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.
One open secret is that batch mode generations often take much less than 24 hours. I've done a lot of generations where I get my results within 5ish minutes.
It can depend a lot on the shape of your batch to my understanding. A small batch job can be tasked out a lot quicker than a large batch job waiting for just the right moment where capacity fits.
I find OpenAI's new flex processing more attractive, as it has the same 50% discount, but allows to use the same API as regular chat mode, so you can still do stuff where Batch API won't work (e.g. evaluating agents), and in practice I found it to work well enough when paired with client-side request caching: https://platform.openai.com/docs/guides/flex-processing?api-...
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.
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.
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.
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.
> 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.
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.
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
It’s because vertex is the “entrrprise” offering that is hippa compliant, etc. That is why vertex only has explicit prompt caching and not implicit, etc. Vertex usage is never used for training or model feedback, but the gemini API does. Basically the Gemini API is Google’s way of being able to move faster like openai and the other foundational model providers, but still having an enterprise offering. Go check Anthropic’s documentation, they even say if you have enterprise or regulatory needs go use bedrock or vertex.
Vertex's offering of Gemini very much does implicit caching, and has always been the case [1]. The recent addition of applying implicit cache hit discounts also works on Vertex, as long as you don't use the `global` endpoint and hit one of the regional endpoints.
[1]: http://web.archive.org/web/20240517173258/https://cloud.goog..., "By default Google caches a customer's inputs and outputs for Gemini models to accelerate responses to subsequent prompts from the customer. Cached contents are stored for up to 24 hours."
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.
> 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."
Thanks for your post, I've stumbled upon the same issue as you.
So I should interpret the "Unknown name \"file_name\" at 'batch.input_config.requests'" as an error with the jsonl file and not the payload itself?
I'm trying to submit a batch with a .jsonl file, but I'm always getting the "Unknown name \"file_name\" at 'batch.input_config.requests'" error.
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.
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.
Readit News
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.
Edit: anthropic also stack batching and caching discounts
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.
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.
You do have - within 24 hours. So don't submit requests you need in 10 hours.
[1]: http://web.archive.org/web/20240517173258/https://cloud.goog..., "By default Google caches a customer's inputs and outputs for Gemini models to accelerate responses to subsequent prompts from the customer. Cached contents are stored for up to 24 hours."
https://discuss.ai.google.dev/t/gemini-2-5-pro-with-empty-re...
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.
Thanks for your post, I've stumbled upon the same issue as you.
So I should interpret the "Unknown name \"file_name\" at 'batch.input_config.requests'" as an error with the jsonl file and not the payload itself?
I'm trying to submit a batch with a .jsonl file, but I'm always getting the "Unknown name \"file_name\" at 'batch.input_config.requests'" error.
[0] https://artificialanalysis.ai/leaderboards/models