Together cost tuning

Together AI Cost Tuning

Overview

Optimize Together AI costs with model selection, batching, and caching.

Instructions

Together AI Pricing Model

Model Category	Price (per 1M tokens)	Example Models
Small (< 10B)	$0.10-0.30	Llama-3.2-3B, Qwen-2.5-7B
Medium (10-40B)	$0.60-1.20	Mixtral-8x7B, Llama-3.3-70B-Turbo
Large (40B+)	$2.00-5.00	Llama-3.1-405B, DeepSeek-V3
Image gen	$0.003-0.05/image	FLUX.1-schnell, SDXL
Embeddings	$0.008/1M tokens	M2-BERT
Fine-tuning	~$5-25/hour	Depends on model + GPU
Batch inference	50% off	Same models, async

Cost Reduction Strategies

# 1. Use Turbo variants (faster, cheaper, similar quality)
# meta-llama/Llama-3.3-70B-Instruct-Turbo vs Llama-3.1-70B-Instruct

# 2. Batch inference (50% cost reduction)
batch_response = client.batch.create(
    input_file_id=file_id,
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    completion_window="24h",
)

# 3. Cache responses for identical prompts
from functools import lru_cache

@lru_cache(maxsize=1000)
def cached_completion(prompt: str, model: str) -> str:
    response = client.chat.completions.create(
        model=model, messages=[{"role": "user", "content": prompt}],
    )
    return response.choices[0].message.content

# 4. Use smallest model that works
# Test with 3B first, upgrade to 70B only if quality insufficient

Error Handling

Issue	Cause	Solution
High costs	Wrong model tier	Downsize model
Batch failures	Invalid input format	Validate JSONL
Fine-tuning expensive	Too many epochs	Start with 1-2 epochs

Resources

• Together AI Pricing
• Batch Inference

Next Steps

For architecture patterns, see together-reference-architecture.