Mistral AI Pricing
Shares tags: pricing & licensing, billing units, per token
AI Tool
Affordable Token-Based Pricing for Enhanced AI Performance
Unlock the power of Together’s hosted open-source models with flexible pricing that adapts to your needs.
Pay only for what you use with our flexible pay-per-token model.Optimize costs with batch inference for large-scale token processing.Customize models with scalable fine-tuning options for your proprietary data.
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Pricing & LicensingBilling UnitsPer Token
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overview
Efficient, Pay-Per-Token Model
Together API offers a transparent and flexible pricing structure that empowers developers to utilize over 200 models effectively. With costs varying based on model families, users can scale their projects without overspending.
- Access diverse models for varied use cases.
- Pay between $0.27 and $1.25 per million tokens based on model requirements.
- Ideal for both small projects and large-scale operations.
features
Advanced Batch Inference
Our Batch Inference API allows you to process billions of tokens at a cost-saving rate, making it perfect for cost-conscious projects that do not require real-time responses. This feature significantly reduces costs, ensuring you get the best value for high-volume workloads.
- Achieve an average of 50% cost reduction.
- Ideal for non-real-time applications.
- Process massive amounts of data efficiently.
use_cases
Fine-Tuning for Customized Performance
Adapt and refine Together’s models to fit your unique data and requirements. Choose from LoRA fine-tuning or full fine-tuning options to enhance model performance, with costs designed to scale with your team’s needs.
- LoRA fine-tuning at $0.48-$1.50 per million tokens.
- Full fine-tuning ranging from $0.54-$1.65 per million tokens.
- Perfect for development teams needing personalized model adjustments.
Frequently Asked Questions
What is the pricing structure for the Token API?
Together API employs a pay-per-token model where costs vary based on input and output tokens across different models, ranging from $0.27 to $1.25 per million tokens.
How does batch inference help save costs?
Batch inference enables processing billions of tokens at a reduced rate, offering approximately 50% savings compared to standard pricing, making it ideal for non-real-time tasks.
Are there options for customizing models?
Yes, Together API provides flexible fine-tuning options, including both LoRA and full fine-tuning, allowing teams to tailor models to fit their unique datasets.