Together AI
A comprehensive platform for building, training, and deploying AI models with open-source flexibility and powerful GPU infrastructure.
Overview
Together AI provides a comprehensive platform designed to accelerate the development and deployment of AI models, particularly focusing on open-source large language models (LLMs). It offers a suite of products ranging from serverless inference APIs for quick integration to dedicated GPU clusters for high-performance, custom deployments. The platform emphasizes flexibility, allowing users to choose the right level of control and scalability for their AI projects. The core value proposition lies in democratizing access to powerful AI infrastructure and open-source models. Users can fine-tune models to achieve specific performance goals, evaluate model quality, and even build sophisticated AI agents with integrated code execution environments. For infrastructure, Together AI offers instant, self-service GPU clusters and large-scale 'AI Factories' with thousands of NVIDIA GPUs, catering to a wide spectrum of needs from individual developers to large enterprises. This full-stack approach aims to streamline the entire AI lifecycle, from experimentation to production at scale.
Pricing
Pros & Cons
Pros
- Access to a wide range of open-source models for inference and fine-tuning.
- Flexible deployment options including serverless, dedicated endpoints, and custom GPU clusters.
- Cost-effective batch inference with significant savings for high-volume processing.
- Scalable GPU infrastructure from instant clusters to large-scale AI factories.
- Tools for model evaluation and code execution enhance the development workflow.
- Supports advanced features like larger models and longer contexts for fine-tuning.
Cons
- Complexity might be high for beginners or those unfamiliar with AI infrastructure.
- Reliance on open-source models means performance can vary compared to proprietary state-of-the-art models.
- Pricing for dedicated clusters and large-scale GPU deployments might be substantial for smaller startups.
- Requires technical expertise to fully leverage fine-tuning and custom hardware deployments.
- Specific model availability might change or be limited compared to direct access to model providers.
Use Cases
Reviews & Ratings
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Best For
- Deploying open-source LLMs for various applications.
- Fine-tuning models with custom datasets for specific business needs.
- Running large-scale AI inference jobs with cost optimization.
- Developing and testing AI agents requiring code execution environments.
- Scaling AI infrastructure for growing enterprise AI initiatives.
- Research and development requiring access to powerful GPU resources.
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