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167 points xnx | 2 comments | | HN request time: 0.437s | source
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dsjoerg ◴[] No.44527801[source]
We used the previous version of this batch mode, which went through BigQuery. It didn't work well for us at the time because we were in development mode and we needed faster cycle time to iterate and learn. Sometimes the response would come back much faster than 24 hours, but sometimes not. There was no visibility offered into what response time you would get; just submit and wait.

You have to be pretty darn sure that your job is going to do exactly what you want to be able to wait 24 hours for a response. It's like going back to the punched-card era. If I could get even 1% of the batch in a quicker response and then the rest more slowly, that would have made a big difference.

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cpard ◴[] No.44527819[source]
It seems that the 24h SLA is standard for batch inference among the vendors and I wonder how useful it can be when you have no visibility on when the job will be delivered.

I wonder why they do that and who is actually getting value out of these batch APIs.

Thanks for sharing your experience!

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1. jampa ◴[] No.44528329[source]
> who is actually getting value out of these batch APIs

I used the batch API extensively for my side project, where I wanted to ingest a large amount of images, extract descriptions, and create tags for searching. After you get the right prompt, and the output is good, you can just use the Batch API for your pipeline. For any non-time-sensitive operations, it is excellent.

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2. cpard ◴[] No.44528924[source]
What you describe makes total sense. I think that the tricky part is the "non-time-sensitive operations", in an environment where even if you don't care to have results in minutes, you have pipelines that run regularly and there are dependencies on them.

Maybe I'm just thinking too much in data engineering terms here.