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 API 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 Infra as a Suite, or deploy it on its own.
Connect · Control · Route · ObserveThe four jobs an enterprise AI service needs, handled by one gateway
StandaloneBuilt and deployed on its own, with no dependency on an existing platform
AI FinOpsUnified analysis of AI usage and cost, reported weekly, monthly, and yearly
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.
Generative AI use in the public sector now requires usage logs and access control. Tiered access control is the means of meeting the requirement.
As external LLM adoption opens up, control over mixed external and internal models, usage records, and internal audit demands follow.
Architecture
What One Gateway Does, in Pictures
Where calls come from, where they go, and what the Gateway controls and distributes in between.
Single Gateway
The One Gateway Every AI Call Passes Through
Calls from users, business services, and AI applications pass through the 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.
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.
Operating Flow
From Connection to Observation in Six Screens
Register providers and endpoints to connect models, validate in the Playground, configure profiles, API keys, and policies, set up routing, then observe tokens, requests, and traces in Monitoring.
Key Features
Connect, Control, Route, Observe: One Gateway
Models, MCP, and Agents connect through one gateway, and policies, routing, and usage are managed centrally.
Connect
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.
Connects manufacturing equipment and system protocols, bringing shop-floor data and AI calls under the same control.
Control
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.
Route
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.
Observe
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.
Unified endpoint management, authentication and authorization (RBAC), and token quotas. The starting point for organizations that need model connection and access control.
Everything in Standard, plus budget control by organization, team, and service tier, guardrails and unified security, and HITL approval. For organizations running AI across departments with audit obligations.
A module that connects manufacturing equipment and system protocols. It can be added to either edition.
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 the Gateway: an AI-specific, standalone gateway that runs inside your network.
| Feature | Korean AI security gateways | Open source gateways | Global API management platforms | AURDA API 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; the Gateway controls model connection, access, and budgets. The two work together as complements.
AURDA Suite
One System, from Resources to Usage Control
Infra + Gateway
Infra Runs Resources and Serving; Gateway Runs Access and Control
AURDA Infra runs GPU and NPU resources and model and Agent serving. AURDA API Gateway handles access, control, and integration. For MCP, Infra registers, deploys, and versions servers while Gateway controls access rights and calls. Deploy them separately or together; Gateway also runs on its own in a third-party GPU environment.
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