AI Gateway — Use case

One gateway for every AI call — multi-LLM routing, MCP server governance, agent-to-agent gateway, prompt firewalls, and AI observability. Same audit, same identity, same Kubernetes as your APIs.

AI teams · Platform · For AI teams

Every AI call lives on the gateway.

Apinizer's AI Gateway routes prompts, governs MCP servers, registers agent identities, filters bad input, and posts cost to the same audit trail you already use for APIs.

Request a demo · Open AI Gateway

The problem

The problem

AI traffic without a gateway is a second platform you didn't budget for.

Teams that skip the gateway get a parallel universe — model keys in env vars, MCP tools wired straight into apps, agents talking without identity, costs invisible until the invoice. Apinizer treats AI as just another lane: same Manager, same Workers, same audit, same identity. The AI plane is a feature, not a side project.

At a glance

1 — AI plane
5+ — AI protocols (LLM · MCP · A2A · embeddings · vision)
0 — audit gaps

Capabilities

Multi-LLM routing

Route prompts by capability, cost, latency, and sovereignty — frontier when needed, regional when sufficient, open-weight when sovereign.

MCP server governance

Every MCP tool registered, scoped, audited. Agents only see the tools you allow; every call lands in the audit ledger.

Agent-to-Agent gateway

A2A identity, scope, and trust live on the gateway. Agents authenticate like people; the gateway keeps the trust boundary.

Prompt firewalls

Injection, jailbreak, topic, DLP, and checksum-validated PII masking at the edge — with an external guardrail engine behind them when a pattern is not enough. Bad prompts blocked; clean prompts logged; the compliance answer is one query.

Cache + cost control

Semantic cache short-circuits paid calls; per-consumer token attribution turns every prompt into a line item.

AI observability

Latency, errors, tokens, anomalies — same Elasticsearch surface as your APIs. Trace any AI call back to a consumer, project, and model.

Real-world examples

Banking

Scenario: Istanbul bank puts customer-service LLM behind the same gateway as core APIs

Outcome: AI assistant inherits the API plane's auth, audit, and rate-limit policy. Regulator stops asking 'where does the model live'.

Metric: 1 control plane

Automotive

Scenario: Munich OEM unifies vehicle telemetry APIs and in-vehicle assistant LLM traffic

Outcome: Connected-car APIs and the assistant share one gateway, one identity, one trace. AI rollout doesn't reopen the platform RFP.

Government

Scenario: Riyadh ministry ships a citizen chatbot under existing controls

Outcome: Chatbot is just another endpoint. Authentication, audit, and rate-limit inherited from the citizen API surface — no new compliance memo.

Insurance

Scenario: Paris insurer routes claims AI through the same gateway as claims APIs

Outcome: Underwriting model calls go through the same access control as claims data. One dashboard for both; one auditor answer for both.

Telecom

Scenario: Milan carrier retires a standalone AI router POC after 5 months

Outcome: Multi-LLM routing, semantic caching, MCP governance, observability all arrive in Apinizer. The standalone stack is decommissioned.

Metric: 1 vendor retired

Retail

Scenario: Amsterdam retailer governs supplier API traffic and supplier-agent A2A traffic together

Outcome: B2B partners hit the same gateway; their integration agents are published as A2A agents on the gateway. Same SLA, same audit, same identity.

Public sector

Scenario: Central-European ministry routes local-language prompts to a regional model first

Outcome: 90% of summarization traffic served by an in-region resident model; frontier providers used only for adversarial cases.

Metric: 90% regional

Energy

Scenario: Baku utility runs operations agents on open-weight models inside the operator network

Outcome: Local 70B model serves SCADA-adjacent agents. Hosted providers reserved for non-operational use. The agent never crosses the network boundary.

Route, enforce, cache, audit — in that order.

01 · Route — Capability, cost, latency, and policy decide the model — frontier, regional, open-weight, or cache.
02 · Enforce — Prompt firewall, PII redaction, scope check, and consumer rate limit run before the upstream call.
03 · Cache — Semantic match in the vector index short-circuits paid calls when the meaning has been answered before.
04 · Audit — Prompt, response, tokens, cost, consumer, project — all indexed in Elasticsearch with the rest of your traffic.

Recommended modules

AI Gateway — The AI lane — routing, MCP governance, A2A gateway, firewalls, semantic cache.
Identity Manager — One identity surface for API consumers, AI consumers, and agent identities.
Analytics Engine — Per-model, per-project, per-consumer telemetry on the same dashboards as your APIs.
Monitoring — Provider health, latency probes, anomaly detection across every AI lane.

Resources

AI Gateway overview — The AI lane on the same Manager, Workers, audit, and identity surface as your APIs.
AI Gateway product — Multi-LLM routing, MCP governance, A2A gateway, firewalls, cache.
Architecture overview — Where the AI lane sits in the topology — control plane, data planes, providers.
AI observability — Trace any AI call back to a consumer, project, model, and cost.
Token economics — How per-consumer cost reports turn every prompt into a line item.
Unified API & AI platform — The strategy: one platform, every protocol, every audit — AI included.

Multi-LLM routing — For AI teams
MCP server governance — For AI teams
Agent-to-Agent (A2A) — For AI teams
Prompt firewalls — For AI teams

Next step

One AI plane

Put every AI call on the gateway you already trust.

A 30-minute walkthrough — routing, MCP, A2A, firewalls, cache, observability — on a Kubernetes of your choice.

Book a Demo · Read the docs