Together AI

Overview

Together AI is a purpose-built AI-native cloud platform that provides a full-stack environment for developing, deploying, and scaling artificial intelligence applications. At its core, the platform integrates inference serving, fine-tuning, and GPU cluster management into a unified workflow. By focusing on the unique demands of AI workloads, Together AI abstracts away much of the underlying infrastructure complexity, allowing teams to concentrate on model quality, application logic, and product innovation. The platform is backed by cutting-edge research, ensuring that its architecture and tools reflect the latest advancements in machine learning and systems optimization. This research-driven approach makes Together AI a trusted foundation for organizations that need both immediate productivity and long-term scalability in an AI-powered world.

The platform offers a robust set of core capabilities for AI practitioners. Its inference services are designed to deliver low-latency, high-throughput responses for large language models and other deep learning architectures, whether they are deployed in staging or production environments. The integrated fine-tuning workflow allows teams to adapt pretrained models to specific tasks, domains, or company data, enabling higher accuracy and relevance without requiring a deep infrastructure stack. In addition, Together AI provides managed GPU clusters that offer scalable, on-demand compute resources. These clusters are tightly integrated with the platform, allowing users to move smoothly from experimentation to training to deployment without re-architecting their workflows.

For developers and researchers, Together AI serves as both an experimentation playground and a production-grade platform. Users can quickly spin up instances to test new architectures, fine-tune open-source models, or benchmark inference performance. As workloads mature, the same infrastructure can elastically expand to support larger training runs or higher traffic volumes. The platform's full-stack nature reduces the burden of integrating separate compute, storage, and inference services, making it an attractive choice for teams with limited infrastructure engineering resources. Its design philosophy centers on giving users control and flexibility while maintaining the operational reliability required in business-critical environments.

Together AI is positioned for a broad range of use cases, from building generative AI features in customer-facing products to training custom models for specialized research problems. Its AI-native architecture means the entire stack is optimized for AI tasks, avoiding the inefficiencies of general-purpose clouds. With a strong connection to the research community, the platform ensures users are not only consumers of AI but also part of an evolving ecosystem that advances what is possible. Whether you are a startup experimenting with large language models, an enterprise deploying AI at scale, or a researcher exploring new techniques, Together AI provides the infrastructure and tools to "build what's next" in artificial intelligence.

Core capabilities

AI Native Cloud

A purpose-built cloud environment optimized for AI workloads, offering a full-stack alternative to generic cloud infrastructure.

High-Performance Inference

Production-ready serving for large language models with low-latency and scalable throughput.

Fine-Tuning Suite

Integrated tools for adapting pretrained models to custom domains and tasks using proprietary or open-source data.

Managed GPU Clusters

On-demand, elastic access to GPU compute clusters for training and inference at scale.

Research-Driven Foundation

Cutting-edge research informs platform capabilities, ensuring users access the latest techniques and optimizations.

Full-Stack Development

Unified platform covering compute, models, and workflows across the entire AI lifecycle.

Scalable Infrastructure

Elastic resources that support rapid experimentation and seamless growth into high-scale production environments.

Developer-Centric Design

Streamlined tools and interfaces that reduce operational overhead for AI engineers and researchers.

Use cases

Running inference for large language models in production

Fine-tuning open-source models on domain-specific data

Training custom models on managed GPU clusters

Prototyping new AI features quickly with full-stack infrastructure

Scaling generative AI workloads without managing cloud infrastructure

Experimenting with cutting-edge AI research and architectures