Together AI¶

What it is¶

Together AI is a cloud platform for building and running generative AI, offering high-performance inference for a wide range of open-source models (Llama 3, Qwen, Mistral, Gemma).

What problem it solves¶

Simplifies the deployment of open-source models by providing a fast, serverless API, eliminating the need to manage complex GPU infrastructure for models.

Where it fits in the stack¶

Inference Provider. It acts as the backend for applications using open-weights models.

Typical use cases¶

Multi-Model Testing: Quickly switching between different open models to find the best fit for a specific task.
Cost Optimization: Using Together's efficient inference to lower API costs compared to proprietary flagship models.
Fine-Tuning: Training and deploying custom LoRA adapters of open models on proprietary data.

Getting started¶

Install the SDK:

pip install together

Basic API call (Python):

from together import Together

client = Together()

response = client.chat.completions.create(
    model="meta-llama/Llama-3-70b-chat-hf",
    messages=[{"role": "user", "content": "What are the benefits of open source AI?"}],
)
print(response.choices[0].message.content)

Strengths¶

Model Variety: Supports hundreds of open-source models across text, image, and code (LLMs, Diffusion, etc.).
Speed: One of the fastest inference providers on the market due to specialized optimizations.
Features: Offers serverless API, dedicated clusters, and integrated fine-tuning workflows.
Pricing Tiers: Offers aggressive Serverless pricing (usage-based, very low cost) and Dedicated Clusters for predictable performance and high throughput.

Limitations¶

Third-Party Dependency: Relying on their platform for uptime and security of the hosted open models.
Complexity: Navigating the massive library of models can be overwhelming for beginners.

When to use it¶

When you want to use top-tier open-source models without the hassle of self-hosting.
When low latency and high throughput are critical for your application.
For scaling applications that require fine-tuned open models with custom LoRA adapters.

When not to use it¶

If you require the specific proprietary reasoning capabilities of models like Claude 3.5 Sonnet or GPT-4o.
If you have strict regulatory requirements to keep all data on your own local hardware.

Licensing and cost¶

Open Source: The platform is proprietary; the models it hosts are mostly open-weights (Llama, Mistral, etc.).
Cost: Paid (Usage-based).
Self-hostable: No (Cloud service), but the models can be hosted elsewhere if needed.

Sources / References¶

Contribution Metadata¶

Last reviewed: 2026-03-03
Confidence: high