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. As of July 2026, it supports the full Llama 4 family, Gemma 3, Qwen 3.5, and specialized coding models, all running on the latest NVIDIA Rubin architecture.

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. It provides a performance-optimized alternative to cloud giants, often compared to claude-4-8-opus-20260528 and GPT-5.5 for specific vertical tasks and high-throughput agentic workflows.

Where it fits in the stack¶

Inference Provider. It acts as the backend for applications using open-weights models and custom fine-tuned adapters.

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.
Agentic Orchestration: Serving as a reliable backend for agents using MCP 3.0 for tool-integrated reasoning.

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 FlashAttention-3 and Liger Kernel 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 4.8 Opus or GPT-5.5.
If you have strict regulatory requirements to keep all data on your own local hardware.

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-4-70b-chat-hf",
    messages=[{"role": "user", "content": "Benefits of open source AI in July 2026?"}],
)
print(response.choices[0].message.content)

CLI examples¶

# List all available models
together models list

# Query a model directly via CLI
together chat "meta-llama/Llama-4-70b-chat-hf" --prompt "Hello"

# Start a fine-tuning job
together fine-tuning create --training-file "data.jsonl" --model "llama-4-8b"

API examples¶

Fine-Tuning and LoRA Deployment¶

Together AI provides a unified API for fine-tuning open models and deploying them as custom adapters.

from together import Together

client = Together()

# List available fine-tuned models
fine_tuned_models = client.fine_tuning.list()

# Inference with a custom adapter
response = client.chat.completions.create(
    model="accounts/your-account/models/your-finetuned-llama-4",
    messages=[{"role": "user", "content": "How should I summarize this report?"}],
)
print(response.choices[0].message.content)

Dedicated GPU Cluster Usage¶

For high-volume production, Together allows provisioning dedicated GPU clusters for guaranteed throughput.

# Usage involves pointing your client to the dedicated model endpoint
response = client.chat.completions.create(
    model="meta-llama/Llama-4-70b-chat-hf",
    extra_body={"dedicated_cluster": "cluster-id-123"},
    messages=[{"role": "user", "content": "Batch process these items."}],
)

Sources / references¶

Contribution Metadata¶

Last reviewed: 2026-07-01
Confidence: high