Vertex AI batch prediction runs large groups of model requests asynchronously instead of serving each one through a real-time endpoint. For Gemini, current Google Cloud batch inference supports input from Cloud Storage JSONL or BigQuery and can write results back to Cloud Storage or BigQuery. Batch inference is designed for high-volume, non-urgent workloads such as enrichment, classification, evaluation, content generation, and offline data processing.
Within AI on Google Cloud, batch prediction is the right pattern when throughput and cost matter more than per-request latency. Google currently prices Gemini batch inference at a substantial discount to real-time inference and targets completion within roughly a day for normal workloads.
The existing batch inference and scheduled scoring article provides the general architecture; this page focuses on Vertex AI/Gemini operating details.
Cloud Storage uses JSONL request records
For Cloud Storage input, Gemini batch jobs read JSON Lines where each record represents a model request.
Output can also be JSONL in a Cloud Storage prefix.
Validate every record locally before submission so one malformed schema or oversized request does not create a large error file after hours of processing.
BigQuery input and output simplify data-pipeline integration
Batch jobs can read request rows from a BigQuery table and write predictions to another BigQuery table.
The input/output BigQuery dataset must be in the same region as the batch prediction job under current documented requirements, and multi-region dataset limitations should be checked for the chosen API path.
This is a strong fit for offline enrichment where source and result tables already live in analytical pipelines.
Publisher models and tuned models use different resource paths
A batch job can reference supported Gemini publisher models or, where supported, a tuned model resource.
Google documents some endpoint-location limitations for tuned-model batch inference, including cases where the global endpoint is not supported.
Record model ID/version and location with every batch job so results remain reproducible after model migrations.
Batch is cheaper because the service can schedule work flexibly
Current Google Cloud documentation describes batch inference as discounted versus real-time serving and able to process very large request sets with higher effective limits.
The trade-off is completion latency: jobs are queued and processed asynchronously rather than immediately.
Do not put an interactive product workflow on batch infrastructure merely to save token cost if the user needs a response in seconds.
Implicit context caching can still affect token economics
Google notes that implicit caching is enabled by default for supported Gemini model families in batch workflows and that cache/batch discounts do not simply stack; the relevant cache discount can take precedence for cached tokens.
This matters for batch datasets containing a repeated long prefix such as the same policy document or system context.
Structure requests consistently so repeated context appears in a cache-friendly prefix when the model supports it.
Jobs should use stable row identifiers
Every request should contain an ID that maps back to the source row or business object.
Do not assume output order alone is sufficient for reconciliation across partial failures, retries, or downstream processing.
Store job name, input dataset/version, output destination, model version, prompt/schema version, and request ID together.
Partial failures need record-level retry rules
Batch systems can return a mixture of successful and failed records.
Separate malformed input, model limits, safety blocks, transient service errors, and downstream write errors before retrying.
Retry only cases that can succeed unchanged; deterministic validation errors should be fixed in the source dataset instead.
Downstream systems need backpressure too
A completed batch can produce hundreds of thousands of results quickly, which may overwhelm a database, search index, or human-review queue.
Consume output in checkpoints and use idempotent writes so downstream recovery can resume safely.
Batch inference removes request orchestration from the model layer; it does not remove the need to control result ingestion.
Batch evaluation is a natural release workflow
Model and prompt regression tests often need the same set of thousands of cases against several candidate configurations.
Batch prediction can produce outputs efficiently for scoring and comparison.
Keep the evaluation dataset stable enough that quality differences represent the candidate model/prompt rather than changes in the test corpus.
Security follows the data locations as well as the model
Cloud Storage buckets and BigQuery datasets need appropriate IAM, encryption, retention, and regional placement.
Batch input/output often contains a larger concentration of sensitive data than an individual online request.
Use dedicated service accounts, restrict access to batch artifacts, and delete historical files/tables according to the application’s retention policy.
