Together AI Tutorial: Getting Started the Right Way

A Together AI tutorial that takes you from account creation to your first API call in five steps, plus model choices, pricing, and cost controls.

By Galactic News Editorial · Published 2026-11-09T10:21:04+00:00 · Updated 2026-11-09T10:21:04+00:00

Together AI Tutorial: Getting Started the Right Way

Together AI is a cloud platform that runs more than 200 open-weight models behind an OpenAI-compatible API. You send a request, you get a response, and you pay per token. You never provision a GPU. This Together AI tutorial takes you from account creation to your first working API call, then shows how to pick a model and control costs.

What Together AI Is (and Is Not)

Together AI hosts open-weight models from Meta, Mistral, Qwen, DeepSeek, and others, and exposes them through an HTTP API. It also offers fine-tuning, dedicated GPU endpoints, and image generation. The platform is serverless inference: Together manages the hardware, and you pay for the tokens you use.

It is not a chatbot app. There is no web chat window for casual use. It is a developer platform, and the main way in is the API. It is also not a model trainer by default. Fine-tuning exists, but most users start with hosted inference and never touch it.

Together AI Tutorial: Your First API Call in 5 Steps

Follow these steps in order. The whole setup takes a few minutes.

1. Create an account and an API key. Register at together.ai. Go to your project's API keys page. Select Create key, give it a name, and copy the value. New keys are only shown once, so save it somewhere safe. Treat the key like a password. Never commit it to a repository.
2. Set the key as an environment variable. In your terminal, run export TOGETHER_API_KEY=<your-key> on macOS or Linux. On Windows PowerShell, use $env:TOGETHER_API_KEY="<your-key>". The official SDK reads this variable automatically when you call Together().
3. Install the SDK. Together publishes official SDKs for Python and TypeScript. For Python, run pip install together. If you already use the OpenAI SDK, you can point it at Together's base URL instead: https://api.together.ai/v1. The rest of your code stays the same.
4. Send your first request. Use the chat completions endpoint with a model from the catalog, such as MiniMax M3.1 Flash. Send a short prompt and print the response. If it prints, your key, your install, and your network all work. If it fails, check the key first. An expired or mistyped key is the most common error.
5. Check your usage and billing. Together bills serverless inference per token from a prepaid balance. As of late October 2026, the first purchase is at least $15, with $5 top-ups after that, and the docs say Together does not currently offer free trials. Confirm the current terms in your account before you scale.

Choosing a Model

The catalog changes often as new open releases land. Treat any specific model name as a snapshot, not a permanent fixture. List the current set through the models endpoint and pin the version you want in your own config. These examples show the price range on the platform as of late October 2026.

Prices are per million tokens and match the model makers own list prices on several models. Together charges the maker rate rather than undercutting it. Cached input is cheaper on select models, so repeated prompts with a shared prefix can cut costs. Check the live pricing page before you size a workload, because rates move with new releases.

Streaming, Structured Output, and Images

Three features cover most next steps after the first call. Streaming returns the response token by token instead of making you wait for the full reply. Set stream=True in Python or stream: true in TypeScript. This matters most with reasoning models, which work through a problem before answering. Their thinking arrives in a reasoning_content or reasoning field, and the answer arrives in content.

Structured output gives you parseable JSON back. Pass a JSON schema through response_format and the model returns data your code can use directly, without regex cleanup.

Image input works with models that accept it, such as MiniMax M3.1 Flash. Add an image_url block to the user message to ask questions about a picture. The same chat endpoint handles text and images.

Costs and Limits to Know Before You Scale

Serverless requests are billed per token from your balance. They are best-effort, which means under high demand they can be throttled with a 429 or 503 error. Build retries with backoff into any production code.

Rate limits vary by model and account tier. The free tier runs on the order of 60 requests per minute. Paid tiers often run near 600 requests per minute with around 1 million tokens per minute, depending on the model. Check the limits page in your account for the exact numbers on your plan.

Two cost controls matter most. First, pick the smallest model that does the job. A classification task does not need a frontier model. Second, use cached input where the model supports it. Repeated system prompts and documents are the easiest savings on the platform.

Common Mistakes to Avoid

The most common mistake is hard-coding a model name and a price into your code, then forgetting both. Models get updated and prices change. Read the model list from the API and keep prices in config, not in code.

The second mistake is committing the API key. Keys leak through notebooks, screenshots, and shared repos. Use the environment variable, and rotate the key if it ever appears anywhere public.

The third mistake is skipping error handling. A 429 is not a bug in your code. It is the platform telling you to slow down. Retry with a delay, and log the failures so you can see patterns.

Bottom Line

Getting started with Together AI takes five steps: create a key, set the environment variable, install the SDK, send one request, and check billing. The platform suits developers who want open-weight models without managing GPUs. Start with a cheap model, pin the version, and add retries before you scale. The first call is the hard part. After that, it is ordinary API work.