Together AI

Together AI is a cloud platform built for running, fine-tuning, and training open-source AI models at scale. Instead of managing your own GPU servers, you call one of Together AI’s 200+ hosted models, from Meta’s Llama family to DeepSeek and Mixtral, through a single OpenAI-compatible API. The platform spans serverless inference billed per token, dedicated GPU endpoints for predictable throughput, on-demand GPU clusters for training, and fine-tuning tools for customizing models on your own data. For AI developers who want the flexibility of open-source models without owning the infrastructure that serves them, Together AI removes the GPU procurement problem entirely.

How Together AI works

Together AI sits between raw GPU hardware and the application you are building, so you never provision a server yourself. You send a request to an endpoint and Together AI routes it to the right hardware behind the scenes. The platform breaks down into a few core pieces.

Serverless inference: You call a hosted model through the API and pay per token processed. There is no server to manage, and the platform handles scaling automatically as your traffic changes.

Dedicated endpoints: For workloads that need consistent latency or high volume, you can reserve GPU capacity for a single model. This costs more per hour but removes the variability of shared, serverless infrastructure.

GPU clusters: For training a model from scratch or running large batch jobs, Together AI rents out raw GPU clusters, including H100s, billed by the hour with on-demand or reserved pricing tiers.

Fine-tuning: Together AI lets you adapt an open-source model to your own data using LoRA (low-rank adaptation, a technique that trains a small set of additional parameters instead of the whole model) or full fine-tuning. Billing is based on tokens processed during training, not GPU time, which makes costs predictable before you start a job.

Model catalog and API compatibility: Every model, whether served or fine-tuned, is reachable through an OpenAI-compatible API. If your application already calls the OpenAI SDK, pointing it at Together AI’s base URL is usually the only change required.

What you can build with Together AI

Retrieval-augmented generation (RAG) system: A pipeline that retrieves relevant documents from your own data and feeds them to an open-source model for grounded, up-to-date answers. Together AI’s embedding and reranking models handle the retrieval side, while a hosted LLM generates the final response.

Custom fine-tuned support agent: A model tuned on your company’s past support tickets so it answers in your product’s terminology and escalation patterns. LoRA fine-tuning keeps the cost and turnaround low enough to iterate on the training data repeatedly.

Multi-model evaluation harness: A testing setup that runs the same prompt across Llama, DeepSeek, Mixtral, and Qwen to compare quality, latency, and cost before committing to one model in production.

High-volume batch processing pipeline: A job that classifies, summarizes, or extracts structured data from a large document set using serverless inference, scaling automatically without you sizing a GPU fleet in advance.

Self-trained domain model: A model trained from scratch or heavily fine-tuned on proprietary data using rented GPU clusters, for teams that need more control than fine-tuning an existing checkpoint provides.