patterngrowingmedium complexity

Router / Gateway Pattern

Router-Gateway is an AI architecture pattern where a single entrypoint (gateway) receives all requests and a router decides which downstream model, tool, or service should handle each one. The router can use rules, heuristics, or another model to classify intent, risk, latency/cost needs, and required capabilities. This enables multi-model orchestration, cost optimization, and safer handling of diverse workloads behind a unified API. It is especially useful when different tasks require different models, modalities, or infrastructure tiers.

5implementations

2industries

Parent CategoryRetrieval Systems

When to Use

You need a single unified API for multiple models, tools, or modalities and want to hide this complexity from client applications.
Different tasks, tenants, or regions require different models (e.g., code vs. chat vs. vision; EU vs. US data residency).
You want to optimize for a mix of cost, latency, and quality by routing to different models based on request characteristics.
You plan to frequently experiment with or swap out models and providers without changing client code.
You must enforce safety, compliance, or policy-based routing (e.g., content categories, PII handling, jurisdictional rules).

When NOT to Use

You have a single primary model and a narrow, well-defined use case where routing adds unnecessary complexity and latency.
Your traffic volume is low and you do not expect to change models or providers frequently; a direct integration is simpler and sufficient.
You lack the observability, metrics, or data needed to evaluate routing decisions; you would be guessing rather than optimizing.
Strict regulatory or security constraints require fully isolated, simple pipelines where additional routing logic increases audit complexity.
Your team does not have the operational maturity to manage a central gateway (SLAs, incident response, configuration management).

Key Components

API Gateway / Entrypoint Service
Request Normalizer (schema validation, auth, rate limiting)
Router Engine (rules-based, ML-based, or hybrid)
Policy & Safety Layer (guardrails, PII detection, compliance rules)
Model Registry / Capability Catalog
Routing Strategies (cost, latency, quality, risk, specialization)
Downstream Model Connectors (LLMs, embeddings, vision, speech, etc.)
Tool / Service Connectors (search, RAG, transactional APIs, databases)
Observability & Telemetry (logging, tracing, metrics, A/B routing)
Feedback & Learning Loop (human feedback, auto-labeling, retraining)

Common Tools

NVIDIA LLM Router (NVIDIA-AI-Blueprints/llm-router)LangDB AI Gateway (langdb/ai-gateway)LangChain (LCEL, routers, multi-model chains)LlamaIndex (router query engines, composable graphs)Semantic Router (for intent-based routing in RAG/chatbots)Kong / NGINX / Envoy / Istio (as HTTP/API gateways and service mesh)FastAPI / Express.js / Spring Boot (custom gateway implementations)OpenAI API (multiple models, function calling, moderation)Anthropic, Google Gemini, Azure OpenAI (multi-model backends)Ray Serve / KServe / Sagemaker Endpoints (model serving & routing)Prometheus / Grafana / OpenTelemetry (metrics and tracing)Feature flag systems (LaunchDarkly, ConfigCat, custom config services)

Top Industries

mining3 transportation2

Best Practices

Start with a simple rules-based router (by endpoint, task type, or tenant) before introducing ML-based routing; only add complexity when you have clear metrics and data.
Define a clear capability catalog for each model or tool (e.g., languages, max context, modalities, safety level, latency, cost) and base routing decisions on this metadata.
Separate gateway concerns (auth, rate limiting, request validation) from routing logic so you can evolve routing without breaking client integrations.
Implement explicit safety and compliance checks in the router path (e.g., PII detection, content filters, jurisdiction-based routing) before calling powerful models.
Always define fallback strategies: backup models, reduced-capability flows, or safe error messages when the preferred route fails or is overloaded.

Common Pitfalls

Overcomplicating routing logic too early (e.g., training a router model without enough data) leading to brittle behavior and hard-to-debug failures.
Hard-coding model-specific assumptions into client applications instead of keeping them inside the router-gateway, making migrations and experiments painful.
Ignoring safety and compliance in routing decisions (e.g., sending high-risk content to models without proper guardrails or regional restrictions).
Failing to implement robust fallbacks, causing user-visible outages when a single model or provider has issues.
Lack of observability into routing decisions, making it impossible to understand why a request was sent to a particular model or why performance regressed.

Learning Resources

tutorialai-gateway Routing Concepts (langdb/ai-gateway)tutorialNVIDIA LLM Router – Route LLM requests to the best model tutorialLLM Traffic Control: Gateway or Router or Proxy tutorialMastering RAG Chatbots: Semantic Router — RAG gateway tutorialThe Inference Router: A Critical Component in the LLM Ecosystem

Example Use Cases

01Customer support platform where the gateway routes simple FAQs to a cheap small model, complex multi-step issues to a larger reasoning model, and account-specific questions to a RAG tool connected to internal systems.

02Enterprise AI assistant that routes legal queries to a high-safety, jurisdiction-specific model, engineering questions to a code-specialized model, and general chit-chat to a low-cost conversational model.

03Multilingual chatbot that routes by detected language and region to models hosted in compliant regions (e.g., EU-only models for EU users) while enforcing data residency policies.

04Content moderation pipeline where the router sends low-risk content to a fast heuristic filter and escalates borderline or high-risk content to a more accurate but slower LLM-based classifier.

05AI writing assistant that routes summarization tasks to a summarization-optimized model, code generation to a code LLM, and image requests to a text-to-image model, all behind a single /generate endpoint.

Solutions Using Router / Gateway Pattern

2 FOUND

mining7 use cases

Mining AI Safety Governance

Mining AI Safety Governance is a suite of tools that designs, monitors, and enforces safety protocols for AI and autonomous systems in mining operations. It unifies risk scanning, guardrails for LLMs, and log-based risk inference to detect unsafe behaviors early and standardize safe responses. This reduces the likelihood of accidents, compliance breaches, and downtime as AI use expands across mines.

mining14 use cases

AI Mining Hazard Intelligence

AI Mining Hazard Intelligence continuously analyzes sensor feeds, video, control system logs, and worker wearables to detect hazards, predict incidents, and flag unsafe conditions across mining operations. It unifies risk monitoring from pit to plant, supporting real-time alerts, safer work practices, and proactive policy decisions. This reduces accidents and downtime while improving regulatory compliance and productivity in high-risk mining environments.