Together AI

Together AI is an AI infrastructure platform built for developers and businesses that want to run, customize, and deploy open-source and open-weight AI models in production. The platform brings together model inference, fine-tuning, dedicated deployments, accelerated computing, and other tools needed to build AI applications at scale. Its model library includes more than 200 models across categories such as language, coding, vision, image, video, audio, embeddings, reranking, and moderation. Developers can work with models from organizations including Meta, Qwen, DeepSeek, Google, NVIDIA, and other major AI research groups, giving them the ability to compare different models and select one based on performance, cost, or a particular application requirement. It offers several ways to run these models depending on how much control and infrastructure an application needs. Serverless inference is designed for teams that want a managed API without maintaining GPUs, while dedicated model inference provides reserved compute for workloads where predictable performance and lower latency are important. There are also dedicated container options for teams running generative media models or models that require a non-standard runtime. This range of deployment choices makes the platform useful at different stages, from early experimentation to high-volume production applications. Fine-tuning is another major part of it. Developers can customize supported open-source models using their own datasets through methods such as LoRA and full fine-tuning, allowing businesses to improve model behavior for specific domains or applications without building their own training infrastructure.

The platform supports large models and multi-GPU training, which can be useful for teams working with more demanding workloads. It also provides batch inference for processing large volumes of requests asynchronously, along with accelerated compute, managed storage, and secure code sandboxes for AI development workflows. Pricing depends on the service being used. Serverless inference is generally charged according to usage, while dedicated infrastructure and deployed models can involve ongoing hosting costs. Fine-tuning is priced according to the amount of data and training method involved. For companies considering a Together AI alternative, important factors include the available model catalog, inference speed, deployment flexibility, fine-tuning support, GPU infrastructure, pricing, and the level of control offered over production workloads. Together AI is particularly well suited to engineering teams that want more than access to an AI API and need a broader platform for running, customizing, and scaling open models in real-world applications.