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GoModelAI gateway written in Go. Lightweight unified OpenAI-compatible API for OpenAI, Anthropic, Gemini, Groq, xAI & Ollama. LiteLLM alternative with observability, guardrails, streaming, costs and usage tracking.

AI gateway written in Go. Lightweight unified OpenAI-compatible API for OpenAI, Anthropic, Gemini, Groq, xAI & Ollama. LiteLLM alternative with observability, guardrails, streaming, costs and usage tracking.

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MIT
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GoModel

GoModel is a lightweight, high-performance AI gateway written in Go that provides a unified OpenAI-compatible API across multiple LLM providers including OpenAI, Anthropic, Gemini, Groq, xAI, and Ollama. It serves as a resource-efficient alternative to LiteLLM, offering built-in observability, cost tracking, guardrails, and streaming support to help teams manage their AI infrastructure and spending.

Key Features

  • Multi-provider support: Unified API for 25+ LLM providers with OpenAI and Anthropic SDK compatibility
  • Cost and usage tracking: Real-time spending analytics, per-request cost estimates, and detailed usage breakdowns
  • Intelligent caching: Exact and semantic response caching to reduce API costs for repeated queries
  • Rate limiting and budgets: Enforce hard spend limits and request caps per user, team, or API key
  • Virtual models: Create model aliases and load-balance traffic across providers using round-robin or cost-based routing
  • Observability dashboard: Monitor performance metrics, tokens, costs, and AI usage patterns in real-time

Use Cases

  • Cost optimization: Cache responses and implement intelligent routing to reduce LLM API expenses
  • Multi-provider management: Consolidate access to multiple LLM providers behind a single, standardized API
  • Team governance: Track spending per user or team, set budgets, and enforce rate limits across your organization
  • Production reliability: Implement automatic failover and retries across backup providers to ensure service continuity

Who Is It For

GoModel is designed for engineering teams and organizations deploying AI applications at scale who need centralized control over LLM access, cost visibility, and provider flexibility without the overhead of complex infrastructure or the security concerns of unmaintained alternatives.

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