Together AI launches Post-Training service for custom models
Together AI introduced Post-Training for RL and SFT workflows, with dedicated GPU resources, multi-LoRA support and introductory B200 pricing. (Read More)
Asanat Analysis — Why it matters
Together AI's Post-Training service targets a specific inefficiency in the AI model development stack: the gap between pre-trained foundation models and production-ready systems. By offering dedicated GPU resources with multi-LoRA support, the platform enables cost-efficient customization without full model retraining. This addresses a real bottleneck—most organizations lack in-house infrastructure for reinforcement learning (RL) and supervised fine-tuning (SFT) at scale, making outsourced post-training a logical service expansion.
The timing signals intensifying competition in AI infrastructure as training commoditizes. With introductory B200 pricing (Nvidia's latest flagship GPU), Together AI is pricing aggressively to lock in adoption early. This mirrors broader market dynamics: as foundation models become standardized, margin pressure forces providers to move downstream into application-layer services. The multi-LoRA architecture is particularly notable—it hints at emerging demand for model personalization and federated fine-tuning workflows, suggesting enterprise customers want customization without vendor lock-in.