Together AI

Together AI positions itself as the AI Acceleration Cloud, an end-to-end platform designed for developers and researchers to build the future of generative AI. It provides a comprehensive suite of tools and infrastructure to train, fine-tune, and run a vast array of open-source models. The platform is built on a foundation of cutting-edge research, aiming to deliver unparalleled speed, cost-efficiency, and flexibility, with a strong commitment to the open-source community.

At its core, Together AI offers a seamless continuum of services that cover the entire generative AI lifecycle. Users can start with the Inference API to quickly integrate over 200 pre-trained models into their applications, move on to fine-tuning these models with their own data for specialized tasks, or leverage powerful GPU clusters to train new, custom models from scratch. This integrated approach empowers organizations of all sizes to innovate and deploy sophisticated AI solutions without vendor lock-in.

How to use Together AI

Getting started with Together AI is straightforward and tailored to different needs:

1. For Inference: Developers can sign up to get an API key. Using the OpenAI-compatible API, they can easily switch from other services or start new projects. You can make API calls to serverless endpoints for various models (chat, image, code, etc.) and pay only for what you use. For consistent high-throughput needs, dedicated instances can be deployed.
2. For Fine-Tuning: Prepare your training data in a standard format like JSONL. Use the simple command-line interface (CLI) to upload your dataset. Then, run the `together finetune create` command, specifying the base model you wish to fine-tune and your dataset. You can start with a single command or dive deeper to control hyperparameters like learning rate, batch size, and epochs to optimize performance.
3. For Training on GPU Clusters: For large-scale projects, you can reserve dedicated GPU clusters. These clusters are equipped with top-tier NVIDIA GPUs (like H100, H200, and GB200) and high-speed interconnects. You can manage your training workloads using standard orchestration tools like Slurm and Kubernetes.

Core Features of Together AI

• Extensive Model Library: Access to over 200 generative AI models, including leading open-source families like Llama, Mixtral, Qwen, Gemma, and DeepSeek, covering chat, code generation, image creation, audio transcription, and embeddings.
• High-Performance Inference: The Together Inference Engine, powered by research innovations like FlashAttention-3 and custom kernels, delivers industry-leading speed and throughput for model inference, significantly reducing latency.
• Customizable Fine-Tuning: A user-friendly API and CLI for fine-tuning open-source models. It supports both efficient LoRA (Low-Rank Adaptation) and full fine-tuning, giving you complete ownership of the resulting model.
• Dedicated GPU Clusters: On-demand access to state-of-the-art NVIDIA GPU clusters for large-scale training and inference, featuring high-speed networking to eliminate bottlenecks.
• OpenAI-Compatible API: A drop-in replacement for the OpenAI API, allowing for seamless migration of existing applications to run on open-source models with minimal code changes.
• Enterprise-Ready Security: The platform is SOC 2 and HIPAA compliant, offering robust security and the ability to deploy within an enterprise's own Virtual Private Cloud (VPC).

Use Cases for Together AI

The platform supports a wide range of applications, including:

• Advanced Chatbots & Virtual Assistants: Building and deploying highly responsive and context-aware conversational AI for customer support, personal assistants, and more.
• Code Generation & Developer Tools: Integrating powerful code models into IDEs to assist with code completion, debugging, and generating entire codebases from prompts.
• Creative Content Generation: Creating high-quality images, marketing copy, and other creative content using state-of-the-art image and language models.
• Data Analysis & Extraction: Fine-tuning models for specialized data tasks like sentiment analysis, document summarization, and structured data extraction from unstructured text.
• AI Research & Foundational Model Training: Providing researchers with the high-performance computing resources needed to train and experiment with new AI architectures.

Advantages of Together AI

Together AI offers several key advantages:

• Speed and Performance: It is one of the fastest AI infrastructure platforms available, with optimizations that deliver superior throughput for both training and inference.
• Cost-Effectiveness: By focusing on open-source models and optimized infrastructure, it provides a significantly more affordable alternative to proprietary AI services.
• Openness and Control: It champions the open-source ecosystem, giving users full control over their models and data, preventing vendor lock-in.
• End-to-End Solution: It provides a single, unified platform for the entire AI development lifecycle, simplifying workflows and accelerating time-to-market.

Pricing and Plans

Together AI offers a transparent, pay-as-you-go pricing model that scales with usage:

• Inference API: Priced per 1 million tokens (for both input and output). Rates vary depending on the model's size and family (e.g., Llama, Qwen, DeepSeek). Image models are billed per megapixel, and audio models per character.
• Dedicated Endpoints: For guaranteed performance, users can rent dedicated GPU instances, billed per hour. Prices vary by GPU type (e.g., RTX-6000, A100, H100).
• Fine-Tuning: Billed based on the number of tokens processed during training (dataset size multiplied by the number of epochs). Prices differ for LoRA and full fine-tuning.
• GPU Clusters: Reserved clusters with NVIDIA H100, H200, and Blackwell GPUs are available for hourly rental, with pricing starting from around $1.75/hour for an H100 GPU.
• Free Endpoints: Several models are available on free-to-use endpoints for testing and experimentation.