Batch requests to Gemini 3 through an inference gateway

Routing a thousand summarization calls through a Gemini 3 batch requests gateway cuts the integration surface area compared to Google’s native Vertex batch pipeline. You keep one OpenAI-compatible client, submit jobs as JSONL, and let the gateway handle provider auth and fallback. The tradeoff is a thin abstraction layer you need to understand before trusting it with production data.

What Google direct batching actually requires

Google’s Vertex AI treats generative batch workloads as batch prediction operations. You stage a JSONL manifest in a Cloud Storage bucket, call batchPredict on a publisher model, and poll an async operation resource. IAM is scoped to the bucket and the Vertex service account, not to a single API key.

That works, but it forces you to maintain GCP SDK credentials, bucket lifecycle rules, and a separate code path from your real-time inference calls. If your app already speaks the OpenAI client protocol, you now run two stacks.

Why a gateway changes the equation

A Gemini 3 batch requests gateway maps the OpenAI batch semantics onto Google’s backend. You upload a file, create a batch, and download results using the same openai Python package you use for GPT-class models. A gateway such as n4n.ai exposes one OpenAI-compatible endpoint that addresses 240+ models, including Gemini 3, so your existing batch submission code works unchanged.

The gateway translates model: google/gemini-3-pro into the correct Vertex publisher model ID, attaches your stored provider key, and normalizes the response schema. You get per-token usage metering in a single dashboard instead of reconciling Vertex billing exports.

Prerequisites

• A gateway API key with batch scope.
• The exact model slug your gateway uses for Gemini 3 (often google/gemini-3-pro or gemini-3-flash).
• A local JSONL file where each line is an OpenAI-style request wrapper.
• Python with the official openai package (>=1.0) or equivalent HTTP client.

Step 1: Build the batch input file

Each line must be a complete request object. The url field is the relative endpoint; for chat completions it is /v1/chat/completions. Keep custom_id stable so you can join results later.

{"custom_id":"doc-001","method":"POST","url":"/v1/chat/completions","body":{"model":"google/gemini-3-pro","messages":[{"role":"user","content":"Summarize the following RFC: ..."}],"max_tokens":256}}
{"custom_id":"doc-002","method":"POST","url":"/v1/chat/completions","body":{"model":"google/gemini-3-pro","messages":[{"role":"user","content":"Summarize the following RFC: .."}],"max_tokens":256}}

Schema mismatches

Gemini accepts system instructions inside messages or as a top-level field depending on the backend. Through a gateway, stick to the OpenAI shape: a system role message first, then user. Don’t embed Gemini-specific generationConfig at the top level unless the gateway documents an extensions passthrough. If you do, validate against one line before scaling.

Avoid binary blobs. Gemini accepts inline text or GCS URIs via Vertex; through a gateway you are limited to what the OpenAI schema carries, so reference external storage in the prompt text if needed.

Step 2: Upload and submit

OpenAI’s batch flow requires a file upload first. Point the client at the gateway base URL.

from openai import OpenAI
import os

client = OpenAI(
    base_url="https://gateway.example.ai/v1",
    api_key=os.environ["GW_KEY"],
)

with open("batch.jsonl", "rb") as f:
    file_obj = client.files.create(file=f, purpose="batch")

batch = client.batches.create(
    input_file_id=file_obj.id,
    endpoint="/v1/chat/completions",
    completion_window="24h",
)
print(batch.id)

The completion_window is a hint, not a guarantee. Google’s backend may take longer during peak load; the gateway should surface that as a status of finalizing rather than silently failing.

Step 3: Poll or receive completion

Batch jobs are async. Poll on an interval that respects the gateway’s rate limit (usually 1 request per 10s is plenty).

import time

while True:
    b = client.batches.retrieve(batch.id)
    if b.status in ("completed", "failed", "expired"):
        break
    time.sleep(30)

if b.status == "completed":
    out = client.files.retrieve_content(b.output_file_id)
    # out is JSONL: each line has custom_id, body with choices, usage

If the gateway supports webhooks, prefer them. Polling a thousand batches from a serverless function will burn concurrency slots.

Step 4: Reconcile usage and handle partial failures

A Gemini 3 batch requests gateway returns per-line usage objects. Sum them for cost attribution:

import json

total_tokens = 0
for line in out.text.splitlines():
    rec = json.loads(line)
    total_tokens += rec["body"]["usage"]["total_tokens"]

Partial failure is normal. Google may reject a line for safety or malformed content while the rest succeed. The gateway wraps each error in the same JSONL stream with error field. Do not assume status: completed means zero errors; check the error_file_id on the batch object.

Tradeoffs and pitfalls

Rate limits and fallback semantics

Gateways like n4n.ai implement automatic fallback when a provider is rate-limited or degraded, but for batch submissions the job is bound to the original provider at submit time. A mid-flight fallback would require re-submitting the manifest to a different backend, which breaks custom_id continuity. Expect the gateway to retry within Google, not to silently reroute to Anthropic.

If your SLA demands cross-provider redundancy for batch, you must split the file and submit two batches yourself.

Cache-control and routing directives

Gemini supports cached content via explicit cache endpoints. The OpenAI schema has no native field for this, but many gateways forward provider-specific hints if you put them in body.extensions or similar. n4n.ai honors client routing directives and forwards provider cache-control hints, so set them explicitly:

{"custom_id":"doc-003","method":"POST","url":"/v1/chat/completions","body":{"model":"google/gemini-3-pro","messages":[...],"extensions":{"google": {"cached_content": "cache-id-xyz"}}}}

If you omit this, you pay full token cost on every line. Verify the gateway’s passthrough policy before scaling.

Data residency and compliance

Going through a Gemini 3 batch requests gateway means prompt text transits a third-party proxy. Google direct keeps bytes inside Vertex’s VPC if you configure VPC Service Controls. For regulated workloads, that difference is decisive. Ask the gateway whether it logs request bodies or only metadata; per-token metering implies it sees token counts, not necessarily raw text.

Provider model version drift

Google rotates model aliases. Your gateway slug gemini-3-pro might pin to a dated build or track the live latest. Direct Vertex calls use a versioned publisher model ID. If reproducibility matters, confirm the gateway’s pinning behavior and capture the resolved model ID from the first batch response.

Latency vs throughput

Batch is not for interactive use. Google’s batch queue prioritizes cheap throughput over tail latency. The gateway adds one network hop and a normalization step. If you need a response in <2s, use the synchronous /v1/chat/completions path, not batch.

When to skip the gateway

Use Vertex direct when:

• You already run GCP-native pipelines and have Terraform for buckets and IAM.
• You need granular VPC-SC perimeters or customer-managed encryption keys on the manifest.
• Your batch size is small enough that the OpenAI client convenience isn’t worth the extra dependency.

Use the gateway when you operate a multi-model fleet and want one retry, metering, and file-handling code path. A Gemini 3 batch requests gateway earns its place the moment you add a second provider.

Quick checklist

1. Format lines as OpenAI chat requests with stable custom_id.
2. Upload via files.create with purpose="batch".
3. Create batch with endpoint="/v1/chat/completions".
4. Poll status, then pull output_file_id and error_file_id.
5. Sum usage per line; alert on non-zero error lines.
6. Confirm cache hints are forwarded if cost matters.

Ship the batch job only after you have validated one line end-to-end against a non-production gateway key.