Together AI is the cloud inference platform serving 100+ open-weight language models via an OpenAI-compatible API at 3-10x lower cost than proprietary models — enabling developers to switch from GPT-4 to Llama-3-70B or DeepSeek-V3 with a single line of code, while Together AI handles the GPU infrastructure, inference optimization, and model hosting.

What Is Together AI?

• Definition: A cloud inference platform founded in 2022 that specializes in hosting and serving open-weight language models (Llama, Mistral, Mixtral, Qwen, DeepSeek) via a REST API compatible with OpenAI's SDK — so existing OpenAI integrations work with different model weights instantly.
• Mission: Democratize access to open-source AI by providing the infrastructure to run large open-weight models affordably — without requiring teams to manage GPU infrastructure, CUDA drivers, or serving frameworks.
• OpenAI-Compatible API: Together AI's inference API mirrors OpenAI's chat completions endpoint — change base_url to api.together.xyz and swap the model name to use Llama or Mixtral instead of GPT-4.
• Custom Inference Stack: Together AI builds optimized inference kernels for throughput and latency — delivering faster time-to-first-token and higher tokens/second than standard self-hosted vLLM on equivalent hardware.
• Founded: 2022, backed by NVIDIA, Salesforce Ventures, and Andreessen Horowitz — with a mission to build the decentralized cloud for AI.

Why Together AI Matters for AI Engineers

• Cost Reduction vs OpenAI: Llama-3.1-70B at ~$0.88/million tokens vs GPT-4o at $5/million input tokens — 5x+ cost reduction for comparable capability on many tasks.
• Open-Weight Access: 100+ open-weight models available via simple API — no hosting infrastructure needed to use Llama, Mistral, DBRX, Qwen, DeepSeek, or Code Llama.
• Zero-Migration API: Build on OpenAI SDK, switch to Together AI with two config lines — no refactoring of prompts, parsers, or application logic.
• Fine-Tuning Service: Upload LoRA fine-tuned adapters or train custom models on Together AI infrastructure — serve custom models via the same inference API.
• No Vendor Lock-in: Build on open-weight models — if Together AI changes pricing, migrate to self-hosted vLLM or alternative provider with same model weights and prompts.

Together AI Services

Inference API (Chat Completions): from together import Together client = Together(api_key="your-key") response = client.chat.completions.create( model="meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo", messages=[{"role": "user", "content": "Explain RLHF in AI training"}], max_tokens=1024 ) print(response.choices[0].message.content)

Fine-Tuning:

• Upload training data in JSONL format (instruction/response pairs)
• Fine-tune base models (Llama, Mistral) on custom domain data
• Serve fine-tuned models via same API with your custom model ID
• Pricing: per training token + per inference token

Embeddings:

• Embed documents with BAAI/bge-large, M2-Bert, and other embedding models
• Returns vectors for RAG pipelines at competitive pricing
• Compatible with LangChain and LlamaIndex embedding integrations

Key Models Available:

• Meta Llama 3.1 405B / 70B / 8B Instruct Turbo
• Mixtral 8x7B / 8x22B Instruct
• DeepSeek-V3, DeepSeek-R1 (reasoning)
• Qwen 2.5 72B / 110B
• DeepSeek Coder, Code Llama (code generation)
• FLUX.1 (image generation)

Pricing Model:

• Pay per million tokens (input + output separately priced)
• No subscription, no minimum spend
• Larger models cost more per token; smaller/quantized models cost less
• Fine-tuning priced per training token

Together AI vs Alternatives

Together AI is the inference cloud that makes open-weight models as accessible as OpenAI's API at a fraction of the cost — by providing a production-grade, OpenAI-compatible inference layer over the best open-source models, Together AI enables teams to build cost-effective AI applications without managing GPU infrastructure or serving frameworks.