Together AI

Together AI is an “AI native cloud” platform that provides a full stack for developing, training, fine‑tuning, and deploying modern generative AI models on performance‑optimized GPU clusters. It is designed for AI‑first companies that need to run very large workloads—up to trillions of tokens in hours—while maintaining a consistent user experience and favorable unit economics. At its core, Together AI offers an integrated platform spanning a large model library, high‑performance inference, fine‑tuning, pre‑training, and managed GPU clusters, all backed by in‑house research that pushes frontier techniques into production quickly. ​ The platform’s model library lets teams evaluate and build with a wide range of open-source and specialized models for chat, images, video, audio, and code, including families like ChatKimi, Qwen, DeepSeek, GLM, and multimodal models such as Nano Banana Pro/Gemini‑based image models and Arcana audio models. Together AI exposes these through simple APIs, with support for OpenAI‑compatible interfaces so teams can migrate from closed ecosystems without rewriting their entire stack. This library is continuously expanded, including recent additions of 40+ new image and video generators, making the platform a central hub for state‑of‑the‑art open models across modalities. ​ Inference is a major focus area: Together AI advertises “unmatched price‑performance” at production scale, powered by systems such as the ATLAS adaptive learning speculator and the Together Inference Engine. These innovations aim to accelerate large language model inference via runtime‑learning and speculative decoding, enabling 3.5× faster inference, 2.3× faster training, and around 20% lower cost for certain workloads compared to conventional setups. Customers can deploy on frontier hardware like NVIDIA GB200 NVL72 and GB300 NVL72, choosing between serverless endpoints and dedicated capacity to match latency, throughput, and isolation requirements. ​ Beyond serving existing models, Together AI supports extensive customization via fine‑tuning and pre‑training workflows. Fine‑tuning tools allow organizations to adapt open models to their own data and tasks, producing smaller, faster variants that are tailored to domain-specific instructions and remain fully owned by the customer. For teams needing deeper control, pre‑training capabilities make it possible to train custom models from scratch on Together’s GPU clusters, leveraging research assets like the Together Kernel Collection (TKC) to improve stability and speed in large‑scale training. These custom models can then be deployed back onto Together’s inference stack, closing the loop from experimentation to production. ​ Underpinning these services is a global fleet of GPU data centers that Together AI positions as “AI factories,” ranging from self‑serve instant clusters to highly customized deployments for the largest workloads. Customers can scale across regions and hardware generations, taking advantage of aggressive network compression (117× in some benchmarks) and other systems optimizations that improve total cost of ownership. The company also invests heavily in open research and ecosystem contributions—projects such as FlashAttention, Mixture of Agents, Dragonfly, Red Pajama datasets, DeepCoder, and open agents—signaling a strategy of advancing open AI infrastructure and making those breakthroughs readily consumable in the cloud platform.

Together AI’s competitive edge comes from combining highly optimized AI infrastructure with an opinionated, model-centric platform that delivers better performance and economics than general-purpose hyperscalers or raw GPU clouds. It competes not just on cheap compute, but on a vertically integrated stack—compute, model APIs, and deployment platform—allowing optimizations “down the stack” that many rivals cannot match. ​ A first major advantage is price–performance. Analyses note that Together AI’s infrastructure can run workloads at up to roughly one‑fifth the cost of traditional hyperscalers, with estimates suggesting compute can be ~80% cheaper while maintaining gross margins around 45%. This comes from owning or aggregating thousands of GPUs across multiple secure facilities and layering custom virtualization, scheduling, and model-optimization software, which reduces idle time and improves utilization. Customers pay primarily on a token-based or per‑endpoint basis rather than by raw GPU hour, aligning costs with actual model use and making the platform particularly attractive to startups with spiky or unpredictable workloads.

• Small Businesses (1-99)
• Mid Market (100-500)
• Enterprise (500+)