Open-model inference without the GPU bill. Together AI runs Llama, Mistral, and custom fine-tuned models via API—no infrastructure to manage.
Together AI is an inference platform that lets you call open-source language models and fine-tuned variants through a REST API. No GPU provisioning. No Kubernetes clusters. No capacity planning. The core value: you pick a model (Llama 2, Mistral, Qwen, Code Llama, or your own fine-tuned version), send a prompt, get tokens back. The platform handles batching, scaling, and hardware allocation behind the scenes. For operators shipping AI features, this removes the infrastructure tax that usually comes with self-hosted inference. Who uses it: founders building chat interfaces, RAG systems, or content generation features who want model flexibility without vendor lock-in. Teams in regulated domains (legal, medical, financial) that need to run models on their own data without sending it to OpenAI or Claude. SaaS operators who want to offer model selection to end users—let customers pick between Llama and Mistral instead of forcing one. Common workflows: A support team uses Together to power a chatbot that summarizes tickets. A legal tech startup fine-tunes Llama on contract language and serves it through Together's API. A content platform batches document summarization jobs overnight, paying only for compute time. A multi-tenant SaaS lets customers toggle between open models based on cost vs. quality tradeoffs. Pricing model: Together charges per token (input and output rates vary by model). Open models like Llama are cheaper than proprietary options. Fine-tuning incurs separate costs. Batch inference typically costs less per token than real-time. Exact rates change—check the pricing page for current numbers. Where it fits: Together sits between self-hosted inference (you manage everything) and fully managed services like OpenAI (you lose model choice). It's cheaper than OpenAI for high-volume workloads if you're willing to use open models. It's easier than running vLLM on your own GPU cluster if you don't want to hire an ML ops person. Honest limits: Open models still underperform GPT-4 on reasoning and instruction-following. Latency varies—real-time chat needs careful model selection and prompt engineering. Fine-tuning requires data prep and iteration; Together provides the infrastructure, not the expertise. If you need sub-100ms responses at massive scale, you may still need local inference. If you need Claude or GPT-4, Together doesn't help. Alternatives: Replicate (simpler UI, broader model catalog, higher per-token cost). Hugging Face Inference API (free tier, limited scale). Modal (more flexible, steeper learning curve). OpenAI API (closed models, highest cost, best quality). Self-hosted vLLM or TGI (lowest cost, highest ops burden).
Open-model inference without the GPU bill. Together AI runs Llama, Mistral, and custom fine-tuned models via API—no infrastructure to manage.
Ship chat interfaces and Q&A systems powered by open-source models without provisioning GPUs; Fine-tune Llama or Mistral on proprietary data (contracts, medical records, code) and serve via API; Build cost-optimized inference pipelines for high-volume summarization, classification, and content generation; Experiment across multiple models (Llama, Mistral, Qwen) to find the right quality-latency-cost balance.
Together AI uses a usage based pricing model, starting around See vendor site, with a free plan available. Pricing changes often — confirm current tiers on the vendor site.
Yes, Together AI lists an API, so you can integrate it into custom workflows.
Open models lag behind GPT-4 and Claude on complex reasoning and multi-step instruction-following; Latency varies by model and load; real-time chat requires careful model selection and prompt engineering; Fine-tuning requires clean, labeled data; Together provides infrastructure, not data science guidance; Batch inference introduces delay—not suitable for synchronous, low-latency use cases.
Popular Together AI alternatives include vllm, modal, replicate, anyscale. See the full alternatives page for side-by-side comparisons.