Do this after you can already call models through the API. The fine-tuning flow builds on the same account and keys.

When is fine-tuning worth it?

Fine-tune when you need a consistent output format, a specific tone, or domain knowledge that is too large to fit in a prompt. If a good prompt already solves the task, prompting is cheaper and faster to iterate. Fine-tuning pays off at volume, where per-call prompt length starts costing more than a tuned model would.

How do you prepare the data?

Use JSONL with one example per line. For chat models, each line holds a messages array with system, user and assistant turns.

1. Clean your examples — remove duplicates and bad outputs.
2. Format as JSONL, one conversation per line.
3. Aim for a few hundred to a few thousand high-quality rows, not a huge noisy set.
4. Split off a small validation slice.
5. Upload the file through the API or dashboard.

Quality beats quantity. 500 clean examples usually outperform 5,000 messy ones.

How do you launch and monitor a job?

Create a fine-tuning job with the base model ID and your file ID, then poll the job status until it completes. The platform reports loss over training steps so you can spot a model that is not learning. Training a modest model on a few thousand rows typically finishes within hours, not days.

How do you use the tuned model?

Once the job finishes, you get a new model ID you can call through the same API, at a per-token price that depends on the base model size. For consistent production traffic, attach a dedicated endpoint so capacity is reserved and latency stays stable. Test the tuned model against your prompt-only baseline before switching production over.

What does it cost, and what are the limits?

You pay for training time plus storage, then per-token inference on the tuned model. The costs are modest compared with renting GPUs yourself. Two caveats: you cannot fine-tune every model in the catalog, and a tuned model can get worse at general tasks as it specializes — keep the base model around for everything else.

Key Takeaways

• Together AI fine-tuning takes a JSONL dataset and returns a callable model.
• Format chat data as one messages array per line.
• Hundreds of clean examples often beat thousands of noisy ones.
• You pay for training, storage, then per-token inference.
• Not all models are tunable; a tuned model may lose general ability.

Last updated October 2026. More at Together AI.

The appeal is cost and choice. If you want a capable open model without managing GPUs, Together AI is one of the simpler routes. It also offers fine-tuning and dedicated endpoints for heavier use.

What is Together AI?

Together AI is an inference and training platform for open models. It exposes a chat completions API compatible with OpenAI’s SDK, plus endpoints for embeddings and images. Models run on their infrastructure, so you send a request and get tokens back without provisioning hardware.

How do you get started?

1. Create an account and generate an API key from the dashboard.
2. List available models via the models endpoint and note the exact IDs.
3. Call the chat completions endpoint with a model ID like meta-llama/Llama-3.3-70B-Instruct-Turbo.
4. Use the OpenAI Python client by setting base_url to the Together endpoint.

Because the interface matches OpenAI, moving existing code over is mostly a one-line change.

Which models are available?

The catalog covers large open models across sizes and licenses, from small fast ones to 70B-class and larger mixture-of-experts models. Model IDs follow a vendor/name pattern. Availability changes as new releases land, so always pull the live model list rather than hardcoding an ID.

How does pricing work?

You pay per input and output token, with rates that vary by model — small models are cheap, large ones cost more. Some models offer a free or heavily discounted tier for experimentation. The starting credit lets you benchmark before committing. For steady high volume, dedicated endpoints give fixed capacity but a higher floor price.

What are the limitations?

You get less control than running the model yourself — no custom quantization or kernel tweaks. Some models have usage restrictions tied to their licenses. And though the API looks like OpenAI’s, edge features (certain tool-calling or response formats) may differ per model, so test your exact use case rather than assuming parity.

Key Takeaways

• Together AI serves 100+ open-source models behind an OpenAI-compatible API.
• Swap in existing OpenAI SDK code by changing the base URL.
• Pricing is per token and varies by model; new accounts get starting credit.
• Also offers embeddings, images and fine-tuning.
• Less low-level control than self-hosting; check license terms per model.

Last updated October 2026. Details at Together AI.