Together AI

Fast, affordable inference for open-source AI models

paidproductionopen-sourceinferenceapihostingaffordable

Memory Types

Integrations

api, langchain, llamaindex


Overview


Together AI provides fast, affordable inference for open-source foundation models. The platform hosts hundreds of open models from the community, offering API access at prices significantly lower than proprietary alternatives. Together emphasizes performance optimization, making open models run faster and cheaper while maintaining quality.


Founded by researchers from institutions like Stanford and Meta, Together has optimized the entire inference stack for open models. They provide both public API access and private deployments, making it easy to run models like Llama, Mixtral, and Qwen at scale without managing infrastructure.


Key Features


  • **200+ Models**: Comprehensive open model library
  • **Fast Inference**: Optimized for performance
  • **Low Prices**: 5-10x cheaper than proprietary models
  • **Custom Deployments**: Private model hosting
  • **Fine-Tuning**: Train on your data
  • **Latest Models**: Quick access to new releases
  • **Simple API**: OpenAI-compatible endpoints
  • **No Vendor Lock-In**: Use open models freely

  • When to Use Together AI


    Together AI is ideal for:

  • Cost-conscious applications at scale
  • Teams wanting to use open-source models
  • Applications where good-enough quality suffices
  • Experimentation with multiple models
  • Organizations avoiding proprietary model lock-in
  • Self-hosting alternative without infrastructure management

  • Pros


  • Significantly cheaper than proprietary models
  • Access to latest open models quickly
  • Fast inference performance
  • No vendor lock-in with open models
  • Simple OpenAI-compatible API
  • Can fine-tune models
  • Private deployment options
  • Good for experimentation

  • Cons


  • Open models less capable than GPT-4/Claude
  • Quality varies across models
  • Less support than major providers
  • Newer platform with less track record
  • Documentation can be limited
  • Model selection can be overwhelming
  • Some models may be deprecated quickly
  • Community support varies

  • Pricing


  • **Llama 3.1 70B**: $0.88 per 1M tokens
  • **Mixtral 8x22B**: $1.20 per 1M tokens
  • **Qwen 2.5 72B**: $1.20 per 1M tokens
  • **Fine-Tuning**: Starting at $3/hour