An AI cloud platform for teams building, tuning, and deploying open-source models.
Together AI is a full-stack AI cloud platform for developing and operating applications with open-source models. It brings together model inference, fine-tuning, GPU compute, and supporting infrastructure, so teams can move from experimentation to deployed workloads within one platform. Inference options range from on-demand serverless access to reserved throughput and dedicated deployments; compute includes GPU clusters, while model shaping supports fine-tuning. The platform also offers managed storage and code sandboxes. Together Research works on production-oriented inference systems, GPU kernels, model shaping, and agents, informing the platform’s focus on AI workloads.
Run models through serverless inference, asynchronous batch processing, provisioned throughput, or dedicated model infrastructure. These options support different workload patterns, from on-demand requests to reserved capacity and dedicated hardware.
Fine-tune models for production workloads using supervised fine-tuning and direct preference optimization. Together AI describes fine-tuning as a way to adjust model accuracy and behavior without managing training infrastructure.
Provision GPU capacity from self-serve clusters to larger deployments. Together AI also offers its Kernel Collection as an optimization layer for compute workloads.
Dedicated container inference is designed for deploying video, audio, and image models on GPU infrastructure, with performance acceleration from Together Research.
Code sandboxes provide environments for AI applications and agents, including a code-interpreter API. Managed storage includes object storage and parallel filesystems for AI workloads.
Use serverless model inference to evaluate available open-source models and connect model capabilities to an application without first managing serving infrastructure.
Submit batch inference jobs for high-volume processing when results do not need to be returned interactively. The service supports serverless models and private deployments.
Choose provisioned throughput for reserved capacity and token-based pricing, or dedicated inference when workloads call for dedicated hardware, custom models, and autoscaling.
Fine-tune an open-source model with supervised fine-tuning or direct preference optimization to shape its behavior for a production workflow.
Combine speech-to-text, language models, and text-to-speech through the voice offering. Its model library and shared API let teams switch voice pipeline models without rebuilding integrations.
The platform supports model inference, fine-tuning, GPU compute, code sandboxes, and managed storage. It also provides a voice solution for building real-time voice agents.
Options include serverless inference, batch inference, provisioned throughput, dedicated model inference, and dedicated container inference. The right option depends on whether the workload is asynchronous, needs reserved capacity, or calls for dedicated infrastructure.
Together AI offers fine-tuning for open-source models and lists supervised fine-tuning and direct preference optimization as available methods. Its product description says training infrastructure does not need to be managed by the user.
Pricing varies by service: serverless models are listed with per-token rates, media models use units such as images, video, characters, or audio minutes, and GPU or sandbox resources have usage-based rates. Some dedicated and reserved options direct customers to contact sales.
Los datos de tráfico son solo como referencia.
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