What Is an AI Egress Gateway?
TL;DR: Tetrate Agent Router Enterprise acts as your AI egress gateway, so your agents reach approved models and tools and your provider keys stay out of application code.
An AI egress gateway is the control point for calls leaving your cluster for model providers and MCP servers. The gateway decides which destinations your agents can reach, adds provider keys on the way out, and counts tokens per team.
Many teams start with a direct call from application code to a provider. Each app stores a provider key. Each app picks a model. No system records which team spent which tokens, and your finance team learns the cost from the invoice.
Tetrate Agent Router Enterprise sits on the outbound path from your agents. Agent Router Enterprise gives each team and each agent a gateway key, routes calls to approved models, and records tokens on each call. AI ingress vs. AI egress covers both directions of AI traffic.
What an AI Egress Gateway Controls
The gateway controls destinations. Your platform team lists the model providers and MCP servers that agents may reach. The gateway blocks every other destination at the exit point.
The gateway controls models. A team may use one approved model for a support agent and a stronger model for a code review agent. The gateway applies the model list per team, per agent, and per project.
The gateway controls keys. Application code sends a gateway key. The gateway adds the provider key on the way out. How to keep LLM API keys out of application code shows the before and after pattern.
The gateway controls spend. The gateway counts input tokens and output tokens on each call. The gateway can stop calls when a team or an agent reaches a token limit. LLM cost chargeback shows how token counts become a chargeback report.
The gateway controls tool calls. MCP calls leave the cluster and reach systems that read data and run actions. MCP egress control covers allow lists and audit for tool calls.
Other vendors use the term AI egress gateway as well. Solo.io describes kgateway as an AI egress gateway between agents and LLMs. NetScaler documents a Kubernetes AI gateway for egress to external providers. The term names the same position in the network: a gateway on the path out of the cluster, with controls built for AI traffic. The AI gateway glossary defines AI egress gateway and related terms.
AI Egress Gateways vs. Network Egress Gateways
| Control | Network egress gateway | AI egress gateway |
|---|---|---|
| Destination | Host, IP address, and port | Model provider, model name, and MCP server |
| Identity | Workload and namespace | Team, agent, project, and gateway key |
| Provider keys | No role | Adds provider keys on the way out |
| Tokens | No token count | Counts input tokens and output tokens per call |
| Cost report | Bytes and connections | Tokens and cost per team and agent |
| Tool calls | Sees a connection to a server | Sees the MCP server and the tool name |
A network egress gateway answers a network question: may this workload open a connection to this host? An AI egress gateway answers an AI question: may this agent call this model, spend this many tokens, and reach this tool?
Two limits. An AI egress gateway governs calls that pass through the gateway. An agent with a direct provider key and a direct network path can bypass the gateway until you remove the key and close the path. The gateway also depends on the identity on each call. One shared gateway key gives you one shared identity in every report.
When You Need an AI Egress Gateway
You need an AI egress gateway when more than one team calls model providers from your cluster. Shared provider keys hide team spend. Separate provider keys per team create key sprawl and hard rotation work.
You need an AI egress gateway when agents call tools outside the cluster. A tool call can read customer data or change a record. Your security team needs an allow list, authentication, and an audit record for each call.
You need an AI egress gateway when finance asks for a split of the provider invoice. Provider invoices name the account and the model. Provider invoices do not name your teams or your agents. The gateway records the team and agent on each call, so finance gets a split from gateway records.
You need an AI egress gateway when a provider outage can stop a product feature. The gateway can route calls to a backup model or a backup provider under policy your team sets. The AI gateway performance benchmarks publish gateway overhead numbers and the test method.
How to Run an AI Egress Gateway on Kubernetes
Run the gateway as a data plane in your cluster. Route outbound model and MCP traffic from agent workloads to the gateway. Keep inbound traffic on your ingress gateway, and read what is an AI ingress gateway? for the inbound side.
Give each team a project and each agent a gateway key. Add provider credentials to the gateway catalog. Set the model list and token limits per project. Block direct outbound access from agent namespaces with NetworkPolicy, so the gateway path is the available path. Kubernetes egress for AI agents compares the NetworkPolicy option and the gateway option in detail.
Agent Router Enterprise runs the data plane in your cluster and a management plane run by Tetrate. Your team sets projects, keys, model access, budgets, and MCP policy in one console. AI gateway vs. API gateway explains how the AI controls differ from web API controls. Who created Envoy AI Gateway covers the open source data plane project.
Agent Router Enterprise
Tetrate Agent Router Enterprise routes AI agent traffic across providers and your own models, with policy, cost controls, and audit on every request — in cloud, on-prem, or edge.
Teams in an active evaluation can compare options in best enterprise AI gateway.
Availability and How to Get Started
Tetrate Agent Router Enterprise is available now as an AI egress gateway for model providers and MCP servers. The docs show how to provision models and providers with stored provider credentials and how to split the AI bill across teams. Book a demo to run the gateway on your own agent traffic.
Frequently Asked Questions
What Is an AI Egress Gateway?
An AI egress gateway is the control point for calls leaving your cluster for model providers and MCP servers. The gateway decides which destinations your agents can reach, adds provider keys on the way out, and counts tokens per team.
How Does an AI Egress Gateway Differ From a Network Egress Gateway?
A network egress gateway controls connections by host, IP address, and port. An AI egress gateway controls calls by model, token, tool, team, and agent, and the AI egress gateway adds provider keys on the way out.
Does an AI Egress Gateway Store Provider Keys?
Yes. The gateway stores provider credentials in the gateway catalog. Application code sends a gateway key, and the gateway adds the provider key on the outbound call.
Can an AI Egress Gateway Stop a Looping Agent?
Yes. The gateway counts tokens on each call and can reject calls after a team or an agent reaches a token limit. Rate limits can also slow a workload that calls a provider too fast.
Do Other Vendors Sell AI Egress Gateways?
Yes. Solo.io and NetScaler use the term for gateways between agents and external model providers. Vendors differ in data plane, management model, and token and MCP controls.
Does an AI Egress Gateway Replace NetworkPolicy?
No. NetworkPolicy closes direct outbound paths from agent namespaces. The AI egress gateway governs the calls that leave through the approved path.
Sources
- Provision models via API, Tetrate docs
- Govern MCP server access, Tetrate docs
- kgateway guardrails, kagent blog
- NetScaler AI Gateway docs
Related: AI ingress vs. AI egress, on the two directions of AI traffic and one control point for both directions.
