Together AI
Fine-tune open-source models for greater accuracy with proprietary data.
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Together AI Overview
The website offers a wide range of large language models and AI solutions for various applications. - Customers can access cutting-edge research and models tailored to their specific needs. - The platform provides flexible deployment options and high-performance AI solutions. - Together AI is trusted for its fast and accurate inference at production scale.
Foundation Year
2022
Parent Company
Together AI
Founders
Vipul Ved Prakash Avinash Raghava Manav Garg Shubham Gupta
Application of Together AI
- Fine-tune open-source models for greater accuracy with proprietary data.
- Access high-end GPU clusters for large-scale training and fine-tuning.
- Run 100+ open-source models on Serverless or Dedicated Instances.
- Train Frontier Models from scratch with multiple model architectures.
Who Can Use It?
- Data Scientists
- Software Developers
- Marketing Professionals
- Business Executives
- Decision Makers
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Together AI Price Structure
Free Trial Available
No
Yearly Pricing Level 1
Llama 3.1 (8B) Reference - $0.20 per 1M tokens - billed monthly
Pricing Level 2
Llama 3.1 (70B) Reference - $0.90 per 1M tokens - billed monthly
Pricing Level 3
Llama 3.1 Instruct (70B) Reference - $0.90 per 1M tokens - billed monthly
Other Details
API Access Available
Yes
Technical Requirements
- Access to a stable internet connection is required for using the website.
- The website is compatible with standard web browsers such as Chrome, Firefox, Safari, and Edge.
- Hardware requirements include a device with sufficient computational resources to handle AI models.
- Software requirements may include the need for specific tools or frameworks for fine-tuning or training models.
- The website may require compatibility with GPU clusters for large-scale training and fine-tuning of AI models.
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Pros and Cons
Pros
- High end clusters without long-term commitments
- Flexible terms for hardware usage
- Clusters available from 16 to 10,000 GPUs
- Snappy setup and blazing fast training
- Pre-configured for high-speed distributed training
- Innovations like Cocktail SGD and FlashAttention-3 for faster processing
- RedPajama project enabling leading generative AI models to be open-source
- Sub-quadratic model architectures for faster performance
Cons
- Limited flexibility in terms of model architecture options
- Lack of detailed information on the cost structure and pricing plans
- Unclear information on the level of customer support available
- Potential challenges in integrating the models into existing production applications
Disclaimer:
This data is based on open sources for informational purposes only. For complete accuracy, please visit the actual website to validate the information.
Community
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