AI gateways and MCP tool calls: what latency changes for agents

At a glance

What this is: This benchmark compares AI gateway performance for streamed model traffic and MCP tool calls, showing that the right gateway adds little user-visible delay while the wrong one can create severe wait times under concurrency.

Why it matters: IAM, NHI, and agent governance teams need to treat the gateway as an identity enforcement point because it now sits in front of model requests and tool access, where latency, policy, and control effectiveness intersect.

By the numbers:

• Highflame adds about 2 ms to the first token, and at a hundred simultaneous chats the first token through Highflame lands in 307 ms.
• Highflame adds 17 ms at ten concurrent sessions, and at a hundred sessions it adds 181 ms while delivering 260 calls a second with zero failures.

👉 Read Highflame's benchmark of AI gateway latency and MCP tool calls

Context

AI gateway performance is now an identity and access issue, not just an infrastructure one. A gateway sits between applications and the model providers and MCP servers they call, so it becomes the control point where API keys, traffic inspection, spend metering, and tool access all converge. In that position, even small latency penalties become part of the user experience and part of the operating model for autonomous and semi-autonomous workloads.

The article’s core finding is that gateway design changes the felt cost of model and tool access more than most teams expect. Some gateways preserve the streaming experience and stay effectively invisible under concurrency, while others introduce buffering or per-token overhead that becomes obvious only when demand rises. For practitioners running model-backed applications or agent fleets, the question is no longer whether to centralise policy, but whether the gateway can do it without becoming the bottleneck.

For NHI and AI agent programmes, this matters because the gateway increasingly mediates non-human credentials, tool permissions, and downstream service calls in one place. If the control layer adds too much delay or behaves inconsistently under fan-in, teams will either weaken policy or route around the gateway, which defeats the point of putting governance in front of the traffic in the first place.

Key questions

Q: How should security teams evaluate an enterprise MCP gateway for safe AI agent access?

A: Start with control before convenience. The gateway should sit in front of MCP servers, enforce authentication and role-based access, inspect tool calls, and log activity for audit and response. Security teams should also check credential isolation, schema validation, and whether the gateway can block unsafe or unapproved actions before they reach underlying systems.

Q: Why do AI gateways become a governance problem as concurrency increases?

A: Because the gateway stops being a simple router and becomes part of the access-control experience. When per-token work, buffering, or connection overhead grows with fan-in, users feel the delay and operators feel pressure to weaken policy. That is a governance failure mode, not just a performance issue.

Q: What breaks when an AI gateway buffers streamed responses?

A: The user experience breaks first, because the model may already be generating tokens while the gateway withholds them. In practice, buffering turns a responsive system into one that appears frozen, which obscures whether the delay came from the model, the gateway, or the downstream tool path.

Q: What should teams do when the gateway becomes the choke point for model and tool access?

A: They should separate model-path testing from MCP-path testing, define latency and throughput targets for both, and confirm the gateway can enforce policy without forcing shadow bypasses. If the control plane cannot meet those targets, the design needs rework before agents scale further.

Technical breakdown

Why AI gateway placement changes first-token latency

An AI gateway sits on the request path before the model and before any MCP tools the application calls. That means its overhead is added to the first-token experience, which users perceive as either an immediate response or a stalled conversation. In streamed inference, the gateway should forward tokens as they arrive rather than buffering them. If it does not, the model may already have generated output while the user still sees an empty bubble. The technical distinction is between pass-through forwarding and response aggregation, which become materially different under load.

Practical implication: validate first-token behaviour with streamed requests, not just total response time.

How concurrency turns per-token work into user-visible delay

When a gateway performs work on every token, small costs compound across concurrent sessions. A design that looks acceptable at ten chats can degrade sharply at a hundred because queueing, serialisation, and per-request bookkeeping stack up. This is especially visible when the model itself is relatively fast, because the gateway becomes the main variable in latency. In practice, the performance problem is not raw model speed but the gateway’s ability to keep pace while preserving stream order, throughput, and connection stability.

Practical implication: test gateway behaviour at the concurrency levels you expect in production, not at demo scale.

MCP tool calls require a different control path than model prompts

MCP tool calls introduce a second identity and policy plane because the gateway is no longer only brokering prompts to a model. It is also mediating calls to external tools, which changes where authentication, policy enforcement, and connection pooling sit. A transparent MCP path should authenticate the caller, apply policy checks, and reuse downstream connections without forcing each session to create a fresh tool connection. That separation matters because tool fan-in can overwhelm a direct server even when the gateway itself remains efficient.

Practical implication: measure MCP path overhead separately from model traffic and confirm that the gateway preserves tool-call throughput.

NHI Mgmt Group analysis

AI gateway latency is now part of identity governance. Once a gateway sits in front of both model calls and MCP tool access, it becomes the place where access policy, authentication, and user experience meet. That means performance is no longer separate from governance. If the gateway cannot preserve both enforcement and responsiveness, practitioners will treat it as a bypass candidate rather than a control point.

MCP creates a broader non-human access surface than model-only routing. Tool calls are not just another API hop because they move the gateway from content mediation into delegated action mediation. That shifts the problem from prompt filtering to permissioned execution, which is a stronger NHI governance challenge. The implication is that teams must judge gateways by whether they can govern tool access without adding operational drag that causes shadow paths to appear.

