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Batch Mode in the Gemini API: Process More for Less
For those who aren't aware, OpenAI has a very similar batch mode (50% discount if you wait up to 24 hours): https://platform.openai.com/docs/api-reference/batch

It's nice to see competition in this space. AI is getting cheaper and cheaper!

DeepSeek has gone a bit different route - they give automatic 75% discount between UTC 16:30-00:30

https://api-docs.deepseek.com/quick_start/pricing

Yes, this seems to be a common capability - Anthropic and Mistral have something very similar as do resellers like AWS Bedrock.

I guess it lets them better utilise their hardware in quiet times throughout the day. It's interesting they all picked 50% discount.

Inference throughout scales really well with larger batch sizes (at the cost of latency) due to rising arithmetic intensity and the fact that it's almost always memory BW limited.
  • qrian
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  • 4 hours ago
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Bedrock has a batch mode but only for claude 3.5 which is like one year old, which isn't very useful.
The latest price increases beg to differ
What price increases?
  • rvnx
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  • 41 minutes ago
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I guess the Gemini price increase
Man googles offerings are so inconsistent, batch processing has been available on vertex for a while now, I dont really get why they have two different offering in vertex and gemini, both are equally inaccessible
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.

> 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.

You can do this, just send 1% using the regular API.

  • cpard
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  • 6 hours ago
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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!

It’s like most batch processes, it’s not useful if you don’t know what the response will be and you’re iterating interactively. It for data pipelines, analytics workloads, etc, you can handle that delay because no one is waiting on the response.

I’m a developer working on a product that lets users upload content. This upload is not time sensitive. We pass the content through a review pipeline, where we did moderation and analysis, and some business-specific checks that the user uploaded relevant content. We’re migrating some of that to an LLM based approach because (in testing) the results are just as good, and tweaking a prompt is easier than updating code. We’ll probably use a batch API for this and accept that content can take 24 hours to be audited.

  • cpard
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  • 1 hour ago
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yeah I get that part of batch, but even with batch processing, you usually want to have some kind of sense of when the data will be done. Especially when downstream processes depend on that.

The other part that I think makes batch LLM inference unique, is that the results are not deterministic. That's where I think what the parent was saying about some of the data at least should be available earlier even if the rest will be available in 24h.

Think of it like you have a large queue of work to be done (eg summarize N decades of historical documents). There is little urgency to the outcome because the bolus is so large. You just want to maintain steady progress on the backlog where cost optimization is more important than timing.
  • cpard
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  • 1 hour ago
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yes, what you describe feels like a one off job that you want to run, which is big and also not time critical.

Here's an example:

If you are a TV broadcaster and you want to summarize and annotate the content generated in the past 12 hours you most probably need to have access to the summaries of the previous 12 hours too.

Now if you submit a batch job for the first 12 hours of content, you might end up in a situation where you want to process the next batch but the previous one is not delivered yet.

And imo that's fine as long as you somehow know that it will take more than 12h to complete but it might be delivered to you in 1h or in 23h.

That's the part of the these batch APIs that I find hard to understand how you use in a production environment outside of one off jobs.

  • jampa
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  • 4 hours ago
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> 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.

  • cpard
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  • 1 hour ago
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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.

Contrary to other comments it's likely not because of queue or general batch reasons. I think it is because that LLMs are unique in the sense that it requires lot of fixed nodes because of vRAM requirements and hence it is harder to autoscale. So likely the batch jobs are executed when they have free resources from interactive servers.
  • cpard
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  • 1 hour ago
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that makes total sense and what it entails is that interactive inference >>> batch inference in the market today in terms of demand.
We've submitted tens of millions of requests at a time and never had it take longer than a couple hours - I think the zone you submit to plays a role.
  • pugio
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  • 3 hours ago
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Hah, I've been wrestling with this ALL DAY. Another example of Phenomenal Cosmic Powers (AI) combined with itty bitty docs (typical of Google). The main endpoint ("https://generativelanguage.googleapis.com/v1beta/models/gemi...") doesn't even have actual REST documentation in the API. The Python API has 3 different versions of the same types. One of the main ones (`GenerateContentRequest`) isn't available in the newest path (`google.genai.types`) so you need to find it in an older version, but then you start getting version mismatch errors, and then pydantic errors, until you finally decide to just cross your fingers and submit raw JSON, only to get opaque API errors.

So, if anybody else is frustrated and not finding anything online about this, here are a few things I learned, specifically for structured output generation (which is a main use case for batching) - the individual request JSON should resolve to this:

```json { "request": { "contents": [ { "parts": [ { "text": "Give me the main output please" } ] } ], "system_instruction": { "parts": [ { "text": "You are a main output maker." } ] }, "generation_config": { "response_mime_type": "application/json", "response_json_schema": { "type": "object", "properties": { "output1": { "type": "string" }, "output2": { "type": "string" } }, "required": [ "output1", "output2" ] } } }, "metadata": { "key": "my_id" } } ```

To get actual structured output, don't just do `generation_config.response_schema`, you need to include the mime-type, and the key should be `response_json_schema`. Any other combination will either throw opaque errors or won't trigger Structured Output (and will contain the usual LLM intros "I'm happy to do this for you...").

So you upload a .jsonl file with the above JSON, and then you try to submit it for a batch job. If something is wrong with your file, you'll get a "400" and no other info. If something is wrong with the request submission you'll get a 400 with "Invalid JSON payload received. Unknown name \"file_name\" at 'batch.input_config.requests': Cannot find field."

I got the above error endless times when trying their exact sample code: ``` BATCH_INPUT_FILE='files/123456' # File ID curl https://generativelanguage.googleapis.com/v1beta/models/gemi... \ -X POST \ -H "x-goog-api-key: $GEMINI_API_KEY" \ -H "Content-Type:application/json" \ -d "{ 'batch': { 'display_name': 'my-batch-requests', 'input_config': { 'requests': { 'file_name': ${BATCH_INPUT_FILE} } } } }" ```

Finally got the job submission working via the python api (`file_batch_job = client.batches.create()`), but remember, if something is wrong with the file you're submitting, they won't tell you what, or how.

  • nnx
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  • 5 hours ago
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It would be nice if OpenRouter supported batch mode too, sending a batch and letting OpenRouter find the best provider for the batch within given price and response time.
Is it possible to use batch mode with fine-tuned models?
Is this an indication of the peak of the AI bubble ?

In a way this is saying that there are some GPUs just sitting around so they would rather get 50% than nothing for their use.

Seems more like electricity pricing, which has peak and offpeak pricing for most business customers.

To handle peak daily load you need capacity that goes unused in offpeak hours.

Why do you think that this means "idle GPU" rather than a company recognizing a growing need and allocating resources toward it?

It's cheaper because it's a different market with different needs which can be served by systems optimizing for throughput instead latency. Feels like you're looking for something that's not there.