Batch-Processing 100K Rows with LLMs Without Losing Your Mind (or Money)
The article describes the author's experience building a tool called Ondine to efficiently batch-process large datasets using large language models (LLMs) without running into cost and performance issues.
Why it matters
Ondine provides a practical solution for efficiently batch-processing large datasets using LLMs, addressing common issues like cost, performance, and data quality.
Key Points
- 1The author had a 158,000-row product catalog that needed structured attributes extracted from free-text descriptions
- 2Initial attempts using a simple for-loop and GPT calls per row were inefficient and costly
- 3Ondine is a Python SDK that allows you to map LLM prompts over tabular data and collect structured results
- 4Key features include checkpoint/resume, cost control, structured output, multi-row batching, and anti-hallucination
- 5Ondine is not an agent framework or a RAG pipeline builder, but a batch processor for tabular data
Details
The author had a large product catalog with 158,000 rows, each containing a free-text description that needed to be processed to extract structured attributes like brand, category, and sentiment. Initial attempts using a simple for-loop and GPT calls per row were inefficient and costly, crashing at row 91,000 and costing $400. This led the author to build a tool called Ondine, a Python SDK that allows you to map LLM prompts over tabular data and collect structured results. Ondine includes key features like checkpoint/resume to handle crashes, cost control to set a hard budget cap, structured output to enforce data schemas, multi-row batching to improve throughput, and an anti-hallucination layer to ensure outputs are grounded in the input data. Ondine is not an agent framework or a RAG pipeline builder, but a specialized batch processor for tabular data that addresses common pain points faced by data engineers working with LLMs.
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