I Built an AI Pipeline for 10,000 Daily Listings. Here's What Broke at Scale.

I watched a pipeline I spent weeks building get shut down in one meeting. The AI rewrite engine for a job platform's listing descriptions was working. Output quality was solid. But at 10,000 listings a day, the API bill hit a number the client couldn't stomach. The pipeline went dark.

That moment taught me more about production AI than any tutorial ever did.

If you're an engineering lead or founder evaluating whether to build AI into your product, most of what you'll read skips the hard part. The architecture patterns. The cost math that makes or breaks a feature. The failure modes that only surface at scale.

Here's what I learned from shipping an LLM scoring pipeline that processes 10,000+ items daily, and where I almost got it wrong.

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