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Together AI

Fast, low-cost inference and fine-tuning API for open-source models — Llama, Mixtral, Qwen, FLUX, and more.

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Together AI is a cloud platform for running open-source and open-weight AI models without owning GPUs. Through a single API it serves 100+ models — Meta’s Llama family, Mixtral, Qwen, DeepSeek, and image models like FLUX — with an OpenAI-compatible interface, so teams can point existing code at Together by changing little more than the base URL and key. The draw is cost and control: per-token prices sit well under the big proprietary APIs, and you pick exactly which open model runs.

The company was founded in 2022 by Vipul Ved Prakash and others, and competes with inference specialists like Fireworks, Replicate, Anyscale, and Groq. Beyond raw inference it offers fine-tuning on your own data, dedicated endpoints for predictable throughput, and research contributions to fast-inference techniques — positioning it as infrastructure for products built on open models rather than a consumer chatbot.

Key Features

  • Unified API access to 100+ open-source and open-weight models
  • OpenAI-compatible endpoints for drop-in migration
  • Optimized inference for high throughput and low latency
  • Fine-tuning and LoRA training on custom datasets
  • Serverless (pay-per-token) and dedicated GPU endpoints
  • Image, embedding, and code models alongside chat LLMs

Pricing

  • Free: sign-up credit to test the platform
  • Pay-as-you-go: per-token pricing that varies by model, from fractions of a cent per 1K tokens
  • Dedicated / Enterprise: reserved GPU capacity, volume discounts, and private deployments

Best For

Developers, AI startups, and teams building products on open models who want fast, cheap inference and the option to fine-tune — without provisioning and babysitting their own GPU clusters.

Limitations

Going open-source means you carry responsibility for choosing and evaluating models; there is no single managed “best” answer the way a flagship proprietary API gives you. Top open models are strong but can still trail the very best closed models on the hardest reasoning tasks, and serverless latency can fluctuate with demand, so latency-critical workloads may need dedicated endpoints.

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