Freemium
All-in-one productivity platform for tasks, docs, goals, and team collaboration
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Pricing
Freemium
Use cases
Developers building generative AI applications who require a serverless inference API to access open-source models for chat, image, and video generation (verified: 2026-01-29)., Founders scaling AI startups who need self-service NVIDIA GPU clusters to manage high-performance computing workloads and dedicated endpoints (verified: 2026-01-29)., Enterprise teams fine-tuning large language models with long contexts using specialized platform upgrades to improve model performance on specific datasets (verified: 2026-01-29).
Strengths
The platform provides OpenAI-compatible APIs which allow developers to migrate from closed models to open-source alternatives without rewriting significant portions of their codebase (verified: 2026-01-29)., Users can access the Batch Inference API to process billions of tokens at a fifty percent lower cost compared to standard inference for most models (verified: 2026-01-29)., The infrastructure includes ATLAS runtime-learning accelerators that deliver up to four times faster inference speeds for large language model workloads (verified: 2026-01-29).
Limitations
Access to high-performance hardware like NVIDIA HGX B200 and H200 clusters requires specific hourly rates or custom pricing agreements (verified: 2026-01-29)., Full fine-tuning and LoRA capabilities are subject to specific platform pricing tiers based on model size and training duration (verified: 2026-01-29).
Last verified
Jan 29, 2026
Strengths
- The platform provides OpenAI-compatible APIs which allow developers to migrate from closed models to open-source alternatives without rewriting significant portions of their codebase (verified: 2026-01-29).
- Users can access the Batch Inference API to process billions of tokens at a fifty percent lower cost compared to standard inference for most models (verified: 2026-01-29).
- The infrastructure includes ATLAS runtime-learning accelerators that deliver up to four times faster inference speeds for large language model workloads (verified: 2026-01-29).
Limitations
- Access to high-performance hardware like NVIDIA HGX B200 and H200 clusters requires specific hourly rates or custom pricing agreements (verified: 2026-01-29).
- Full fine-tuning and LoRA capabilities are subject to specific platform pricing tiers based on model size and training duration (verified: 2026-01-29).
FAQ
What types of hardware options are available for developers needing dedicated GPU resources on the platform?
Together AI provides self-service NVIDIA GPU clusters, including options for NVIDIA H100, H200, and HGX B200 hardware. These resources are available through instant clusters or dedicated endpoints to support scaling AI infrastructure for startups and enterprises (verified: 2026-01-29).
How does the platform assist developers who want to migrate their applications from OpenAI to open-source models?
The platform offers a Model Library featuring open-source models for chat, images, and code that are accessible via OpenAI-compatible APIs. This compatibility simplifies the migration process by allowing developers to use familiar integration patterns while switching to open-source alternatives (verified: 2026-01-29).
What specific tools does Together AI provide for optimizing the cost and speed of large-scale inference?
Together AI offers the Batch Inference API for processing large volumes of tokens at reduced costs and the ATLAS runtime-learning accelerator for increasing inference speed. These tools are designed to help builders manage performance and expenses during AI-native development (verified: 2026-01-29).
