Together AI is an AI-native cloud for open-source models, offering fast serverless inference, batch processing, dedicated endpoints, GPU clusters and fine-tuning on one research-driven platform.
Together AI built its cloud specifically for generative AI workloads rather than adapting general-purpose infrastructure. Its inference stack, informed by the team’s systems research (including work on FlashAttention and speculative decoding), claims up to 2x faster inference and up to 60% lower cost than typical alternatives for popular open models. Developers can call hundreds of open-source models — chat, code, image, video and audio — through serverless APIs with no infrastructure to manage and no long-term commitments.
Beyond inference, the platform covers the whole model lifecycle. Batch inference processes massive asynchronous workloads cost-effectively, provisioned throughput reserves committed capacity with a 99% uptime SLA, and dedicated deployments run models on isolated infrastructure. Teams that need to shape models can fine-tune open-source weights with managed training, spin up GPU clusters from a few cards to thousands for pre-training, and use secure code sandboxes to run agent workloads at scale — all with AI-optimized storage that carries zero egress fees.
Together AI is pay-as-you-go. Serverless inference is billed per million tokens at published per-model rates — many leading open models cost well under $1 per million tokens — while batch jobs, dedicated endpoints and GPU clusters are priced by capacity and duration. There are no platform subscriptions; you pay only for the compute and tokens you consume, with committed-use arrangements available for production scale.
Teams building products on open-source models who need production-grade speed, scale and the option to fine-tune or train. If you only need a single proprietary frontier model, a direct provider API may be simpler.