AI Gateway Guide

Problem: Applications often use OpenAI, Claude, Gemini, DeepSeek, Qwen, and private models at the same time. Each provider has different APIs, limits, pricing, and reliability behavior.

Solution: An AI gateway creates a unified traffic management layer so teams can route requests, abstract provider differences, and change models without rewriting every application.

Problem: LLM usage can scale faster than expected because cost depends on tokens, context length, retries, agents, and background automation.

Solution: Teams can apply token-aware limits, cost-aware routing, caching strategies, and usage monitoring before spend becomes an invoice surprise.

Problem: Model providers can hit rate limits, fail regionally, return slow responses, or change behavior across model versions.

Solution: Gateway-level fallback, retry, load balancing, and circuit breaking help AI applications stay available even when one backend is degraded.

Problem: Without a shared layer, teams struggle to see which products, tenants, prompts, models, or agents drive latency, errors, token usage, and cost.

Solution: AI observability at the gateway records request metadata, model behavior, token consumption, latency, errors, and policy events in one place.

Route traffic to a specific provider based on customer region, data policy, contracted provider, or application preference.

Example: Example: route EU tenant traffic to an approved regional provider while routing internal prototypes to a public model API.

Choose models based on task requirements such as reasoning depth, coding, vision, long context, embeddings, or tool calling.

Example: Example: send summarization requests to a fast general model, but send code-generation requests to a model optimized for developer tasks.

Use observed provider latency or region latency to keep interactive AI experiences responsive.

Example: Example: if GPT-4-class latency exceeds a threshold, route eligible traffic to another available model until health improves.

Match model cost to request value, tenant plan, or workload complexity instead of sending every request to the most expensive model.

Example: Example: if token cost exceeds a tenant budget, route simple requests to cost-efficient models and reserve premium models for high-value tasks.

Define provider or model backups when the primary model is unavailable, rate limited, or degraded.

Example: Example: retry the primary provider once, then fail over to a secondary provider with compatible output constraints.

Continuously adapt routing based on provider error rates, timeout behavior, streaming interruptions, or regional availability.

Example: Example: temporarily drain traffic from a provider when circuit breaking detects sustained 5xx errors or timeout spikes.

A platform team gives product, internal, and data teams one governed path to models with shared access control, audit logs, prompt policies, cost controls, and provider approvals.

Agents call models, APIs, tools, and MCP servers. A gateway controls model access, tool permissions, MCP server access, budgets, and audit logs.

A SaaS product embeds AI for many tenants and needs tenant isolation, plan-based quotas, resilient routing, usage tracking, and clear cost attribution.

An enterprise uses public model APIs for some workloads, private or self-hosted models for sensitive workloads, and regional providers for data residency.