What is API Gateway for ML?

Success metrics include system uptime, model performance stability, deployment velocity, and operational cost efficiency.

Beyond standard authentication, rate limiting, and routing, an ML gateway should handle model version routing for A/B tests and canary deployments, request validation against model input schemas, prediction caching for duplicate requests, model-specific timeout configurations, and response enrichment with metadata like model version and confidence indicators. It should also log prediction requests and responses for monitoring and compliance. Think of it as both a traffic manager and an ML-specific middleware layer.

Start with a standard gateway like Kong, AWS API Gateway, or Envoy with custom plugins for ML-specific needs. Building a custom gateway is only justified when you need deep integration with your model serving framework that plugins can't achieve. Standard gateways handle 90% of ML gateway requirements out of the box. Add custom middleware for prediction caching, model routing logic, and input validation. The build-versus-buy break-even is typically at 10+ production models with complex routing requirements.

Use API keys for service-to-service communication within your infrastructure. Use OAuth or JWT tokens for external consumer authentication. Set rate limits based on the cost of model inference, not just traffic volume. A model requiring GPU inference should have tighter limits than a CPU-based model. Implement tiered rate limits for different consumer priorities. Monitor rate limit hits as a capacity planning signal since increasing hits indicate growing demand that may require scaling.

Beyond standard authentication, rate limiting, and routing, an ML gateway should handle model version routing for A/B tests and canary deployments, request validation against model input schemas, prediction caching for duplicate requests, model-specific timeout configurations, and response enrichment with metadata like model version and confidence indicators. It should also log prediction requests and responses for monitoring and compliance. Think of it as both a traffic manager and an ML-specific middleware layer.

Start with a standard gateway like Kong, AWS API Gateway, or Envoy with custom plugins for ML-specific needs. Building a custom gateway is only justified when you need deep integration with your model serving framework that plugins can't achieve. Standard gateways handle 90% of ML gateway requirements out of the box. Add custom middleware for prediction caching, model routing logic, and input validation. The build-versus-buy break-even is typically at 10+ production models with complex routing requirements.

Use API keys for service-to-service communication within your infrastructure. Use OAuth or JWT tokens for external consumer authentication. Set rate limits based on the cost of model inference, not just traffic volume. A model requiring GPU inference should have tighter limits than a CPU-based model. Implement tiered rate limits for different consumer priorities. Monitor rate limit hits as a capacity planning signal since increasing hits indicate growing demand that may require scaling.

Beyond standard authentication, rate limiting, and routing, an ML gateway should handle model version routing for A/B tests and canary deployments, request validation against model input schemas, prediction caching for duplicate requests, model-specific timeout configurations, and response enrichment with metadata like model version and confidence indicators. It should also log prediction requests and responses for monitoring and compliance. Think of it as both a traffic manager and an ML-specific middleware layer.

Start with a standard gateway like Kong, AWS API Gateway, or Envoy with custom plugins for ML-specific needs. Building a custom gateway is only justified when you need deep integration with your model serving framework that plugins can't achieve. Standard gateways handle 90% of ML gateway requirements out of the box. Add custom middleware for prediction caching, model routing logic, and input validation. The build-versus-buy break-even is typically at 10+ production models with complex routing requirements.

Use API keys for service-to-service communication within your infrastructure. Use OAuth or JWT tokens for external consumer authentication. Set rate limits based on the cost of model inference, not just traffic volume. A model requiring GPU inference should have tighter limits than a CPU-based model. Implement tiered rate limits for different consumer priorities. Monitor rate limit hits as a capacity planning signal since increasing hits indicate growing demand that may require scaling.