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Doubleword Control Layer

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The world’s fastest AI model gateway (450x less overhead than LiteLLM). Unified access to LLMs across endpoints (openAI, self-hosted, etc.) behind a single authentication layer - w

OSS

Doubleword Control Layer

Added 1 Oct 2026

#gateway #llm #openai

Overview

Doubleword Control Layer is a Rust-based AI model gateway that provides unified access to LLMs across endpoints such as OpenAI and self-hosted models behind a single authentication layer. It includes API key generation, user management, and request logging. The project claims 450x less overhead than LiteLLM.

Best for

Best for
Teams needing a lightweight, self-hosted LLM gateway with centralized access control.

Use cases

  • Route requests to multiple LLM endpoints through one API key
  • Manage user access and API keys for internal AI tools
  • Log and monitor LLM requests for observability

Notes

Doubleword Control Layer is a Rust-based AI model gateway that provides unified access to LLMs across endpoints such as OpenAI and self-hosted models behind a single authentication layer. It includes API key generation, user management, and request logging. The project claims 450x less overhead than LiteLLM.

95 stars on GitHub. Last updated 2026-10-01. Licensed Apache-2.0.

Use cases

  • Route requests to multiple LLM endpoints through one API key
  • Manage user access and API keys for internal AI tools
  • Log and monitor LLM requests for observability

Pros

  • Very low overhead compared to LiteLLM, with a claimed 450x reduction
  • Unified authentication and user management in one layer
  • Written in Rust for performance

Cons

  • Early-stage project with only 95 GitHub stars
  • Community-maintained, so no commercial support
  • Limited ecosystem and integrations compared to established gateways

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Very low overhead compared to LiteLLM, with a claimed 450x reduction
  • Unified authentication and user management in one layer
  • Written in Rust for performance

Cons

  • Early-stage project with only 95 GitHub stars
  • Community-maintained, so no commercial support
  • Limited ecosystem and integrations compared to established gateways
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