Together AI

Company Overview

Together AI positions itself as "The AI Native Cloud" — a full-stack platform for AI inference, fine-tuning, pre-training, and GPU cluster management.^[1] What distinguishes Together AI from pure inference providers is its research DNA: the team created FlashAttention (now standard in virtually all transformer training) and has contributed Red Pajama datasets, Mixture of Agents, and DeepCoder to the open-source ecosystem.^[2]

Backed by NVIDIA, Salesforce Ventures, General Catalyst, and Kleiner Perkins, Together AI has positioned itself at the intersection of research innovation and production infrastructure.

What It Does

• Inference — 200+ models via OpenAI-compatible APIs, including Llama, Mistral, Qwen, and DBRX^[3]
• Fine-tuning — Custom model training on Together's infrastructure
• Pre-training — Full model training from scratch on dedicated clusters
• GPU clusters — Dedicated NVIDIA GB200/GB300 NVL72 hardware for large-scale workloads
• Serverless & dedicated — Choose between shared endpoints or reserved capacity

How It Works

Together AI runs on cutting-edge NVIDIA hardware, including GB200 and GB300 NVL72 racks. Their inference stack is optimized with custom kernels derived from FlashAttention research, plus integration with TensorRT-LLM and vLLM.

For inference, you call OpenAI-compatible API endpoints. For fine-tuning and pre-training, you configure jobs through the API or dashboard. GPU clusters provide dedicated multi-node hardware for organizations needing guaranteed capacity.

Pricing

• Per-token pricing for inference (varies by model)
• Per-GPU-hour for fine-tuning and training
• Dedicated clusters — custom pricing for reserved hardware
• Free credits available for new accounts

Together AI claims 20% lower cost than alternatives, though this varies by model and workload.^[1]

Strengths

• Research pedigree — FlashAttention creators bring genuine optimization expertise
• Latest hardware — GB200/GB300 NVL72 before most competitors
• Full stack — Inference + fine-tuning + pre-training + clusters under one roof
• Open-source contributions — Red Pajama, DeepCoder build community trust
• 200+ models — Broad selection with OpenAI-compatible APIs
• Performance claims — 3.5x faster inference, 2.3x faster training

Weaknesses / Risks

• Premium positioning — Cutting-edge hardware comes at a price
• Less enterprise compliance — SOC 2 but lacks HIPAA (vs Baseten, Fireworks)
• Crowded market — Competing with well-funded Baseten, Fireworks, and custom silicon players
• Research focus may diffuse — Balancing research contributions with product execution
• CodeSandbox acquisition — Expanding scope into developer tools may dilute inference focus

Competitive Landscape

vs. Baseten: Baseten leads on compliance (HIPAA) and GPU breadth. Together AI leads on hardware generation and research.

vs. Fireworks AI: Fireworks focuses on pure inference speed and reliability. Together AI offers a broader platform including pre-training.

vs. Groq/Cerebras: Custom silicon is faster for supported models. Together AI offers more model variety and fine-tuning.

vs. Modal: Modal is general-purpose serverless compute. Together AI is purpose-built for AI workloads with optimized serving.

Ideal User

• AI teams wanting cutting-edge hardware without managing infrastructure
• Organizations needing the full ML lifecycle (train → fine-tune → serve)
• Research groups that value open-source contributions and community alignment
• Companies running large-scale inference across many model types

Bottom Line

Together AI is the research-meets-production play in AI inference. The FlashAttention pedigree and latest NVIDIA hardware create genuine technical differentiation. Best for teams that want a single platform for the entire ML lifecycle with research-grade optimization. The risk is whether they can maintain focus as they expand into adjacent areas.

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