LlamaCloud addresses the limitations of relying solely on large language models (LLMs) for document processing, offering a hybrid approach that combines LLMs with advanced parsing techniques to improve accuracy and reduce costs. While frontier LLMs like GPT-4.1, Claude Sonnet 4.0, and Gemini 2.5 Pro have advanced capabilities, they often struggle with accuracy, metadata provision, and operational challenges at an enterprise scale, such as rate limits and content filtering. LlamaCloud enhances LLM capabilities by integrating layered text extraction, metadata provision, and vision models for layout reconstruction, offering a standardized schema interface and operational features like caching, deduplication, and cost optimization. This approach ensures the reliability and maintainability of document processing infrastructure, crucial for enterprise applications that require detailed metadata, consistency across teams, and robust handling of document workflows.