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Together AI

By Together AI

IntermediatePlatform1.9K learners

Together AI is a cloud platform that provides API access to open-source large language models for inference and fine-tuning, along with GPU cloud infrastructure for training AI models at scale.

Definition

Together AI is a cloud platform that provides API access to open-source large language models for inference and fine-tuning, along with GPU cloud infrastructure for training AI models at scale.

Overview

Together AI focuses on making open-source models — such as Llama, Mixtral, and other community-released LLMs — as easy to use as proprietary APIs like OpenAI's. Developers call hosted models through a standard API for chat, completion, embeddings, and image generation, without needing to provision or manage GPU servers themselves. Beyond hosted inference, Together AI offers fine-tuning services so teams can customize open-source models on their own data, and dedicated GPU cloud infrastructure for larger training and inference workloads that need more control than a shared API endpoint. This combination of managed inference plus raw compute access positions it between fully managed model APIs and infrastructure-only providers like RunPod. Because it specializes in optimizing inference speed and cost for open large language models, Together AI is often compared with other open-model inference platforms such as Groq and Fireworks AI, with competition centered on latency, throughput, pricing, and the breadth of supported models.

Key Features

  • Hosted API access to a wide range of open-source large language models
  • Fine-tuning services for customizing open models on proprietary data
  • Dedicated GPU cloud instances for training and large-scale inference
  • Optimized inference infrastructure for lower latency and cost
  • Support for embeddings, chat, and image-generation model endpoints
  • Compatibility with common API formats used by other LLM providers
  • Usage-based pricing without requiring long-term infrastructure commitments

Use Cases

Building applications on open-source LLMs without managing GPU infrastructure
Fine-tuning open models on domain-specific or proprietary datasets
Running production inference workloads at lower cost than proprietary APIs
Accessing dedicated GPU capacity for large training jobs
Comparing multiple open-source models through a single API
Powering retrieval-augmented generation and agent applications with open models

Frequently Asked Questions