How to design an enterprise-grade LLM Gateway with dynamic fallback, tenant rate limiting, and cost allocation?

Short answer: An enterprise-grade LLM Gateway acts as a central proxy architecture providing tenant authentication, dual-metric rate limiting (TPM/RPM via Redis), dynamic provider fallback routing, semantic caching, unified telemetry tracing, and automated billing cost allocation across heterogeneous downstream LLM endpoints.

Detail

Direct API consumption by internal microservices creates security risks, uncoordinated vendor spend, and rate-limit fragility.

[Internal Microservice A] ──┐
[Internal Microservice B] ──┼──► [Enterprise LLM Gateway Proxy]
[Internal Microservice C] ──┘                 │
                                              ├── [Redis Rate Limiter & Semantic Cache]
                                              ├── [OpenTelemetry Spans + Cost Allocator]
                                              └── [Provider Fallback Engine]
                                                        │
                                     ┌──────────────────┼──────────────────┐
                                     ▼                  ▼                  ▼
                              [OpenAI API]      [Anthropic API]      [vLLM Cluster]

Architectural Sub-Systems

1. Authentication & Budget Enforcement: Maps API keys to tenant IDs (org_sales) with monthly USD hard caps.
2. Dual Metric Rate Limiter: Enforces Requests-Per-Minute (RPM) and Tokens-Per-Minute (TPM) sliding window caps in Redis.
3. Resilient Provider Router: Automatically attempts primary model, falling back to secondary providers on HTTP 5xx or rate limits.
4. Unified Telemetry Logger: Emits OpenTelemetry spans capturing prompt tokens, completion tokens, latency, and cost.

Example

Python high-level LLM Gateway proxy middleware:

async def handle_gateway_request(tenant_id: str, request_payload: dict, redis_client, provider_client):
    # 1. Tenant Budget & Rate Limit Check
    if not await check_rate_limit(redis_client, tenant_id, request_payload["estimated_tokens"]):
        return {"error": "HTTP 429: Rate Limit Exceeded"}, 429

    # 2. Semantic Cache Lookup
    cached_resp = await get_semantic_cache(request_payload["prompt"])
    if cached_resp:
        return cached_resp, 200

    # 3. Provider Invocation with Fallback
    response, cost = await provider_client.execute_with_fallback(request_payload)

    # 4. Asynchronous Cost & Metric Logging
    await log_cost_attribution(tenant_id, cost)
    return response, 200

Interview tips

• Discuss OpenTelemetry semantic conventions for AI applications.
• Explain trade-offs between self-building a gateway vs deploying open-source gateways like LiteLLM Proxy.

Related Concepts

• [[What is document metadata filtering in vector databases?]] (#123): What is document metadata filtering in vector databases?
• [[What is load balancing for LLM inference clusters across multi-region GPU pools?]] (#151): What is load balancing for LLM inference clusters across multi-region GPU pools?
• [[What is cost attribution modeling per user, team, and organization in LLM applications?]] (#162): What is cost attribution modeling per user, team, and organization in LLM applications?

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