AI Gateway: API Routing, Rate Limiting, Fallback Models, Cost Management, and Logging

As your AI application grows, you will use multiple models from multiple providers. Managing API keys, rate limits, costs, and failover across providers becomes a nightmare without a gateway. Here is how to build an AI gateway that centralizes LLM API management.

Why You Need an AI Gateway

Direct LLM API integration works for prototypes but fails in production. Each provider has different authentication, rate limits, error formats, and pricing. Your code becomes a mess of conditional logic for handling different providers.

An AI gateway sits between your application and LLM providers. It routes requests to the right model, handles authentication, enforces rate limits, and provides consistent error handling. Your application code calls the gateway with a simple API and never needs to know provider details.

The gateway also provides centralized visibility. Every LLM call passes through it, so you get complete logs, cost tracking, and performance monitoring without instrumenting each application service.

API Routing

The gateway routes requests based on model availability, cost, latency requirements, and user tier. Define routing rules that map request characteristics to target models.

Cost-based routing sends inexpensive queries to cheaper models. A simple Q and A about documentation can use a small model like Claude Haiku in cents. Complex reasoning tasks route to Claude Opus.

Latency-based routing sends time-sensitive requests to faster models. User-facing chat needs sub-second responses and should use the fastest available model. Background processing can use more powerful but slower models.

Feature-based routing sends requests from different product features to different models. Your chatbot feature uses one model configuration while your summarization feature uses another. The gateway handles this transparently.

Rate Limiting

LLM APIs have strict rate limits. Exceeding them causes 429 errors and degraded user experience. The gateway enforces rate limits across your entire application, so one aggressive feature does not starve others.

Implement token-based rate limiting. Track tokens consumed per time window per model. When the limit is approached, queue requests or route to a fallback model. Token-based limits are fairer than request-based limits because they account for variable-length prompts.

Set per-user and per-feature rate limits. A power user should not be able to consume your entire API budget. Feature-level limits prevent a runaway background job from blocking user-facing features.

Queue requests that exceed rate limits instead of rejecting them. For user-facing features, a brief queue with good UX is better than an error message. For background jobs, batch requests and process them when capacity is available.

Fallback Models

No LLM provider is perfectly reliable. Providers experience outages, latency spikes, and degraded quality. The gateway should automatically fail over to alternative models or providers.

Define a fallback chain. If the primary model fails or exceeds latency thresholds, try the next model in the chain. For example: Claude Opus fallback to GPT-4, then GPT-4 fallback to Claude Sonnet, then Sonnet fallback to a cached response or graceful error message.

Test fallback behavior regularly. A fallback chain that is never tested may fail when you need it. Run chaos engineering exercises that simulate provider outages and verify the gateway routes correctly.

Cache successful responses at the gateway level. If a model fails and the fallback succeeds, cache the response. Subsequent identical queries can skip the LLM entirely and serve cached responses.

Cost Management

The gateway provides centralized cost control. Without it, every developer adds LLM calls without understanding the cost implications. With it, you have visibility and control.

Track cost per request, per user, per feature, and per model. This granular view lets you optimize costs where they matter most. If one user accounts for 20% of your LLM costs, you might need to discuss their usage patterns.

Set spending limits per feature or per user. When a limit is approached, apply cost controls: route to cheaper models, reduce max_tokens, or block expensive requests. Better to degrade gracefully than to receive an unexpected thousand-dollar bill.

Log every cost event. When you need to reconcile invoices or investigate cost anomalies, the gateway provides the data. Cost logging is also essential for building usage-based pricing for your own AI product.

Logging and Observability

Centralized logging through the gateway provides complete visibility into LLM usage. Log every request with input, output, model used, tokens consumed, latency, and cost.

Store logs for compliance and debugging. For regulated industries, LLM call logs are audit evidence. For debugging, logs let you replay problematic requests to understand and fix issues.

Mask sensitive data in logs. LLM prompts often contain PII or business confidential information. The gateway should redact sensitive fields before storing logs. Use pattern matching or a classification model to identify sensitive data.

Build dashboards from gateway data. Track request volume, error rates, latency percentiles, model distribution, and cost per day. These dashboards should be the first thing you check when investigating production issues.

Several open-source and commercial AI gateways exist: Portkey, Helicone, and LiteLLM. Evaluate them against your requirements before building a custom gateway. The right choice depends on your scale, compliance needs, and engineering resources.