AI gateway request routing

An AI gateway routes requests to the right model, provider, or policy path before the request is executed. Think of it like an API gateway, but optimized for LLM/AI traffic.

What it typically does

• Model routing: send requests to GPT, Claude, Gemini, local models, etc.
• Fallbacks: retry on failure or low quality, switch to another provider
• Load balancing: distribute traffic across endpoints
• Cost optimization: choose cheaper models for simpler tasks
• Latency optimization: route to the fastest available model/region
• Policy enforcement: block sensitive data, apply moderation, redact fields
• Prompt transformation: add system prompts, templates, or tool config
• Response normalization: standardize outputs across different models
• Observability: logs, traces, token usage, error rates
• Quota/rate limiting: per user, app, org, or route

Common routing strategies

1. Rule-based

   • If request contains code → route to code model
   • If user is premium → route to stronger model
   • If prompt length > threshold → route to large-context model

2. Content-based

   • Classify intent: chat, summarize, extract, classify, code
   • Route by domain: legal, support, medical, internal search

3. Performance-based

   • Route to the provider with best current latency/error rate
   • Fail over if SLA drops

4. Cost-aware

   • Use small/cheap model unless confidence is low
   • Escalate only when needed

5. Policy-aware

   • Sensitive data routes through compliant providers only
   • Certain tenants restricted to specific regions

Example routing logic

• Simple FAQ → small model
• Long document summary → long-context model
• Code generation → coding-specialized model
• High-risk content → moderated model + policy checks
• Outage on primary provider → fallback provider

A typical architecture

Client → AI Gateway →

• auth/rate limit
• request inspection/classification
• policy checks
• routing engine
• selected model/provider
• post-processing/validation
• logging/metrics

Routing decision inputs

• User/tenant
• Request type
• Prompt size
• Language
• Sensitivity level
• Required tools/function calling
• SLA/latency target
• Cost budget
• Model availability
• Historical quality metrics

If you’re building one

You’ll usually need:

• a request classifier
• a routing rules engine
• provider adapters
• fallback/retry logic
• observability
• policy/middleware
• evals to measure routing quality

If you want, I can also provide:

1. a reference architecture diagram,
2. example routing rules in YAML/JSON, or
3. a sample implementation in Python/Node/Go.