Together AI Batch Inference Skill for Claude Code

What it is

This is a skill that teaches AI coding agents how to use Together AI's batch inference API. Instead of making synchronous inference calls one at a time, batch inference lets you submit large volumes of prompts as asynchronous jobs. Together AI processes them in the background at up to 50% lower cost compared to real-time API calls.

This skill is for developers using Claude Code or similar AI agents who need to process hundreds or thousands of prompts through Together AI's models cost-efficiently.

How it saves time or tokens

Real-time inference charges full price per token. Batch inference trades latency for cost: you submit a batch, wait for processing (minutes to hours), and retrieve results at a significant discount. For workloads like dataset labeling, content generation, or evaluation runs, batch mode saves both money and rate limit headaches.

How to use

1. Install the Together AI skills package with npx skills add togethercomputer/skills.
2. The skill activates when you ask Claude Code to run batch inference.
3. Submit prompts as a batch and retrieve results when processing completes.

Example

# Install the skill
npx skills add togethercomputer/skills

import together

client = together.Together()

# Create a batch job
batch = client.batch.create(
    model='meta-llama/Llama-3-70b-chat-hf',
    requests=[
        {'messages': [{'role': 'user', 'content': 'Summarize quantum computing'}]},
        {'messages': [{'role': 'user', 'content': 'Explain transformer architecture'}]},
        {'messages': [{'role': 'user', 'content': 'What is RLHF?'}]},
    ]
)

# Check status
status = client.batch.retrieve(batch.id)
print(status.status)  # 'processing' or 'completed'

# Get results when done
results = client.batch.results(batch.id)
for r in results:
    print(r.choices[0].message.content[:100])

Related on TokRepo

• AI Tools for API -- AI inference and API tools
• AI Tools for Automation -- Batch processing and automation

Common pitfalls

• Batch jobs are asynchronous. Results are not available immediately. Your code must poll for completion or use webhooks to be notified when the batch finishes.
• Not all Together AI models support batch inference. Check the Together AI documentation for batch-eligible models before submitting jobs.
• Batch results expire after a limited time. Download results promptly after the batch completes to avoid data loss.

Before adopting this tool, evaluate whether it fits your team's existing workflow. Read the official documentation thoroughly, and start with a small proof-of-concept rather than a full migration. Community forums, GitHub issues, and Stack Overflow are valuable resources when you encounter edge cases not covered in the documentation.