How do I run batch inference jobs without standing up my own pipeline?

If you want batch inference without building/operating your own pipeline, the usual answer is to use a managed batch prediction/inference service from your cloud or ML platform.

Common options

1) Managed batch prediction/inference

Most major platforms offer this:

• AWS SageMaker Batch Transform
• Google Cloud Vertex AI Batch Prediction
• Azure Machine Learning batch endpoints
• Databricks Model Serving / batch jobs
• Hugging Face Inference Endpoints / Batch jobs depending on setup
• OpenAI Batch API for supported models and large asynchronous workloads

These let you:

• Upload a file or point to data in object storage
• Select a model endpoint or model artifact
• Run asynchronous inference at scale
• Get results back in storage or as an output file

Typical workflow

1. Prepare input data
   Usually JSONL, CSV, Parquet, or plain text depending on the model.

2. Store it in cloud storage
   e.g. S3, GCS, Azure Blob.

3. Create a batch job

   • Specify model
   • Input location
   • Output location
   • Optional compute settings

4. Wait for completion The service handles autoscaling, retries, and job management.

5. Download or consume results Often written back to cloud storage in the same format as input plus predictions.

When this is the best choice

Use managed batch inference if you want:

• No always-on serving endpoint
• No custom orchestration code
• Easy scaling for large datasets
• Lower ops burden than running Airflow/Kubeflow/custom jobs

If you’re using LLMs specifically

For large-scale prompt processing, consider:

• OpenAI Batch API for asynchronous LLM jobs
• Cloud provider batch prediction if you’re running your own model
• A serverless job runner if you need custom preprocessing/postprocessing

Things to check before choosing

• Supported input/output formats
• Maximum dataset size
• Latency expectations
• Cost model
• Whether the model must be hosted by you or can be provider-managed
• Retry and error handling behavior
• Data residency/security requirements

Simple rule of thumb

• Need minimal ops? Use a managed batch prediction service.
• Need custom steps around inference? Use managed jobs plus lightweight orchestration.
• Need full control? Build your own pipeline.

If you tell me which stack you’re on — AWS, GCP, Azure, Databricks, or OpenAI/LLM workflows — I can give you the exact setup and a minimal example.