What is Together AI used for?

Together AI is a platform to run, train, and serve open-source AI models. It offers serverless inference, fine-tuning, GPU clusters, and code execution sandboxes for developers and businesses.

How does pricing work on Together AI?

Pricing varies by service: serverless inference is charged per million tokens (from $0.00 to $4.40), provisioned throughput costs $0.05 per PTU per minute, dedicated inference and GPU clusters are per GPU per hour (e.g., H100 from $3.99), and fine-tuning is per million tokens based on model size.

Which models are available on Together AI?

Together AI hosts many popular open-source models including DeepSeek V4 Pro, MiniMax M3, Qwen3.5, Llama 3.3, Gemma 4, and custom models. They also support image, audio, video, and vision models.

Can I fine-tune my own model on Together AI?

Yes, Together AI offers both supervised fine-tuning and direct preference optimization. You can use LoRA or full fine-tuning on models up to 100B parameters, and then deploy the fine-tuned model for inference.

Is there a free tier or free trial?

No, Together AI does not offer a free tier. However, some models like PrismML Ternary Bonsai 27B have $0.00 input cost on serverless, and you can start with pay-as-you-go pricing without upfront commitment.

Together AI

Updated Jul 25, 2026

Explore Together AI's comprehensive docs for running, training, and serving open-source AI models. Includes APIs, fine-tuning, GPU clusters, and more.

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Editor's Verdict

Rating: 4.5/5.0•Reviewed by RAGWiki

At From $0.00 to $4.40 per 1M tokens, Together AI stands out as a powerful solution in the developer tools,code assistant,chatbots landscape. It is especially well-suited for professionals like AI/ML Engineer and Data Scientist. However, potential buyers should note that it might not be perfect if you are strictly trying to avoid complex pricing structure. Overall, it offers a robust toolset that significantly accelerates workflows.

Key Takeaways

Serverless Inference
OpenAI Compatibility
Fine-Tuning
GPU Clusters

In-Depth Review: What is Together AI?

Together AI provides a powerful platform to run, train, and serve open-source AI models. With an OpenAI-compatible API, serverless and dedicated inference, fine-tuning, GPU clusters, and code execution, developers can build and deploy AI applications quickly. Explore our extensive documentation for quickstarts, guides, and examples to get started.

Core Features

Serverless Inference

Run leading open-source AI models on demand, charged per token used with no upfront commitment.

OpenAI Compatibility

Use Together AI's API with OpenAI-compatible SDKs and endpoints for seamless integration.

Fine-Tuning

Fine-tune models on your own data using supervised or preference tuning, then deploy for inference.

GPU Clusters

Spin up H100 and B200 clusters with attached storage for large-scale training or batch jobs.

Code Execution Sandbox

Run Python code securely alongside model calls using Together AI's sandbox or code interpreter.

Dedicated Inference

Deploy single-tenant GPU instances with guaranteed performance, autoscaling, and custom model support.

Multi-Modal Models

Access a wide range of chat, vision, image, audio, video, and embedding models for diverse AI tasks.

Batch Processing

Queue asynchronous generations and fetch results later at reduced Batch API prices.

LLM Evaluations

Automate scoring of model outputs with LLM judges and reports using the evals API.

Provisioned Throughput

Reserve dedicated throughput units (PTUs) for predictable latency and capacity on popular models.

Pricing

Serverless Inference

From $0.00 to $4.40 per 1M tokens

Pay per token for input and output
Cached input tokens at discount
Batch API with lower prices
Access to all serverless models

Provisioned Throughput

$0.05 per PTU per minute

Reserved capacity in throughput units
Fixed tokens-per-minute per model
Predictable latency and throughput

Dedicated Inference

From $5.49 per GPU per hour (H100)

Single-tenant GPU instances
Support for custom models
Autoscaling and traffic spike handling
Guaranteed performance

GPU Clusters

From $3.99 per GPU per hour (H100)

On-demand and reserved capacity options
High-bandwidth shared filesystem storage ($0.16/GiB/month)
Health checks and node repair

Sandbox (Code Interpreter & VM)

$0.0446 per vCPU/hour + $0.0149 per GiB RAM/hour; Code Interpreter $0.03/session

Secure code execution for LLM outputs
Customizable VM deployments
Session-based pricing

Fine-Tuning

From $0.48 per 1M tokens (LoRA SFT, up to 16B model)

Supervised Fine-Tuning and Direct Preference Optimization
LoRA and full fine-tuning options
Minimum $4.00 per job

Pros and Cons

Pros

OpenAI-Compatible APIEasily switch from OpenAI with SDK support in Python, TypeScript, and cURL.
Wide Model SelectionAccess hundreds of open-source models including DeepSeek, Qwen, Llama, and custom models.
Flexible Deployment OptionsChoose between serverless, provisioned throughput, dedicated instances, or GPU clusters for any workload.
Built-in Fine-TuningFine-tune models on your own data and deploy them directly without third-party tools.
Code Execution SandboxRun Python code securely with the hosted sandbox, ideal for agents and data processing.

Cons

Complex Pricing StructureMultiple pricing models (per token, per PTU, per GPU hour, per session) can be confusing to estimate costs.
No Free TierAll services are paid; no free usage tier for experimentation.
Limited to Open-Source ModelsOnly open-source and custom models are available; no proprietary models like GPT-4 or Claude.
Potential Latency on ServerlessServerless inference may have higher latency for large outputs due to resource sharing.
Requires Technical IntegrationUsers need API knowledge to integrate; no no-code interface for building applications.

Use Cases & Recommended Professions

AI/ML Engineer→ View Toolkit

Need to deploy, fine-tune, and scale open-source models for production applications.

Data Scientist→ View Toolkit

Use serverless inference for prompt experiments and batch processing of large datasets.

Research Scientist→ View Toolkit

Leverage GPU clusters for training custom models and running evaluations.

Full-Stack Developer→ View Toolkit

Integrate AI capabilities into web apps using OpenAI-compatible API and code sandbox.

Startup CTO→ View Toolkit

Seek flexible, scalable infrastructure for AI products without heavy upfront investment.

AI Product Manager→ View Toolkit

Evaluate and compare model performance using dedicated inference and evals API.

Frequently Asked Questions

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ℹ️ Curation Disclosure: The overview and features of Together AI were synthesized using AI and fact-checked by our curation team to ensure accuracy.