DeepSeek
Deploy DeepSeek V3 and R1 on Together AI. Transparent reasoning, frontier performance, and 90% cost savings vs closed-source alternatives.
Why DeepSeek on Together AI?
Designed for production workloads that need consistent performance and operational control.
Frontier performance at a fraction of the cost
DeepSeek’s MoE architecture delivers GPT-4 class performance at one-tenth the price. Together AI’s infrastructure adds 70–90% cost savings over closed-source alternatives.
Transparent reasoning, no black boxes
DeepSeek R1 exposes its complete chain-of-thought in <think> tags. Debug, verify, and trust your model’s reasoning process at every step.
Enterprise-ready from day one
SOC 2 Type II certified, HIPAA compliant, and deployed on US-based infrastructure. Full model ownership with no data retention by default.
Meet the DeepSeek family
Explore top-performing models across text, image, video, code, and voice.
Breakthrough technical innovations
Explore all the game-changing architectural advances that make DeepSeek models shine.
- Mixture of Experts (MoE)
Sparse expert routing activates only 37B out of 671B parameters for each token in V3. Advanced load balancing without auxiliary losses maintains performance while reducing computational cost.
- Group Relative Policy Optimization
New RL approach that removes separate value networks in RLHF, using grouped relative advantage estimation to cut compute requirements while maintaining training stability.
- Native Reasoning Transparency
First reasoning model to expose complete thinking process in <think> tags. Native reasoning capabilities built into model foundation through large-scale reinforcement learning.
- MetaP Training
First successful implementation of FP8 mixed precision training on a 671B parameter model. Pioneering reinforcement learning approach without supervised fine-tuning as preliminary step.
- Multi-Head Latent Attention
Innovative attention mechanism that reduces KV-cache memory requirements while maintaining modeling performance. Optimized for efficient inference deployment.
- Multi-Token Prediction
Novel training objective that allows the model to predict multiple tokens simultaneously. Enhanced performance and efficiency through advanced training techniques.
Deployment options
Run models using different deployment options depending on latency needs, traffic patterns, and infrastructure control.
Real-time
A fully managed inference API that automatically scales with request volume.
Best for
Batch
Process massive workloads of up to 30 billion tokens asynchronously, at up to 50% less cost.
Best for
Dedicated Model Inference
An inference endpoint backed by reserved, isolated compute resources and the Together AI inference engine.
Best for
Dedicated Container Inference
Run inference with your own engine and model on fully-managed, scalable infrastructure.
Best for