together-batch-inference

Batch-process inference requests asynchronously via Together AI's Batch API.

2|1|Updated Feb 20, 2026

Search Tags:#workflow #api #asynchronous #batch #inference #jsonl #bulk-processing

Ready to run

npx skills add https://github.com/zainhas/togetherai-skills --skill together-batch-inference

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: together-batch-inference
Source: https://github.com/zainhas/togetherai-skills/tree/main/skills/together-batch-inference
Command: npx skills add https://github.com/zainhas/togetherai-skills --skill together-batch-inference

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires together, together-ai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Batch-processing large volumes of inference requests can be costly and slow when sent individually. This Skill provides a scalable workflow to aggregate, submit, monitor, and retrieve results via Together AI's Batch API.

Core Features & Use Cases

Upload input as JSONL, create batch jobs, monitor progress, and download results.
Supports up to 50K requests per batch and 100MB per input file.
Useful for offline inference, bulk data classification, synthetic data generation, and cost-optimized large-scale workloads.

Quick Start

Run the batch_workflow.py script to execute a complete batch-inference workflow from input preparation to results download.

Frequently Asked Questions about together-batch-inference

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I process bulk inference requests asynchronously using the Together AI API?▼

You can process bulk inference requests asynchronously by uploading a JSONL file to the Together AI Batch API, creating a job, and downloading the results once polling shows completion. This workflow handles up to 50K requests per batch.

What is the best way to lower the cost of large-scale synthetic data generation?▼

The best way to lower costs for large-scale synthetic data generation is using batch inference. By aggregating offline inference requests into bulk jobs, you avoid the premium of real-time processing and optimize workload expenses.

What are the file size and request limits for batch inference with Together AI?▼

Together AI batch inference supports input files up to 100MB in size and allows a maximum of 50,000 requests per batch. These limits ensure scalable asynchronous processing without overwhelming the API.

Can I use JSONL files for bulk data classification and offline inference?▼

Yes, you can use JSONL files for bulk data classification and offline inference. The workflow accepts JSONL input format to aggregate, submit, and retrieve classification results asynchronously.

How do I monitor the status of an asynchronous batch inference job?▼

You monitor an asynchronous batch inference job through status polling. After submitting the JSONL input file, the workflow periodically checks the job status until processing completes and results are ready for download.

Why should I use batch processing instead of individual inference requests for large workloads?▼

You should use batch processing for large workloads because sending individual inference requests is slow and costly. Batch processing aggregates requests for asynchronous execution, optimizing both processing time and overall expenses.