Batch prediction succeeds when large inference jobs become reproducible data pipelines
The mature system validates inputs, versions model/prompt/data, chooses GCS or BigQuery deliberately, monitors job states, handles partial failure, controls downstream load, and preserves lineage from source row to output.
Batch inference should make high-volume AI predictable—not turn millions of asynchronous requests into an opaque one-off job.
Input design should exploit per-record model parameters only when necessary. Batch rows can carry request-specific generation settings, but allowing every upstream producer to set arbitrary temperature/output length/tool configuration can make results inconsistent and cost unpredictable. Standardize a small number of approved request profiles where possible.
Batch job location should be an explicit data-governance choice. Cloud Storage buckets, BigQuery datasets, and the job/model location need compatible regional placement. If source data is in a multi-region warehouse and the batch API requires a single compatible region for a particular path, plan export/staging rather than discovering the mismatch during submission.
Large datasets should be partitioned into recoverable job sizes. One giant batch can be cheaper to orchestrate but creates a large failure/retry domain. Split by date, tenant, dataset shard, or workflow phase so failed subsets can be rerun and completed output can flow downstream before the entire corpus finishes.
Job orchestration should track pending/running/succeeded/failed/cancelled/paused states and the age of the oldest job. A queue of ‘active batches’ is not enough operational detail if one job has remained pending for hours because quota or capacity is constrained.
Output schemas should be validated before loading downstream. A model can return structurally valid but semantically missing fields, blocked responses, or shorter content than expected. Treat model output as untrusted data and apply deterministic schema/business validation before writing to production tables or search indexes.
Batch inference should be compared with real-time parallelism before adoption. For medium-size workloads with strict completion windows, a carefully rate-limited online pipeline may finish sooner; for large nonurgent jobs, batch generally simplifies orchestration and cost. Choose based on throughput deadline, not on the label ‘offline.’
Cancellation should be part of the runbook. If a wrong dataset or prompt version is submitted, operators need a way to stop the job, record the partial output boundary, and prevent downstream ingestion of the wrong results. Lineage metadata should make it easy to invalidate all outputs from one bad job ID.
Model retirement should include recurring batches. Scheduled weekly/monthly jobs can be forgotten because they are not user-facing. Inventory model IDs in batch orchestration and run migration tests before the model is removed, otherwise a quiet reporting pipeline can fail long after the main online service has moved on.
Batch quality metrics should be computed per slice, not only globally. A 95% overall classifier accuracy can hide poor performance for one language, tenant, document type, or rare class. Store source attributes with the request ID so offline scoring can identify the cohorts that need a different prompt/model.
Batch input generation should be deterministic and auditable. Build requests from a versioned transformation job so the same source snapshot and prompt version produce the same batch file or table. Ad hoc notebooks that write production batch inputs make it difficult to explain why one run behaved differently from another.
Concurrency with other workloads should be considered at the project and data-platform layers. A large batch can compete for BigQuery slots, Cloud Storage throughput, model quota, and downstream pipelines even if the batch service itself is managed. Schedule jobs and isolate projects/quotas where critical online workloads should not be affected.
Completion within a target window is not the same as guaranteed ordering. Downstream consumers should wait for the job’s terminal state and use request IDs to reconcile output. Avoid streaming partial outputs into irreversible business workflows unless the pipeline explicitly records which subset is complete and can safely handle later corrections.
Cost controls should estimate batch size before submission. Tokenize or sample representative requests, project input/output volume, and compare the expected job cost with budget. A bad upstream export that duplicates rows or unexpectedly expands prompts can turn a routine nightly batch into a very large spend before anyone notices from the output.
Batch jobs should use a dead-letter/review path for persistent failures. After one or two safe retries, move records that still fail into a separate table or file with error reason and source context. This keeps the main pipeline progressing while preserving enough evidence for engineers or business reviewers to fix the problematic cohort.
Batch jobs should carry a reproducible input snapshot, model version, parameters, destination, and completion record. That makes reruns comparable and prevents a large offline inference job from producing outputs that cannot later be tied to the exact data and model state used.
