What is Together AI?
Together AI describes itself as the AI Native Cloud. Its product menu groups the platform into inference, compute, model shaping and developer environments, with a library of open-source models for chat, image, code, vision and video.
Inference comes in several forms: serverless inference as APIs, batch inference for batch workloads, provisioned throughput with token-based capacity and SLAs, dedicated model inference on custom hardware, and dedicated container inference for custom models. On the compute side, Together offers GPU clusters and managed storage for model weights and data. Model shaping covers fine-tuning with managed SFT and DPO jobs, post-training where you define your own RL and SFT loop, and evaluations to measure model quality. A sandbox product provides development environments for AI.
Together AI also publishes systems research, with FlashAttention among its featured publications. Pricing is published on a dedicated pricing page and depends on the model and product chosen.
Key capabilities
- Serverless inference High-performance inference as APIs.
- Batch and provisioned inference Batch workloads, and token-based capacity with SLAs.
- Dedicated inference Custom hardware or containers for custom models.
- GPU clusters Reliable clusters at scale.
- Fine-tuning and post-training Managed SFT and DPO jobs, or your own RL and SFT loop.
- Evaluations Measure model quality.
Where Together AI fits
- Serverless inference The fastest way to run open-source models on demand, with no infrastructure to manage and no long-term commitments.
- Batch inference Asynchronous processing of massive workloads, scaling to 30 billion tokens per model with any serverless model or private deployment.
- Provisioned and dedicated inference Token-based pricing, reserved throughput and a 99% uptime SLA, or models deployed on dedicated infrastructure.
- GPU clusters Scale from self-serve instant clusters to thousands of GPUs, all optimized for better performance with Together Kernel Collection.
- Managed storage Object storage and parallel filesystems optimized for AI, with zero egress fees.
- Fine-tuning and post-training Fine-tune open-source models for production workloads without managing training infrastructure.
- Sandboxes Fast, secure code sandboxes at scale for development environments for AI apps and agents.
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