AI Gateway

Every agent your organization runs eventually calls a model. Multiply that across the agents you ship, and the traffic behind those calls becomes infrastructure in its own right. That infrastructure needs the same routing, cost control, and governance you expect from anything else in your stack.

This page covers what an AI gateway is, why the agentic SDLC makes it urgent, where the gateway layer runs out of road, and how Port picks up from there.

What is an AI gateway?

An AI gateway is a single layer that every model call passes through before it reaches a provider such as OpenAI, Anthropic, AWS Bedrock, Azure, or Databricks. It exists so routing, cost control, and safety logic live in one place, instead of being copy-pasted into every app and agent that calls an LLM.

LiteLLM, Vercel AI Gateway, and Bifrost are three established options doing this today. Different implementations, same core job: one control point standing in front of many providers.

A gateway typically gives you:

• A unified API. One interface across every provider, instead of a different integration per model.
• Routing and failover. Traffic keeps flowing when a model or provider degrades.
• Budgets and rate limits. Spend gets capped before it becomes a surprise.
• Guardrails. Safety and compliance checks enforced in-line, on every call.
• Observability. Logs, traces, and cost data per request.
• An MCP / tool gateway. One front door for the external tools agents reach for.

Why the agentic SDLC makes this urgent

API gateways have existed for years. What has changed is who is making the calls.

Agents multiply call volume. A single task can fan out into dozens of model calls chained across tools, where a human doing the same task might have made one API call, if any. Traffic that used to scale with headcount now scales with how many agents you have turned loose.

Tool and MCP sprawl compounds it. Agents do not just call models. They reach for external tools and data sources constantly, and each one is a new integration point with its own access surface.

Cost stops being predictable. Usage now scales with agent autonomy, not release cadence. A single agent given more latitude can generate a month's typical spend in a day, with no code deploy to flag it.

Mistakes propagate faster. An ungoverned agent can act on a bad model response, call the wrong tool, or write to the wrong system before a human ever sees it. There is no code review step in the loop the way there is for a human-written pull request.

An AI gateway is the layer that makes this survivable: one place to route, cap, and guard traffic instead of trusting every agent and app to do it correctly on its own.

What gateways still miss

A gateway solves the traffic problem. It does not solve the organizational one. Four gaps show up consistently once agents are running through it at scale.

Traffic, not organization

A gateway sees the key that called the model. It does not see the team that owns that key, or the cost behind it. That gap matters twice over. Without ownership, an orphaned key (left behind after the agent it belonged to was retired) keeps spending indefinitely, because nothing flags that it should have been revoked. Without team attribution, "which team is driving this month's spend" turns into a manual reconciliation instead of a query anyone can run themselves.

Config-driven, not workflow-driven

Budgets, guardrails, and access are things you set in a config file or through an API. There is no built-in path for a developer to request a change, or for security to approve one. Agentic development moves at the pace of iteration, not release cycles. A developer testing a new agent needs a budget increase today, not after a config PR gets reviewed, so without a request path, they either wait or find a way around the guardrail entirely.

A runtime view, cut off from everything else

A gateway can tell you which agents and MCP servers are live right now. It cannot tell you whose repo any of them came from. An agent's behavior is inseparable from the code and tools it is built from, so when that runtime view is not connected to the service or repo that owns it, an incident means cross-referencing two separate systems by hand, under time pressure.

Dashboards without enforcement

A gateway's built-in UI shows spend and usage, but it stops at visibility. There is no mechanism to turn "this agent has no guardrail" into a standard the org actually enforces. As the number of agents, tools, and keys needing governance grows faster than any team can review by hand, a dashboard that only shows the problem becomes one more thing nobody has time to act on.

How Port closes the gap

Port does not replace the gateway. It sits on top of it, turning gateway data into something the rest of the org can see, act on, and enforce.

One catalog, fully connected

Port pulls every key, budget, guardrail, agent, and MCP server the gateway manages into the same software catalog as the rest of your engineering org, related to the team and service that actually owns each one. An agent is just another artifact your org ships. It belongs next to the service and repo it comes from, not off in a system only one team ever opens. This is also what turns cost attribution from a manual exercise into a standing report: because every key is tied to a team, "who's driving spend" is a query, not a reconciliation project.

See External agents and the agent registry for how Port inventories agents alongside the rest of your catalog.

Internal tools carry the same risk

A gateway governs which MCP servers an agent can reach, but MCPs are not the only tools an agent calls. Port exposes your organization's own self-service actions and golden paths as tools too, and those need the same governance question an MCP does. They are already org-approved business logic, but approved to exist is not the same as safe for any agent to invoke unsupervised. As agents take on more of the SDLC, the tools they reach for are increasingly internal, not external APIs, so governance that stops at MCPs misses the fastest-growing category of what agents can actually do. Port's governed MCP connectors and Port MCP server apply the same access-scoping logic here, regardless of whether the tool is an MCP server or an internal workflow.

Gateway operations become self-service

Issuing a key, requesting a budget increase, testing a guardrail, or invoking a registered agent can each become a governed Port action, complete with approvals and an audit trail, instead of a direct call to the proxy. Agent development iterates fast enough that any manual step (filing a ticket for a key, pinging platform on Slack) becomes the actual bottleneck in the loop, not the model call itself.

Complete governance across the whole AI stack

Port scorecards extend the same standards you already apply to services and pipelines to the gateway's agents, guardrails, and keys, so an ungoverned agent gets caught by the same mechanism as an undocumented service. Two pieces make this hold up as agents scale:

• Unified agent creation. Every agent gets scaffolded through Port, so the right traffic rules apply from the moment it exists, not bolted on afterward. See Build an AI agent and External agents.
• Dynamic policies. Budget caps, tool access, and creation permissions can flex based on context (who is asking, what they are doing) instead of a static rule that either blocks legitimate work or lets risky work through. See dynamic permissions for the same pattern on self-service actions.

Once agents outnumber the services they support, governance has to be built into how they are created, not a step someone remembers to run.

Related capabilities

You need to…	Port capability
Inventory agents alongside services and teams	External agents and agent registry
Govern which MCP servers and skills agents may use	AI registry
Connect IDEs and agents to your catalog	Port MCP server
Enforce standards on agents, keys, and guardrails	Scorecards

The gateway routes the traffic. Port governs what runs through it.