Identity blast radius at the gateway: the control point now concentrates API keys, tool permissions, and spend metering in one enforcement layer. That consolidation is useful, but it also makes the gateway a high-consequence dependency for both human and non-human workflows. The practitioner lesson is not to centralise less, but to understand that centralisation changes failure modes from scattered risk to concentrated bottlenecks.

Policy that cannot survive peak load is not governance, it is a lab condition. The measurements here show that some gateway designs remain effectively invisible under concurrency while others collapse only when demand scales. That is exactly why NHI and agentic AI programmes need operational testing, not policy statements. The field should stop treating identity enforcement as a static configuration problem and start treating it as a runtime capacity problem as well.

Gateway architecture is becoming a prerequisite for safe agent scaling. If AI agents and MCP clients are going to share a common access layer, the gateway has to support least-privilege controls, tool-scoped policy, and stable throughput at the same time. That requirement is already reshaping how teams think about workload identity, delegated access, and runtime authorisation. The practitioners who align those layers early will avoid rebuilding governance after the first scale event.

From our research:

• Only 18% of MCP server deployments implement any form of access scoping for tool permissions, according to Astrix Security's The State of MCP Server Security 2025.
• Only 53% of MCP servers expose credentials through hard-coded values in configuration files, which shows how often the control surface begins with secret handling rather than policy design.
• For deeper context, Guide to the Secret Sprawl Challenge shows how sprawl and poor lifecycle discipline make runtime controls harder to sustain.

What this signals

Access scoping for tool permissions is becoming the real dividing line for AI gateways. If the gateway can centralise model and MCP traffic but cannot enforce granular tool boundaries, the organisation is consolidating risk rather than reducing it. That makes the gateway a workload identity control point as much as an API routing layer, and it should be designed against that expectation from day one.

Secret handling and runtime policy now fail together. The same environments that struggle with hard-coded values in MCP configurations often struggle to hold the line on delegated tool permissions once demand increases. That is why secret hygiene, access scoping, and runtime enforcement have to be treated as one governance problem, not three separate projects.

As agent fleets expand, teams should expect the question to move from whether to use a gateway to which requests it can safely mediate without introducing route-around behaviour. The operational signal to watch is simple: if users or developers begin treating the gateway as optional under load, the governance model is already losing authority.

For practitioners

• Measure first-token overhead under real streaming load Run benchmark tests that separate model latency from gateway-added latency, then verify the first token still arrives within your acceptable user experience threshold. Include concurrency levels that reflect production fan-in, not only small test cases.
• Test MCP tool-call paths independently Validate the gateway’s behaviour for MCP sessions separately from model prompt traffic, including authentication, policy enforcement, and downstream connection reuse. Confirm that tool calls do not inherit hidden buffering or queueing behaviour from the model path.
• Treat gateway throughput as an identity control constraint Set minimum performance requirements for policy enforcement layers so teams cannot route around them when load increases. If the gateway becomes a bottleneck, users and developers will look for bypasses that erode access governance.
• Benchmark memory footprint before centralising traffic Compare the gateway’s resident memory and connection handling against expected peak sessions. If the footprint prevents sidecar or per-sandbox deployment, you may be forced into a central choke point that is harder to govern and scale.

Key takeaways

• AI gateway design now affects both user experience and identity control because it sits directly in the path of model and MCP access.
• Latency, buffering, and per-token overhead become governance issues once concurrency rises, because users respond to delay by bypassing controls.
• Teams should benchmark model-path and MCP-path enforcement separately so policy, throughput, and streaming behaviour are all validated before rollout.

Key terms

• AI Gateway: A control point that sits between AI applications and the models, tools, or data they call. In practice, it can authenticate requests, enforce policy, inspect runtime behaviour, and stop unsafe actions before they spread into connected systems.
• Model Context Protocol: Model Context Protocol is an open protocol that lets AI agents connect to tools and data sources. It expands what an agent can reach, so governance has to cover not only the model and its prompts, but also every system that can receive or return agent-driven data.
• Streaming Latency: Streaming latency is the delay users experience before the first token or response fragment appears. In AI systems, it is often the clearest indicator of whether a gateway is forwarding traffic efficiently or buffering, queueing, or serialising work in ways that the user can immediately feel.
• Tool Permission Scoping: The practice of limiting what a non-human identity can do with each tool, including which data sources it can reach and which actions it can trigger. Strong scoping is a core control for agent governance because it reduces overreach, improves auditability, and supports revocation.

What's in the full report

Highflame's full article covers the operational detail this post intentionally leaves for the source:

• Exact benchmark methodology for streamed first-token timing across 10, 50, and 100 concurrent chats
• Per-gateway memory and throughput behaviour during five-minute peak-hour simulations
• MCP tool-call measurement details, including session fan-in and downstream connection reuse
• Runtime notes on the compiled gateway setup and load-rig configuration

👉 Highflame's full article covers the measurement setup, the per-load charts, and the MCP ceiling details.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM or identity governance programme, it is worth exploring.