Making Large-Scale LLM Processing Practical and Affordable

Data teams constantly need to apply LLMs to large datasets for classification, extraction, and scoring, but naive approaches (one API call per row) quickly become prohibitively expensive and fragile. Ondine solves this critical gap by combining checkpointing for reliability, cost controls for budget safety, efficient batching to reduce API calls, and structured output validation. This matters because it brings production-grade LLM data processing within reach for teams without specialized ML infrastructure.