AI Gateway

An AI gateway is a control layer placed between applications and one or more artificial intelligence services. Like an API gateway, it receives requests and forwards them to backends, but it adds AI-specific controls for model selection, token or compute usage, safety, privacy, cost, and performance. It can provide one stable endpoint for cloud models, self-hosted systems, and Ultralytics YOLO model serving, making production artificial intelligence systems easier to govern as their models and providers change. (learn.microsoft.com)

Link to this sectionHow An AI Gateway Works#

The gateway evaluates each incoming request before sending it to an inference engine. Depending on configured policies, it may:

• Authenticate and protect requests: Apply access controls, quotas, input validation, and defenses based on the OWASP Top 10 for LLM Applications, alongside broader data security practices.
• Route traffic intelligently: Select a model or endpoint based on latency, availability, cost, region, task, or hardware load. The Kubernetes Gateway API Inference Extension standardizes model-aware routing for self-hosted generative models.
• Improve reliability: Use retries, load balancing, and Vercel AI Gateway model fallbacks when a provider or model becomes unavailable.
• Control consumption: Enforce request, token, or compute budgets through policies such as Envoy Gateway rate limiting.
• Record telemetry: Capture latency, errors, model choices, and usage through observability systems using standards such as OpenTelemetry GenAI attributes. (gateway.envoyproxy.io)

Link to this sectionReal-World Applications#

• Retail Vision Inspection: Cameras submit product images through a gateway to a YOLO26 object detection model. The gateway authenticates each store, limits request volume, routes traffic to the nearest deployment, and sends failures to a backup endpoint, supporting dependable real-time inference.
• Multi-Model Customer Assistant: An application uses the Vercel AI Gateway unified API or Cloudflare AI Gateway to route simple questions to a lower-cost model and complex requests to a more capable one. Logs support cost analysis, debugging, and model monitoring.
• Enterprise AI Access: Organizations can use Azure API Management AI gateway capabilities to govern models, tools, and remote Model Context Protocol services through centralized authentication, quotas, logging, and content-safety policies. (learn.microsoft.com)

Link to this sectionComputer Vision Example#

The inference code remains focused on prediction while the gateway handles access, routing, limits, and telemetry:

from ultralytics import YOLO

model = YOLO("yolo26n.pt")
results = model.predict("https://ultralytics.com/images/bus.jpg")
detections = len(results[0].boxes)
print({"detections": detections})

This handler could run behind an Ultralytics Platform deployment endpoint, where deployment monitoring tracks requests, latency, errors, logs, and health checks. (learn.microsoft.com)

Link to this sectionAI Gateway Vs. Related Terms#

An AI gateway manages traffic before and after model execution, while model deployment places a model into production and model serving executes predictions. An inference gateway is more specialized, optimizing routing among model replicas or accelerators. Meanwhile, AI agent orchestration coordinates multi-step decisions and tools rather than controlling network access.

Current best practices include minimizing logged sensitive content, applying data privacy controls, testing fallback paths, tracking per-model quality and cost, and following the NIST Generative AI Risk Management Profile. Recent research on LLM control planes and adversarial risks in model routing also highlights the importance of auditable policies and secure routing decisions. (nist.gov)