For many developers, the challenge of running Large Language Models (LLMs) isn't just about accuracy; it is about economics. As you scale from a prototype to a production system, the difference between synchronous and asynchronous processing becomes a significant factor in your monthly spend. If you are handling large volumes of data—such as document summarization, classification, or data extraction—you don't need immediate results. You need efficiency.
Together AI’s serverless inference platform offers a robust solution for this specific use case. By leveraging their batch processing capabilities, you can decouple the cost of computation from the latency of the response. This guide explores how to architect your pipeline to maximize cost-performance, ensuring you are paying only for the tokens processed, without the premium for real-time availability.
Understanding the Economics of Batch Inference
In a standard synchronous request, you are essentially paying for a "reserved" instance of computational power that is ready to serve your request instantly. In a batch context, the provider can aggregate multiple requests from various users and process them in the background. This allows the infrastructure to be utilized more efficiently, which translates to lower costs for the developer.
When using Together AI, the primary benefit is the ability to submit large JSONL (JSON Lines) files for processing. The platform handles the concurrency, error handling, and model loading. For intermediate developers, the mental model shift is important: stop thinking of the API as a function call, and start thinking of it as a job scheduler.
Implementing a Batch Pipeline
The workflow typically involves three stages: preparation, submission, and retrieval. Below is a practical example using Python to prepare a dataset and submit it to the Together AI API. Note that you must replace YOUR_API_KEY with your actual credentials.
import json
import time
from together import Together
# 1. Prepare your data
# Assume 'documents' is a list of strings to be processed
documents = ["Text sample 1", "Text sample 2", "Text sample 3"]
batch_data = []
for idx, text in enumerate(documents):
batch_data.append({
"custom_id": f"doc_{idx}",
"method": "POST",
"url": "/v1/chat/completions",
"body": {
"model": "meta-llama/Llama-3-70b-chat-hf",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": f"Summarize the following text: {text}"}
],
"temperature": 0.7
}
})
# 2. Submit the batch
client = Together(api_key="YOUR_API_KEY")
# The batch endpoint accepts a list of requests
batch_response = client.batch.create(requests=batch_data)
batch_id = batch_response.id
print(f"Batch ID: {batch_id}")
# 3. Poll for completion
# In a real-world scenario, you would use exponential backoff
while True:
status = client.batch.retrieve(batch_id)
if status.status == "completed":
break
elif status.status == "failed":
print("Batch failed")
break
else:
print(f"Status: {status.status}")
time.sleep(10)
# 4. Retrieve results
if status.status == "completed":
results = client.batch.results(batch_id)
for result in results:
print(result.custom_id, result.response)
Best Practices for Maximizing Efficiency
To get the most out of this approach, consider these technical optimizations:
- Minimize Prompt Overhead: Batch processing is expensive if your prompts are repetitive and long. Ensure that system prompts are as concise as possible while maintaining context.
- Group by Similarity: If you are using a model that supports specific system prompt caching or similar optimizations, group related documents together. While Together AI handles model loading, reducing the variance in your data can sometimes help with throughput consistency.
- Error Handling: Always include a
custom_id. This allows you to map the result back to the original input easily. If a specific request fails, you can identify it without processing the entire batch again. - Size Limits: Be mindful of the maximum file size and token limits. Splitting massive datasets into smaller, manageable batches (e.g., 1,000 requests per batch) can improve reliability and allow for parallel processing of multiple batches.
Conclusion
Optimizing for cost-performance is not just about finding the cheapest model; it is about using the right infrastructure for the job. By moving non-latency-sensitive tasks to Together AI’s serverless batch inference, you can drastically reduce your operational costs while maintaining high-quality LLM outputs. This approach allows your engineering team to focus on building valuable AI features rather than managing infrastructure bottlenecks. Start small, validate your results, and scale your batch sizes as your confidence in the pipeline grows.