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Avian API
Fastest, production grade API for Open Source LLMs

What is Avian API?

Avian API is a cutting-edge language model inference platform that provides enterprise-grade performance through state-of-the-art open source language models. Powered by Meta's Llama 3.1 405B model and the latest Nvidia H200 SXM technology, it delivers unmatched performance and reliability at half the price of OpenAI.

The platform features OpenAI-compatible integration, native tool calling capabilities, and efficient streaming API for real-time responses. With a strong focus on privacy and security, Avian API operates on SOC/2 approved infrastructure through Microsoft Azure, ensuring GDPR and CCPA compliance while processing queries in real-time without data storage.

Features

  • Processing Speed: 142 tokens per second with Meta Llama 3.1 405B
  • Context Length: Full 131k context window support
  • Native Tool Calling: Seamless integration with external tools and APIs
  • Streaming API: Real-time response capabilities
  • OpenAI Compatibility: Drop-in replacement for OpenAI API
  • Privacy Protection: SOC/2, GDPR, and CCPA compliant
  • Infrastructure: Powered by Nvidia H200 SXM technology
  • Model Performance: Superior natural language understanding and reasoning

Use Cases

  • Enterprise AI Integration
  • Real-time Text Generation
  • Interactive AI Applications
  • Natural Language Processing
  • Complex Reasoning Tasks
  • Knowledge-based Query Processing
  • Secure Data Processing
  • API Integration Projects

FAQs

  • What is the pricing structure for Avian API?
    Pricing starts at $3 per million tokens for the Meta Llama 3.1 405B Instruct model, with lower-priced options available for smaller models.
  • How does Avian API ensure data privacy?
    Avian API processes queries in real-time without storing data, operates on SOC/2 approved infrastructure, and is GDPR and CCPA compliant.
  • What is the context length supported by Avian API?
    Avian API supports a full 131,072 token context length across all its models.

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