Build an AI Gateway for LLM Routing

By Khimananda Oli | Last reviewed: August 2026

Directly integrating application code with multiple Large Language Model (LLM) APIs creates fragile dependencies, unpredictable billing, and significant security blind spots. When you build an AI gateway for LLM routing, you insert a dedicated control plane that abstracts provider differences, enforces governance policies, and manages traffic flow before requests ever reach external models. This architectural layer is no longer optional for production systems; it is the standard interface for reliable, observable, and cost-effective generative AI infrastructure.

Why should you build an AI gateway for LLM routing instead of direct integration?

In my experience auditing SOC 2 compliance for AI startups, direct API integration is consistently the primary source of security findings and operational incidents. Without a gateway, every microservice holds raw provider keys, making rotation impossible without downtime and audit trails fragmented across dozens of log streams. A centralized gateway solves this by acting as the single point of egress for all model traffic.

Beyond security, the economic argument is compelling. Provider pricing changes frequently, and model performance varies by task. A routing layer allows you to shift traffic from expensive frontier models to cheaper open-weights alternatives for simple tasks, or failover to a backup provider during outages, all without redeploying application code. For teams in Nepal or regions with specific data residency requirements, the gateway also serves as the enforcement point for geo-fencing policies, ensuring sensitive PII never leaves approved jurisdictions before reaching a model endpoint.

If you are already managing complex infrastructure, think of the AI gateway as analogous to an Ingress Controller in Kubernetes. Just as you wouldn't expose backend services directly to the internet without proper ingress management, you shouldn't expose your AI budget and data to unmanaged API calls. The gateway provides the same level of abstraction, policy enforcement, and traffic shaping for machine learning workloads that traditional gateways provide for HTTP microservices.

How do you configure intelligent routing and fallback strategies?

Routing is the core value proposition of any AI gateway. In practice, static routing (always sending request X to model Y) is insufficient for production. You need dynamic routing that responds to real-time conditions. Most modern gateways support configuration-as-code, typically via YAML or environment variables, which aligns well with GitOps workflows discussed in our ArgoCD GitOps guide.

Defining Fallback Chains

A robust fallback strategy prevents user-facing errors when a primary provider experiences degradation. Configure your gateway to attempt providers in priority order, with automatic retry logic for transient failures (429, 500, 503). Here is a representative configuration pattern using LiteLLM syntax:

model_list:
  - model_name: "smart-chat"
    litellm_params:
      model: "openai/gpt-4o"
      api_key: os.environ/OPENAI_KEY
      timeout: 30
      num_retries: 2
  - model_name: "smart-chat"
    litellm_params:
      model: "anthropic/claude-sonnet-4-20250514"
      api_key: os.environ/ANTHROPIC_KEY
      timeout: 30
  - model_name: "smart-chat"
    litellm_params:
      model: "azure/gpt-4o-mini"
      api_key: os.environ/AZURE_KEY
      deployment_id: "gpt4o-mini-eastus"

router_settings:
  routing_strategy: "latency-based-routing"
  set_verbose: true
  enable_pre_call_checks: true
  cooldown_time: 60

This configuration defines a virtual model alias smart-chat. The router attempts OpenAI first. If it fails twice or exceeds the timeout, it automatically falls back to Anthropic, then Azure. Crucially, the cooldown_time parameter prevents the gateway from hammering a failing provider for 60 seconds after an error, allowing recovery time. This mirrors circuit breaker patterns used in resilient microservice architectures.

Cost-Aware and Latency-Based Routing

For high-volume workloads, routing purely by availability leaves money on the table. Configure cost-aware routing to prefer cheaper models when latency budgets allow. Many gateways now track real-time latency metrics per deployment. By setting a threshold (e.g., "use the fastest provider under $2/M tokens"), the gateway dynamically balances performance and spend. This is particularly effective for RAG pipelines where embedding generation can be routed to low-cost local models while synthesis uses frontier APIs.

What observability and security controls are essential for AI gateways?

Operating an AI gateway without observability is flying blind. Unlike traditional APIs where response codes tell the story, LLM failures are often semantic: the model returns a 200 OK but hallucinates, refuses a valid prompt, or leaks PII. Your gateway must capture the full request/response lifecycle for debugging and compliance.

Integrate your gateway with dedicated LLM observability platforms like Langfuse, Helicone, or Arize Phoenix. These tools understand token-level metrics and trace hierarchies that generic APM tools miss. As covered in OpenTelemetry standards, structured tracing is non-negotiable for debugging multi-step agent workflows. Ensure your gateway exports traces in OpenTelemetry format to maintain compatibility with your existing monitoring stack.

On the security front, implement input/output filtering at the gateway level. This includes PII detection (using regex or lightweight NER models), prompt injection scanning, and output validation against expected schemas. For teams handling sensitive data, this is where you enforce redaction policies before data ever touches a third-party API. Treat these filters as WAF rules for AI: they should be configurable per tenant and auditable.

Which open-source AI gateway framework fits your infrastructure?

The ecosystem has matured significantly by 2026. Choosing the right tool depends on whether you prioritize configuration flexibility, enterprise features, or language-native integration. Below is a comparison of the three most battle-tested options I've deployed in production environments.

LiteLLM remains the default choice for most engineering teams building custom platforms. Its Python-native design makes it easy to extend with custom middleware, and its unified interface covers virtually every provider including local Ollama instances. Portkey excels when product teams need sophisticated guardrails and caching without managing infrastructure. Kong AI Gateway is the right pick if you already run Kong for API management and want to consolidate AI traffic into your existing ingress layer, though it lacks some of the nuanced semantic features of specialized tools.

How do you deploy and scale an AI gateway in Kubernetes?

For production workloads, deploy your AI gateway as a stateless service in Kubernetes. This allows horizontal scaling independent of your application pods. Since LLM requests can be long-running (especially for streaming responses), tune your resource limits and timeouts carefully. Standard HTTP defaults of 30s will kill legitimate inference requests.

1. Resource Allocation: AI gateways are CPU-bound during token counting and encryption/decryption. Allocate at least 1 vCPU and 1Gi RAM per replica. Use VPA recommendations to right-size after initial load testing.
2. Connection Pooling: Enable HTTP/2 keep-alive connections to upstream providers. Cold TLS handshakes add 100-300ms latency per request. Configure connection pools to match your concurrency limits.
3. Secrets Management: Never store API keys in ConfigMaps. Use External Secrets Operator or Sealed Secrets to inject provider credentials at runtime. Rotate keys automatically via your secrets manager.
4. Health Checks: Implement a dedicated /health endpoint that verifies connectivity to at least one upstream provider. Avoid checking all providers on every probe to prevent rate-limit exhaustion.

When scaling, remember that semantic caching requires shared state. Deploy Redis or Valkey as a separate stateful set or use a managed service. The gateway pods themselves should remain stateless to enable rapid autoscaling during traffic spikes. Monitor token throughput rather than just request count for HPA metrics, as a single large context request consumes far more resources than a simple completion.

Next Steps for Production AI Infrastructure

Building an AI gateway for LLM routing transforms generative AI from a fragile experiment into a manageable engineering discipline. Start with a minimal viable gateway: unified API, basic fallbacks, and token-level logging. Iterate toward advanced routing, semantic caching, and automated guardrails as your usage grows. The investment pays compounding dividends in reliability, cost control, and audit readiness.

If your team needs help designing a compliant, scalable AI gateway architecture tailored to your specific workload and compliance requirements, reach out to discuss your infrastructure needs. Whether you're operating globally or serving Nepal-specific markets with data residency constraints, getting the routing layer right from day one prevents costly rearchitecture later.