Together AI
Together AI builds a full-stack AI Native Cloud powered by systems research, helping teams train, customize, and deploy open models faster, more reliably, and at lower unit cost. Its stated mission is to make open-source AI accessible to developers and enterprises through open and responsible development.
At a Glance
- AI-native startups
- Enterprise software and large enterprises
- Foundation-model developers and research labs
- Developers and application teams building on open models
- +2 more
AI Tools by Together AI
(3)
HybridAgent
Hybrid Reasoning Agent Research Toolkit
Hallmark
AI Design Rules for Coding Agents
Together AI
AI Native Cloud Platform
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Latest News
HybridAgent public release: open-source hybrid reasoning agents with latent and text modes, HybridSFT, HybridRL, released models, and datasets.
Together Link launched to connect existing coding-agent harnesses to open models and cut spend by more than 50%.
Strategic partnership with Moonshot AI announced; Kimi K3 and future Moonshot open-weight models to launch natively on Together AI.
Announced an $800 million Series C at an $8.3 billion post-money valuation and more than 500 MW of additional compute commitments.
Products & Services
OpenAI-compatible, usage-billed API for production inference across chat, vision, image, audio, video, transcription, embeddings, reranking, and moderation models; supports 200+ open models across modalities.
Asynchronous inference for large workloads, with discounts of up to 50% versus real-time serverless inference.
Reserved, token-based inference capacity with SLA-backed throughput and predictable pricing.
Dedicated custom-hardware inference deployments for production models.
Market Position
Together AI positions itself as a research-optimized, full-stack alternative to hyperscaler AI clouds and closed-model providers: it combines open-model access, high-performance inference, model shaping, GPU clusters, and developer tooling in one platform, emphasizing lower cost, speed, ownership of model IP, and reduced vendor lock-in. Its competitive set includes AWS, Google Cloud/Vertex AI, Microsoft Azure, CoreWeave, Lambda, RunPod, Modal, Anyscale, Replicate, Fireworks AI, Groq, Together's model/API aggregators such as OpenRouter, and closed-model APIs from OpenAI, Anthropic, and Google.
Leadership
Founders
Vipul Ved Prakash
Serial entrepreneur and former Apple executive; Together AI's Founder & CEO.
Ce Zhang
Data-systems and machine-learning researcher, CTO and co-founder; also a Neubauer Associate Professor of Data Science at the University of Chicago and an expert in decentralized/distributed training and data-centric MLOps.
Chris Ré
Stanford AI and machine-learning professor, serial entrepreneur and co-founder with previous successful exits; a leading researcher in AI model architectures and infrastructure.
Percy Liang
Stanford AI researcher and professor, co-founder and leading researcher in AI model architectures and infrastructure.
Tri Dao
Researcher and creator of FlashAttention and FlashAttention-2; Together AI Founder & Chief Scientist.
Executive Team
Vipul Ved Prakash
Founder & CEO
Serial entrepreneur and former Apple executive.
Ce Zhang
Founder & CTO
Data-systems and machine-learning researcher and University of Chicago data-science professor.
Board of Directors
Founding Story
The founders started Together AI in 2022 after seeing generative AI as a turning point for human progress and believing that open-source models needed specialized, high-performance cloud infrastructure. Their initial vision was an open ecosystem and comprehensive cloud platform that would let developers and enterprises train, fine-tune, and run open and custom models without vendor lock-in, while making AI accessible and cost-effective.
Business Model
Revenue Model
Usage-based cloud and API revenue: per-token serverless and provisioned inference, per-hour dedicated inference and GPU clusters, plus usage-based batch inference, fine-tuning/post-training, evaluation, sandbox, and managed-storage services. Enterprise customers can also purchase dedicated capacity and SLA-backed deployments.
Pricing Tiers
No minimums or provisioning cost; pricing varies by model and modality.
NVIDIA H100, H200, B200, GB200 and other cluster configurations; price varies by GPU, term, and capacity.
LoRA and full fine-tuning pricing is model- and hardware-dependent.
Target Markets
- AI-native startups
- Enterprise software and large enterprises
- Foundation-model developers and research labs
- Developers and application teams building on open models
- Financial services, healthcare, education, media, retail, and other industries adopting generative AI
- Organizations needing private, compliant, or sovereign AI infrastructure
- Production inference for AI-native applications and agents
- Training, pre-training, fine-tuning, and post-training of foundation models
- Coding assistants and software-engineering agents
- Data analysis and code-interpreting LLM applications
- Multimodal image, video, audio, speech, and vision applications
- Enterprise RAG, embeddings, reranking, and model customization
- Cursor
- Cognition
- Decagon
- Zoom