Together AI: what it is, who it is for and alternatives
Category: Model APIs and platforms
Together AI is a cloud platform for running, fine-tuning, and training open-source models. It offers serverless inference APIs, dedicated endpoints, and GPU clusters.
Together AI is a cloud platform for running, fine-tuning, and training open models. Its serverless API offers many open-weight models, including Llama, DeepSeek, Qwen, Mistral, gpt-oss, and image models such as FLUX, through OpenAI-compatible endpoints. Beyond inference, it provides dedicated endpoints, fine-tuning for custom models, batch inference, code sandboxes, and GPU clusters for training. Its research team has contributed widely used work such as FlashAttention.
It fits AI startups and engineering teams building on open models who want performance without operating GPUs themselves, and those who want to fine-tune and serve custom variants. Teams that depend on proprietary models from OpenAI or Anthropic will need those providers directly or through a cloud marketplace.
Compared with Groq, Together offers a broader catalogue and training services, while Groq emphasizes speed on its own chips. Compared with Hugging Face, Together is primarily a compute provider, not a model-sharing hub. Together bills serverless inference per token by model, dedicated endpoints and GPU clusters by time, and fine-tuning by tokens processed.
Before building, compare serverless versus dedicated endpoints for your traffic pattern, check which models support fine-tuning, and review data retention settings. Teams training models should plan GPU cluster reservations and monitoring, and keep evaluation sets to compare open models against proprietary alternatives. For teams comparing open models, a single account covering many model families makes it easy to benchmark candidates on the same prompts, then move the winner to a dedicated endpoint or fine-tuned variant as traffic grows.
Current plan prices, and any gaps we could not verify, are on our Together AI pricing page. Nothing on this page reflects personal hands-on testing; it is desk research from the vendor’s official pages.
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