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AI Gateway vs Runtime Assurance

One manages model traffic. The other governs agent behavior. Buyers keep conflating them. Here's the actual difference.

In short

An AI gateway manages LLM API traffic: routing, key custody, rate limits, cost, and payload filtering for requests that are routed through it. Runtime assurance governs what AI agents actually do, authorizing every tool call, file access, and API request against enterprise policy before it executes, on the endpoint, regardless of where the traffic points. The gateway governs the traffic it is given; runtime assurance sees agent behavior at the source.

Capability
Certiv
AI Gateways
Primary job
Govern agent behavior: authorize each action before it executes
Manage LLM API traffic: routing, keys, rate limits, cost, caching
Vantage point
The endpoint, where the agent reasons and acts
The network path between application and model API
Sees local models & shadow agents
Yes, endpoint-native discovery and enforcement
No. Traffic that never routes through the gateway is invisible
Sees tool calls, file access, shell commands
Yes, at the point of execution, with the machine context behind them
In the payload only: sees the call to main.py, not what main.py does
Context available for policy decisions
The agent's working environment: files, tools, session history, effects
Payload text alone, without the machine context to infer intent
Enforcement granularity
Per action, with intent and session context
Per API call: filter, rate-limit, or block the request
Model routing, provider failover, response caching
No. Different problem
Yes, its core function
Coverage guarantee
Detects agentic work on the endpoint regardless of where traffic points
Opt-in by configuration: one changed base URL and traffic never arrives

AI gateways solved a real problem: once an organization uses several model providers, someone has to hold the keys, route the requests, cap the spend, and keep a log. That's traffic management, and gateways (and the LLM-proxy pattern generally) do it well.

The confusion starts when the gateway is asked to be a security control for agents. Two things break. The first is coverage: a gateway only governs traffic that is configured to reach it. A new agent pointed at a different base URL, a developer using their own key, one changed environment variable: that traffic never arrives, and the gateway cannot enforce policy on requests it never sees. Guaranteeing that every agent actually routes through the gateway requires a second enforcement layer on the endpoint, which is the control the gateway was supposed to be.

The second is context. For the traffic it does proxy, the gateway reads payload text, and tool calls do appear in that text. It can see the agent invoke main.py; it cannot see what main.py does, what data sits next to it, or the environment the agent is acting in. The agent has that context. The gateway does not, and payload text alone is not enough to infer intent.

Runtime assurance was defined for exactly that gap: an endpoint-native control layer that detects agentic work regardless of where its traffic points, sees the behavior behind the payload (reasoning context, tool calls, data access, and their effects), and authorizes each action against policy before it runs. If the gateway is a tollbooth on the highway to the model, runtime assurance rides in the car: agents that take a different road are still governed. Most enterprises adopting agents at scale will run both.

FAQ

Frequently Asked Questions

Expand to view common questions.

What is the difference between an AI gateway and runtime assurance?
An AI gateway is network infrastructure that manages LLM API traffic: routing requests across models, holding API keys, enforcing rate limits, tracking cost, and filtering prompts and responses in transit. Runtime assurance is a security control layer that governs what AI agents actually do: it authorizes each agent action (tool calls, file access, shell commands, API requests) against enterprise policy before the action executes, on the endpoint where the agent runs. A gateway manages the model traffic it can see; runtime assurance governs agent behavior wherever it happens.
Can an AI gateway secure AI agents?
Only for the traffic that is routed to it, and only at the payload level. A gateway can inspect and block the model calls that pass through it, including the tool calls and file paths that appear in those payloads, and that is real control for sanctioned usage. But it has two structural gaps as an agent security control. First, coverage is opt-in: the gateway only sees traffic explicitly pointed at it, so a new agent, a developer key, or a single changed environment variable quietly takes traffic around it, and something else has to enforce that everything routes through. Second, context: the gateway reads payload text without the machine behind it. It can see an agent invoke main.py; it cannot see what main.py does, what data sits next to it, or the environment the agent is operating in, which is not enough context to infer intent. Runtime assurance closes both gaps from the endpoint: it detects agentic traffic no matter where it points and evaluates each action with the machine context behind it.
Do I need an AI gateway or runtime assurance?
They answer different questions. If your problem is managing sanctioned LLM API usage (which models, whose keys, how much spend) an AI gateway is the right tool. If your problem is what autonomous agents can do on your endpoints and with your data, including agents you didn’t sanction, you need runtime assurance. Enterprises rolling out agents at scale typically end up with the gateway managing model traffic and runtime assurance governing agent behavior.
Can an AI gateway and Certiv run together?
Yes, and they compose cleanly. The gateway remains the control point for API key custody and model routing. Certiv sits on the endpoint and authorizes agent actions pre-execution, covering what the gateway structurally cannot: local models, shadow agents, traffic that was never routed to it, and the execution context behind every tool call. Certiv’s decision records also give the gateway’s traffic logs the missing context of which agent acted and why.

Keep reading

Certiv vs AI Proxies

The network-layer cousin of the gateway question: why proxies miss the point of intent.

What is Runtime Assurance?

The full definition of the category: the endpoint control layer for AI agents.

For security teams

The controls to evaluate when rolling out Claude Code and other AI agents.