Together AI

Together AI positions itself as an AI-native cloud, bringing model inference, training, and infrastructure together under one platform. Rather than stitching together separate vendors for GPUs, serving, and fine-tuning, teams can use a single environment to move from experimentation to production workloads.

The platform covers serverless and batch inference APIs, provisioned throughput with SLAs, dedicated model and container inference, and access to GPU clusters including B200, H200, and H100 hardware. Post-training tools support custom RL and SFT loops, managed fine-tuning with SFT and DPO, and evaluations for measuring model quality. Managed storage for model weights and data, plus sandbox developer environments, round out the stack.

It is aimed at AI developers, research teams, and startups that build on open-source models and need scalable inference or training capacity without operating their own infrastructure. The model catalog spans chat, image, code, vision, and video models, which helps teams compare and deploy options quickly.

A practical approach is to start with serverless inference to validate a model, then move to provisioned throughput or dedicated capacity as traffic grows. Teams planning custom training should evaluate post-training and fine-tuning options alongside GPU cluster availability to control cost and performance.

Overall, Together AI is a consolidated option for teams that want open-model flexibility with managed infrastructure.