LLM Gateway simplifies enterprise AI operations by putting one intelligent gateway in front of every LLM provider. With centralized routing, scoped access control, cost tracking, caching, and audit visibility, teams can scale GenAI faster without losing security, reliability, or control.
Unified inference endpoints for chat completions and embeddings
Multi-provider backend support with configurable routing
Stable route slugs that decouple application code from provider model names
Route-level controls for system prompts, temperature, token limits, and failover providers
JWT and scoped API key authentication models
Provider and model allowlists for least-privilege access
In-memory rate limiting and request-size protection
Retry and circuit-aware failover behavior for upstream provider resilience
Redis-backed caching for repeated non-stream completions
Per-provider and per-model cost rule configuration
Usage analytics, request logs, API key analytics, and spend-group breakdowns
End-to-end audit logging for gateway-to-provider and provider-to-gateway traffic
For architects, the main value is separation of concerns. Product teams focus on application behavior, while platform teams retain centralized control over reliability, governance, security, and cost management. This makes llm_gateway especially useful in environments where multiple teams, providers, and deployment standards need to coexist without creating fragmented AI integration patterns.
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