Cloud-Native AI Gateway

Problem

Teams often adopt AI providers directly from application code. That works for prototypes, but production quickly exposes missing controls: no central rate limiting, no token cost attribution, no consistent prompt boundaries, weak fallback behavior, and little visibility into provider latency or failures.

Architecture

The gateway sits between applications and model providers. It evaluates request policy, selects the target provider/model, applies budgets and rate limits, emits OpenTelemetry traces, records token/cost metadata, and keeps provider fallback paths explicit. The goal is to make AI traffic behave like platform traffic, not random external API calls.

Trade-offs

A gateway adds an extra hop and must be operated carefully. The payoff is central control: safer provider migration, clearer budget enforcement, reusable observability, and one place to implement prompt and data-handling policy.

Result

AI usage becomes easier to operate across teams. Incidents have traces, cost spikes have ownership, and provider changes can be handled through infrastructure policy instead of application rewrites.

Constraints

• Keep model latency visible while adding policy, routing, and fallback controls.
• Attribute token usage and estimated cost to services, teams, and request paths.
• Enforce rate limits and prompt/data boundaries without forcing every application to duplicate gateway logic.

Key decisions

• Use a single gateway boundary instead of provider SDK calls scattered through services.
• Emit OpenTelemetry spans for provider choice, token usage, latency, errors, and fallback events.
• Keep provider failover explicit so production behavior is debuggable during degraded provider states.

Failure modes

• Provider outage or regional degradation creates cascading application errors.
• Unbounded token usage turns AI adoption into an uncontrolled cost surface.
• Prompt-policy decisions disappear inside application code and cannot be audited consistently.

Result

AI traffic becomes an operable platform flow with visible policy decisions, model routing, cost attribution, and provider resilience.

Related technologies

Kubernetes · EKS · OpenTelemetry · Prometheus · Grafana · AI Gateway · LLM providers · Policy as code

Why put an AI Gateway in front of model providers?

It centralizes policy, routing, telemetry, rate limiting, provider failover, and cost visibility so every team does not have to rebuild those controls independently.

What should an AI Gateway measure?

Latency, errors, model/provider selection, token usage, estimated cost, rate-limit decisions, policy denials, fallback events, and request traces.