Why building your own RAG stack can be a costly mistake

Many companies investing in building in-house generative AI systems face significant challenges, such as increased costs, security risks, and inefficiencies, compared to using established RAG (Retrieval-Augmented Generation) services. DIY RAG systems often struggle with issues like hallucinations, compliance failures, vendor management complexities, upkeep demands, scaling costs, high latency, and multi-language support difficulties. These challenges divert focus from core business objectives and can lead to user dissatisfaction and legal troubles. On the other hand, RAG-as-a-service platforms offer comprehensive, scalable, and secure solutions that mitigate these issues, allowing businesses to benefit from advanced AI capabilities without the associated risks and resource investments. By leveraging these services, companies can focus on delivering value and maintaining competitive advantages, sidestepping the pitfalls of developing and managing proprietary systems.