Unified API & AI platform — Use case

One platform for every API and every AI call — same audit, same identity, same permissions, same kubernetes. The technology bet you only have to make once.

Executives · Strategy · For executives

One platform. Every API. Every model. Every agent.

AI is just another protocol on the gateway. Apinizer runs your REST, SOAP, gRPC, WebSocket, and GraphQL alongside your LLM, MCP, and A2A traffic — with the same audit, identity, and access controls.

Request a demo · Read the docs

The problem

The problem

AI doesn't get its own platform. It gets a bypass — or a duplicate.

When the AI team needs a gateway, the org has two bad options: bolt a second platform on top of the first (and budget two of everything — identity, audit, ops), or skip the gateway entirely (and watch model traffic disappear from the books). Apinizer's unified plane gives executives a third option: one stack, every protocol, every audit, every dashboard — including AI.

At a glance

1 — platform
10+ — protocols (REST · SOAP · gRPC · GraphQL · WS · MCP · A2A · LLM …)
0 — duplicate budgets

Capabilities

Every protocol, one runtime

REST, SOAP, gRPC, WebSocket, GraphQL, and AI traffic — LLM, MCP, agent-to-agent — handled by the same gateway, the same way.

One identity surface

OAuth2, OIDC, JWT, LDAP, AD, SAML — wired once, reused everywhere. AI consumers authenticate like API consumers.

One audit ledger

Every request, every change, every grant — captured at the framework boundary. The AI plane is not an audit gap.

AI Gateway as a first-class lane

Multi-LLM routing, MCP and A2A as first-class proxy types, semantic caching, RAG on your own VectorDB, prompt firewalls — without leaving the platform.

Shared lifecycle

Same design, ship, version, retire flow for API and AI endpoints. Same APIops manifests. Same promotion path.

Shared observability

One Elasticsearch-backed analytics surface. API traffic and AI traffic on the same dashboards, joined to the same audit timeline.

Real-world examples

Banking

Scenario: Istanbul Tier-1 bank consolidates 2 gateways and 1 AI router

Outcome: Two API gateways and a separate AI gateway POC become one platform. Identity, audit, and ops budgets reclaim two FTEs in the first year.

Metric: 3 stacks → 1

Automotive

Scenario: Munich OEM unifies vehicle telemetry APIs and copilot LLM traffic

Outcome: Connected-car APIs and the in-vehicle assistant share the same gateway, same identity, same audit. Auditors stop asking 'what about the AI part'.

Government

Scenario: Riyadh ministry ships a citizen-services chatbot under existing controls

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

Insurance

Scenario: Paris insurer puts claims AI behind the same gateway as claims APIs

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

Telecom

Scenario: Milan carrier retires a vendor AI router after 5 months

Outcome: Multi-LLM routing, semantic caching, and observability arrive in Apinizer. The standalone AI gateway is decommissioned; ops sleep easier.

Metric: 1 vendor retired

Retail

Scenario: Amsterdam retailer governs supplier API 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: Prague ministry consolidates 7 ad-hoc API surfaces into one

Outcome: Each department had its own gateway. Apinizer brings them onto one Manager with project scopes. Cost-to-serve drops; cross-department flows stop hand-rolling.

Energy

Scenario: Baku utility puts MCP servers next to SCADA APIs

Outcome: Operations agents hit MCP servers through the gateway. The audit, identity, and permission model is the one operators already trust for SCADA.

Recommended modules

API Gateway — Multi-protocol runtime that handles every traditional API on one plane.
AI Gateway — AI lane on the same gateway — multi-LLM routing, MCP governance, A2A gateway, prompt firewalls.
Identity Manager — One identity surface for API consumers, AI consumers, and agent identities.
Analytics Engine — Shared dashboards for API and AI traffic — joined to the same audit timeline.

Resources

Unified platform overview — How API and AI traffic share the same Manager, Workers, audit, and identity surface.
Architecture overview — The full topology — control plane, data planes, AI lane, identity surface.
AI Gateway — What the AI lane looks like — and why it doesn't need a separate stack.
AI Gateway lane — The AI plane on the same Manager, Workers, audit, and identity surface as your APIs.
Customers — Banks, ministries, defense, telecom — teams running one platform for both API and AI.
Why Apinizer — Engineering choices that let one platform carry both protocol surfaces.

Legacy modernization — For executives
AI cost control — For executives
AI Gateway — For AI teams
Multi-LLM routing — For AI teams

Next step

Make the bet once

One platform for every API and every AI call.

A 30-minute walkthrough — API plane, AI plane, shared identity, shared audit — on a Kubernetes of your choice.

Book a Demo · Read the docs