u/anirudh8398

We parse structured data out of job descriptions — around 20,000 JDs/day pulled from LinkedIn. Claude Opus does it beautifully, but at ~₹2.20 (~$0.026) per job that's ~₹44K/day, roughly $16K/month. Our unit economics just can't carry that.

What we've tried so far:

• Opus: quality is exactly what we need, but the cost is a dealbreaker at this volume.
• Sonnet: cheaper, but extraction quality drops noticeably on messier, unstructured JDs.
• DeepSeek: much cheaper again, but same story: quality isn't where Opus is.

So we're stuck between "great and unaffordable" and "affordable and not good enough."

For anyone who's run a narrow, high-volume extraction task in production:

• How did you bring the cost down without tanking quality?
• Did fine-tuning a small open model (Llama / Qwen, etc.) actually close the gap to Opus?

We parse structured data out of job descriptions — around 20,000 JDs/day pulled from LinkedIn. Claude Opus does it beautifully, but at ~₹2.20 (~$0.026) per job that's ~₹44K/day, roughly $16K/month. Our unit economics just can't carry that.

What we've tried so far:

• Opus: quality is exactly what we need, but the cost is a dealbreaker at this volume.
• Sonnet: cheaper, but extraction quality drops noticeably on messier, unstructured JDs.
• DeepSeek: much cheaper again, but same story: quality isn't where Opus is.

So we're stuck between "great and unaffordable" and "affordable and not good enough."

For anyone who's run a narrow, high-volume extraction task in production:

• How did you bring the cost down without tanking quality?
• Did fine-tuning a small open model (Llama / Qwen, etc.) actually close the gap to Opus?

How does Juicebox/Kello source profiles on their landing page? Juicebox mentions they have around 800 million profiles, and Kello mentions they have 100 million profiles. Do these companies do an initial round of candidate vetting?