Together AI's core pitch is breadth plus control: a large catalog of open-weight models available through a single API, plus the ability to fine-tune and deploy custom models on the same infrastructure — a different value proposition from a single-frontier-model provider, aimed at teams wanting model choice and customization rather than one best-in-class default.
Together AI provides API access to a broad catalog of open-weight models (Llama, Mixtral, Qwen, and many others) alongside fine-tuning and dedicated deployment options, letting developers choose or customize a model for their specific task and cost profile rather than relying on a single provider's proprietary model lineup.
Why Model Choice and Customization Matter (and When a Single Frontier Model Is Simpler)
Model choice matters when different parts of your application have genuinely different cost/capability tradeoffs — a high-volume classification task and a complex reasoning task benefit from different models, and having a wide catalog behind one API lets you match each task to an appropriately capable (and appropriately priced) model without integrating multiple providers.
A single frontier model provider is simpler when your application doesn't have meaningfully varying task complexity, or when the operational simplicity of one API and one model is worth more than the potential cost savings of matching models to tasks — the integration and evaluation overhead of managing multiple models isn't free.
Getting Started with Together AI
Basic request using the OpenAI-compatible API:
import Together from "together-ai";
const client = new Together({ apiKey: process.env.TOGETHER_API_KEY });
const response = await client.chat.completions.create({
model: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages: [{ role: "user", content: "Explain the CAP theorem in plain terms." }],
});
console.log(response.choices[0].message.content);
Fine-tuning a model on custom data:
const fineTune = await client.fineTuning.create({
training_file: uploadedFileId,
model: "meta-llama/Llama-3.3-70B-Instruct-Turbo",
n_epochs: 3,
});
Core Together AI Concepts Every Developer Should Know
A broad model catalog behind one API lets you tier model selection by task without multi-provider integration overhead — routing simple tasks to a smaller, cheaper model and complex tasks to a larger one, all through the same client and authentication, is more practical when the whole catalog is available from a single provider.
Fine-tuning lets you adapt an open model to your specific domain or task in ways that improve performance beyond what prompting alone achieves — this is a meaningfully different capability from providers offering only inference against fixed models, relevant specifically when you have enough domain-specific training data to make fine-tuning worthwhile.
Dedicated deployment options (versus shared inference endpoints) trade cost for predictable performance — a dedicated instance guarantees capacity and latency characteristics that shared, multi-tenant endpoints can't, relevant for production workloads where consistent performance under load matters more than minimizing per-token cost.
Open-weight models mean you're not dependent on a single provider's roadmap or pricing changes for the underlying model — you can self-host the same open weights elsewhere if needed, which is a meaningfully different risk profile than being fully dependent on a proprietary model only available through one provider.
Common Mistakes With Together AI and How to Fix Them
Mistake 1: using a single large model for all tasks despite having a full catalog available, missing the cost savings of tiering model choice by task complexity. Fix: evaluate whether smaller, cheaper models in the catalog handle simpler sub-tasks adequately, reserving larger models for tasks that need them.
Mistake 2: fine-tuning without enough quality training data to make it worthwhile, producing a model that doesn't meaningfully outperform prompting the base model well. Fix: evaluate whether your training data is sufficient in volume and quality before investing in fine-tuning versus improving prompts against a base model.
Mistake 3: using shared inference endpoints for production workloads needing consistent latency under load. Fix: evaluate dedicated deployment options for production paths where performance consistency matters more than minimizing per-token cost.
When Should You Fine-Tune Instead of Prompting a Base Model?
Fine-tune when you have substantial domain-specific training data and prompting alone hasn't achieved sufficient quality or consistency for your task — fine-tuning is an investment that pays off specifically when base-model prompting has a demonstrated, meaningful gap versus your requirements. Prompt a base model when you don't yet have enough training data, or when good prompting already achieves acceptable results — fine-tuning without that gap adds cost and complexity without proportional benefit.
Together AI in Production
Tier model selection across the available catalog by actual task complexity rather than using one model uniformly, and evaluate fine-tuning specifically when base-model prompting has a demonstrated quality gap for your domain. Use dedicated deployment for production paths needing consistent latency, and factor in the flexibility benefit of open weights when evaluating long-term provider risk.
If you're evaluating Together AI, start by mapping your application's actual task variety across its model catalog — the platform's core value is matching model choice to task, and that value is largest when your application genuinely has varied task complexity to match against.