Most vector database pilots fail at ingest, not at search. You build a clever retrieval pipeline, you watch it work on a thousand documents, and then someone hands you fifty million rows. The next two weeks disappear into rate limits, partial failures, and three rewrites of your batch logic.
This post is the guide I wish I had the first time I imported a real dataset into Weaviate. It covers server-side batching, error handling, the data type decisions that cost you the most if you get them wrong, and how to ingest media and PDFs without standing up an OCR pipeline.
Try this as you readThe fastest way to follow along is a free Weaviate Cloud trial paired with Weaviate Embeddings. No infrastructure to manage, no embedding API keys, free vectorization on the trial. Every code snippet below runs against that setup without modification.
The ingest problem nobody warns you about
A working prototype tells you nothing about what happens at scale. The problems that bite production teams almost never show up in tutorials.
