Production ML needs more than just model inference. Learn how AI gateways provide routing, load balancing, monitoring, and traffic management for reliable model serving at scale.
Directly exposing model servers to clients creates operational nightmares:
Can't route between model versions, perform A/B tests, or gradually roll out new models. Every deployment is all-or-nothing with high risk.
Every model service reimplements authentication, rate limiting, logging, and monitoring. Inconsistent behavior across services, maintenance nightmare.
Without intelligent routing, some model servers get overloaded while others sit idle. No automatic failover when instances crash.
Clients hardcode model endpoints. Changing infrastructure requires coordinating client updates. Model versioning forces client code changes.
Route requests to appropriate model versions based on headers, client ID, geographic location, or custom rules.
X-Model-Version: v2.1Distribute traffic across model instances and scale capacity based on demand.
Simple, works well for uniform requests. Each instance gets equal traffic.
Route to instance with fewest active connections. Better for variable request durations.
Send more traffic to higher-capacity instances (more GPUs, better hardware).
Route to fastest-responding instances. Automatically avoids slow/overloaded servers.
Centralize security and abuse prevention so model services focus on inference.
Cache predictions at gateway level to reduce load on model servers and improve latency.
For recommendation systems, 40-60% cache hit rate is typical. Search/classification can achieve 70-80% for common queries.
Prevent cascading failures when model services experience issues.
Centralized logging, metrics, and tracing across all model services.
Our infrastructure team has built AI gateways serving billions of predictions. Get expert guidance on architecture and implementation.
Yes! Even with one model, gateways provide critical features: authentication, rate limiting, caching, monitoring, and circuit breaking. Plus, you'll eventually add more models, and the gateway makes multi-model management trivial. It's easier to start with a gateway than retrofit one later.
Modern gateways add 1-5ms latency for simple routing. With caching enabled, 40-60% of requests return in under 5ms from cache, actually reducing average latency. The benefits (load balancing, failover, observability) far outweigh minimal overhead. Use gRPC instead of REST to minimize gateway latency.
Use header-based routing: clients send X-Model-Version header, gateway routes to appropriate backend. Run multiple versions simultaneously. Implement canary deployments: route 5% to new version, monitor metrics, gradually increase. Default to latest stable version for clients not specifying version.
Absolutely. Response caching reduces model server load by 40-60%, cutting compute costs proportionally. Intelligent load balancing maximizes utilization. Auto-scaling based on actual demand prevents over-provisioning. Rate limiting prevents abuse and runaway costs. Many teams save 30-50% on inference costs with proper gateway setup.
Track: request rate, latency (p50/p95/p99), error rate, cache hit ratio, backend health, and active connections. Set up alerts for: error rate over 1%, p99 latency over 500ms, cache hit ratio under 30%, or backend failures. Use distributed tracing to identify bottlenecks. Dashboard should show both gateway and downstream model service metrics.
Don't let infrastructure become your bottleneck. Our team will design and implement AI gateway architecture that scales reliably to millions of predictions.