Together AI

Technical Use Cases

Together AI is well-suited for the following engineering and ML workloads:

• High-throughput serverless inference — serve open-source LLMs at scale without managing GPU infrastructure, billed per token
• Large-scale batch processing — run offline inference jobs up to 30 billion tokens per model, ideal for data pipelines, evaluation, or content generation at scale
• Production deployments with SLAs — use provisioned throughput or dedicated inference for latency-sensitive applications requiring guaranteed performance
• Model fine-tuning pipelines — adapt Llama, Qwen, DeepSeek, Gemma, or other supported models to specialized domains using Together AI's fine-tuning capabilities
• Custom container deployments — dedicated container inference supports teams with bespoke model serving requirements
• GPU cluster workloads — access H100, H200, B200, GB200, and GB300 clusters for large-scale training or research
• Inference optimization research — leverage FlashAttention, ATLAS, and ThunderKittens-backed infrastructure for cutting-edge performance