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LLMFly AI

An OpenAI-compatible API gateway for calling GPT, Claude, Gemini, Grok, and other catalog models through one endpoint and billing workflow.

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Introduction

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Information

  • Publisher
    pushtimepushtime
  • Websitellmfly.ai
  • Published date2026/09/13

Categories

  • Developer Tools
  • AI & Automation

Tags

  • AI Powered
  • API
  • Multi-Model
  • Developer Tools
  • OpenAI Compatible
  • Integrations
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How to Use LLMFly AI

LLMFly AI's Core Features

Unified model API

Access models from multiple providers through one catalog, endpoint, and account.

OpenAI-compatible client

Keep a familiar SDK pattern for compatible routes by changing the base URL, key, and model ID.

Environment-specific keys

Issue separate keys per app and environment so one credential can be revoked without taking everything offline.

Model pricing catalog

Compare listed input, output, and cache rates before selecting a model.

Usage visibility

Inspect request consumption and charges through account usage history.

LLMFly AI's Use Cases

1

Multi-model applications

Switch among candidate models without maintaining a separate provider integration for every experiment.

2

AI agents and coding tools

Connect compatible agents, editors, scripts, and development assistants to one model catalog.

3

Model evaluation

Compare cost and behavior across models using a consistent application interface.

4

Backend automation

Run server-side summarization, extraction, generation, and routing workflows with per-environment keys.

FAQ from LLMFly AI

LLMFly AI Support & Company Information

Company
FLAMEWRIGHT TECHNOLOGIES LTD.
Contact
Open Contact

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LLMFly AI

LLMFly AI provides one API endpoint for models from providers such as OpenAI, Anthropic, Google, and xAI. Developers can keep a familiar OpenAI-compatible client, switch the base URL and model ID, and compare model rates in a shared catalog.

Core capabilities
  • Call GPT, Claude, Gemini, Grok, and other listed models through one gateway
  • Use an OpenAI-compatible API for supported routes
  • Create separate API keys for applications and environments
  • Compare input, output, and cache rates before sending production traffic
  • Review usage history and model-specific charges
  • Connect backend applications, scripts, coding tools, agents, and automation workflows

An API gateway becomes part of the security and privacy boundary for every prompt it processes. Test provider-specific features, protect keys, review retention policies, and validate pricing before routing production data.

  1. Create an account and a separate API key for the application or environment.
  2. Choose a model ID from the current catalog and review its rates.
  3. Point a server-side OpenAI-compatible client at the documented LLMFly base URL.
  4. Start with a short non-streaming request, then test tools, streaming, and structured output if needed.
  5. Monitor usage history, revoke leaked keys, and keep production credentials out of client-side code.
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