Components multiply fast, from LLMs to MCP and AI Agents, and the parties using AI diversify across organizations, users, services, and Agents.
Business · AI Products
Every AI Call in the Enterprise,
Controlled Through One Gateway
AURDA AI Gateway is the single gateway that every public and private model, MCP, and AI Agent call passes through. Access rights, quotas, budgets, and AI safety policies are controlled centrally; routing and failover keep services stable; tokens, requests, and traces are observed in one place. It is a standalone product with no dependency on an existing platform: combine it with AURDA AI Infra as a Suite, or deploy it on its own.
Why
The Wider AI Use Spreads, the Harder It Is to Manage
As components grow beyond models to MCP and Agents, and as the parties using them multiply, a consistent system of control and operations becomes necessary. Regulation brings that moment forward.
Expanding AI use
Policy demands grow, from controlling AI access and usage to managing sensitive data.
Scattered AI usage and request flows are hard to see as a whole.
Regulatory compliance
Korea's AI Basic Act imposes risk management, monitoring, and record-keeping duties for high-impact AI. Control and logging systems need to be in place within the grace period.
- Risk management
- Monitoring
- Record-keeping
Generative AI use in the public sector now requires usage logs and access control. Tiered access control is the means of meeting the requirement.
- Usage logs
- Access control
As external LLM adoption opens up, control over mixed external and internal models, usage records, and internal audit demands follow.
- Mixed-model control
- Usage records
- Internal audit
Concept
The One Gateway Every AI Call Passes Through
Calls from users, business services, and AI applications pass through AI Gateway to public and private models, MCP servers, and AI Agents. Callers only need the gateway address, and a model swap behind it changes nothing for them.

Console
From Connection to Observation, in One Console
These are the product screens as they are. Model and MCP connections, access profiles, guardrails, routing, and usage are all operated from one console.

Dashboard
The Whole Gateway on One Screen
Requests, token usage, average latency, and estimated cost are shown as a monthly total and for today. Hourly request and token trends sit alongside the routing strategy distribution.
Architecture
What One Gateway Does, in Pictures
Where calls come from, where they go, and what AI Gateway controls and distributes in between.
Control Pipeline
Four Checks Between a Request and the Model
Authentication and access, quota and budget, guardrails, and human approval for high-risk requests, in that order. Requests that fail are blocked or rejected. Every step is recorded, which becomes the evidence for AI Basic Act and N2SF compliance.
Routing
Distribute by Cache and Load, Roll Out New Models Gradually
Repeated questions are answered from cache, and requests are distributed by load and processing history. A new model takes a share of requests first (canary), is validated, then widened. On an outage, traffic fails over to an alternate path automatically.
Key Features
Connect, Control, Route, Observe: One Gateway
Models, MCP, and Agents connect through one gateway, and policies, routing, and usage are managed centrally.
Public and private models, MCP servers, and AI Agents connect through a single gateway endpoint. One standard API links existing AI services quickly, and swapping a model behind it leaves the caller untouched.
From standard protocols to OCR and newly released APIs, services attach flexibly in passthrough or custom mode. Manufacturing equipment and system protocols connect through an add-on.
MCP servers are registered, deployed, and versioned here. Access rights and call control for registered MCP servers are handled in the same place.
Profiles and API keys are issued per user, organization, and service, and role-based access control (RBAC) decides which models and tools each may reach.
Usage is measured in each model's own units, whether tokens, pages, or requests, and budgets are allocated and capped by organization, team, and service.
AI safety policies apply consistently to inputs and outputs, and high-risk requests run only after a person approves them. Approval and rejection history is retained as audit evidence.
Requests are distributed according to processing history and load. The same question is not processed from scratch every time.
Requests spread across multiple backends whose health is checked continuously, and traffic fails over to an alternate path on an outage.
A new model is rolled out to a share of requests first, checked, and then widened. Model replacement risk stays minimal.
Throughput, latency, and errors per gateway and per model, on one screen.
Token usage and a trace per request show where time goes, from queueing to generation.
AI usage and cost are analyzed together and reported on the enterprise's own cycle, weekly, monthly, or yearly.
Editions
Pick the Level of Control You Need
Standard covers connection and access control. Enterprise adds tiered budgets, guardrails, and HITL. The manufacturing protocol comes as an add-on.
Standard
The starting point for organizations that need model connection and access control.
- Unified endpoint management
- Authentication and authorization (RBAC)
- Token quotas
Enterprise
For organizations running AI across departments with audit obligations.
- Everything in Standard
- Budget control by organization, team, and service tier
- Guardrails and unified security
- HITL approval
Manufacturing Protocol Add-on
Brings shop-floor data and AI calls under the same control. It can be added to either edition.
- Manufacturing equipment and system protocol connection
Positioning
A Complement to Security Products, Not a Competitor
Data-leak prevention stays with security products. Model connection, access control, and budget control belong to AI Gateway: an AI-specific, standalone gateway that runs inside your network.
| Feature | Korean AI security gateways | Open source gateways | Global API management platforms | AURDA AI Gateway |
|---|---|---|---|---|
| Core role | Data-leak prevention (DLP) | Model call proxy and routing | An add-on to API management | Single gateway and usage control for models, MCP, and Agents |
| Governance | Input and output security policies | Basic key and limit management | Assembled from plugins | RBAC, tiered budgets, guardrails, HITL |
| Cost control | Out of scope | Basic usage aggregation | Per-request billing management | Token budgets by organization, team, and service |
| Deployment and support | On-premise, Korean support | Self-built, self-operated | Global pricing and support | In-network deployment, commercial support, audit response |
Qualitative comparison based on our products, October 2026Security products block data leaks; AI Gateway controls model connection, access, and budgets. The two work together as complements.
AURDA Suite
One System, from Resources to Usage Control
AI Infra + AI Gateway
AI Infra Runs Resources and Serving; AI Gateway Runs Access and Control
AURDA AI Infra runs GPU and NPU resources and model and Agent serving. AURDA AI Gateway handles access, control, and integration. MCP servers are registered, deployed, and versioned in AI Gateway, which also controls their access rights and calls. Deploy them separately or together; AI Gateway also runs on its own in a third-party GPU environment.
This download is a product brochure (PDF). We do not distribute software installers.To see the product in action, please reach us through Contact Sales.
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AURDA AI Gateway Product Overview
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