected from malicious entities when passed into a black box system. Moreover, reliance on third-party platforms raises concerns about latency and maintaining production-grade service-level agreements for commercial applications (SLAs). Finally, business leaders increasingly see AI as core to their IP, and they increasingly see customized models with proprietary data as a key differentiator that will set them apart from competitors. Put simply, enterprises are no longer happy with the idea of entrusting intellectual property to a third party and being just a thin layer on top of someone else’s API.
Once considered lacking in performance, open-source models have experienced a remarkable transformation through fine-tuning, and they are now emerging as powerful contenders. Fine-tuned open-source models are now meeting, if not exceeding, commercial models’ level of performance, while retaining a substantially smaller footprint.