Batch Mode for the Gemini API feels like Google’s way of asking, “What if we made AI more affordable and slower, but at massive scale?” Now you can process 10,000 prompts like “Summarize each customer review in one line” for half the cost, provided you’re willing to wait until tomorrow for the results.
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.
calaphos 3 hours ago [-]
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 7 hours ago [-]
Bedrock has a batch mode but only for claude 3.5 which is like one year old, which isn't very useful.
dlvhdr 6 hours ago [-]
The latest price increases beg to differ
dmos62 3 hours ago [-]
What price increases?
rvnx 3 hours ago [-]
I guess the Gemini price increase
segalord 3 hours ago [-]
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
nikolayasdf123 33 minutes ago [-]
omg I realized this is not Vertex AI face-palm
dsjoerg 8 hours ago [-]
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.
Jensson 6 hours ago [-]
> 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.
Implicated 14 minutes ago [-]
I was also rather puzzled at this comment - why not dev against real time endpoints and batch when you've got things where you need them?
cpard 8 hours ago [-]
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!
vineyardmike 8 hours ago [-]
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 4 hours ago [-]
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.
3eb7988a1663 8 hours ago [-]
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 4 hours ago [-]
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 6 hours ago [-]
> 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 4 hours ago [-]
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.
YetAnotherNick 7 hours ago [-]
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 4 hours ago [-]
that makes total sense and what it entails is that interactive inference >>> batch inference in the market today in terms of demand.
serjester 6 hours ago [-]
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.
druskacik 2 hours ago [-]
I've been using OpenAI's batch API for some time, then replaced it with Mistral's batch API because it was cheaper (Mistral Small with $0.10 / $0.20 per million tokens was perfect for my use case). This makes me rethink my choice, e.g. Gemini 2.5 Flash-Lite seems to be a better model[0] with only a slight price increase.
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:
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.
TheTaytay 2 hours ago [-]
Thank you for posting this! (When I run into errors with posted sample code, I spend WAY too long assuming it’s my fault.)
nnx 8 hours ago [-]
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.
tucnak 1 hours ago [-]
I really hope it means that 2.5 models will be available for batching in Vertex, too. We had spent quite a bit of effort making it work with BigQuery, and it's really cool when it works. There's edge-case, though, where it doesn't work: in case the batch is also referring to cached prompt. We did report this a few months ago.
dmitry-vsl 5 hours ago [-]
Is it possible to use batch mode with fine-tuned models?
great_psy 6 hours ago [-]
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.
graeme 6 hours ago [-]
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.
reasonableklout 6 hours ago [-]
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.
https://discuss.ai.google.dev/t/gemini-2-5-pro-with-empty-re...
It's nice to see competition in this space. AI is getting cheaper and cheaper!
https://api-docs.deepseek.com/quick_start/pricing
I guess it lets them better utilise their hardware in quiet times throughout the day. It's interesting they all picked 50% discount.
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.
You can do this, just send 1% using the regular API.
I wonder why they do that and who is actually getting value out of these batch APIs.
Thanks for sharing your experience!
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.
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.
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.
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.
Maybe I'm just thinking too much in data engineering terms here.
[0] https://artificialanalysis.ai/leaderboards/models
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.
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.
To handle peak daily load you need capacity that goes unused in offpeak hours.
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.