AI Gateway & Model Access
— one front door to every model

A governed AI gateway that sits between your applications, agents and every model provider — unified access, token-level observability, cost control and guardrails enforced once, at the gateway, across Azure, AWS, Google Cloud and OpenRouter.

Every serious AI estate has a gateway

Direct app-to-model calls are now an anti-pattern. A gateway gives one endpoint, one identity model, one audit log and one bill — while the models behind it change monthly.

Guardrails moved to the edge

Prompt injection defence, PII redaction, content safety and tool allow-lists are enforced at the gateway for every app and agent, not re-implemented per team.

Cost and reliability are routing problems

Model routers pick the cheapest model that meets a quality bar, semantic caches remove duplicate calls, and cross-provider failover keeps agents running through outages and rate limits.

Azure API Management GenAI gateway policies (token limits, token metrics, semantic caching, load balancing across Azure OpenAI and Foundry Models) plus Azure AI Foundry Model Router and Content Safety / Prompt Shields.

Amazon Bedrock as the unified model API with Bedrock Guardrails, cross-region inference, application inference profiles for cost allocation, and API Gateway / Lambda or a dedicated LLM gateway in front.

Apigee as the AI gateway (token quotas, semantic caching, Model Armor policy enforcement) over Vertex AI Model Garden — Gemini, Claude, Llama, Mistral behind one policy plane.

A neutral gateway to hundreds of models from every major lab through one OpenAI-compatible API — provider routing, fallbacks, spend limits and per-key controls, ideal for multi-model evaluation and portability.

LiteLLM, Portkey, Kong AI Gateway and Envoy AI Gateway for organisations that want the policy plane independent of any cloud.

The same choke point governs agent tool calls: MCP server registry, per-agent identity, tool allow-lists and action logging.