Together AI is a full-stack platform for the entire AI development lifecycle. It is officially described as an "AI Native Cloud," driven by frontier research and covering core areas such as inference, fine-tuning, and GPU clusters. The platform aims to support the AI development journey from experimentation to large-scale production.
Purpose and positioning Together AI provides the infrastructure and model services needed to build and run AI applications. It officially states that it brings together "optimized" open-source models, and users can explore models covering directions such as text, images, video, code, and speech. The platform also targets production-grade workloads that need to deploy, fine-tune, and scale models.
Main capabilities The core services officially listed include:
- Serverless Inference: Run open-source models on demand, with no infrastructure to manage and no long-term commitment. It is officially described as the fastest way to run open-source models.
- Batch Inference: Process large-scale workloads asynchronously and cost-effectively, with support for scaling up to 30 billion tokens per model, and it can use any serverless model or private deployment.
- Provisioned Throughput: Provides committed inference capacity priced by token, reserved throughput, and a 99% uptime SLA, and emphasizes API compatibility for production workloads with no infrastructure to manage.
- Dedicated Model Inference: Deploy models on dedicated infrastructure, aimed at teams that need speed, control, and economics.
- Dedicated Container Inference: GPU infrastructure for generative media workloads, capable of deploying video, audio, and image models, with performance acceleration provided by Together Research.
In addition, the platform also provides Accelerated Compute (supporting scaling from self-service instant clusters to thousands of GPUs, optimized for the Together Kernel Collection), Sandbox (used to quickly and securely set up a complete development environment for AI applications and agents), and Managed Storage (high-performance object storage and parallel file systems for AI-native workloads, and it states zero egress fees).
Fine-tuning capabilities The platform supports fine-tuning open-source models. It officially states that it uses the latest research techniques, requires no management of training infrastructure, and can improve accuracy, reduce hallucinations, and control model behavior.
How to use Official pages show examples of using the code sandbox, involving @codesandbox/sdk, including steps such as creating a sandbox, connecting, running build commands, obtaining a preview address, and hibernating the sandbox while preserving a snapshot. However, the overall onboarding process, API call details, account registration, and billing methods are not yet provided in the materials.
Applicable scenarios According to the official description, Together AI is suitable for developers who need to run open-source models on demand, teams that need to handle large-scale asynchronous inference tasks, production workloads that need committed throughput and SLAs, teams that need dedicated infrastructure to deploy models, and generative media scenarios such as deploying video, audio, and image generation models. The platform also mentions a sandbox development environment for AI applications and agents.
Limitations and information not yet provided The materials do not provide specific pricing, billing standards, free quotas, specific parameters and performance metrics for each model, available regions, or restrictions beyond the service level. Users should refer to the latest official information.
Research and ecosystem Official pages list multiple research items and product announcements involving directions such as inference acceleration, quantization, long context, fine-tuning methods, and agent evaluation, and mention Together Link and the open-source AI stack (models, inference, router, tools, etc.). These contents reflect its research-driven platform positioning.
