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

At a glance

What this is: This is a benchmark analysis of AI gateways in front of model and MCP tool traffic, showing that latency can remain low at first token but rise sharply under load depending on streaming behaviour and gateway design.

Why it matters: It matters because the gateway becomes part of the identity and policy plane for both model access and tool calls, so performance choices now influence control placement for autonomous and semi-autonomous workflows.

By the numbers:

• At a hundred simultaneous chats the first token through Highflame lands in 307 ms, indistinguishable from hitting the model with no gateway at all.
• At a hundred sessions Highflame added 181 ms to an MCP tool call, while delivering 260 calls a second with zero failures.
• A single Highflame instance on four cores peaks at 8,200 tool calls a second and holds around 6,800 with a thousand concurrent agent sessions on it.

Context

An AI gateway is a control point between applications and the model or tool endpoints they call, including MCP servers. In practice it centralises API keys, policy checks, spend metering, and traffic inspection, but it also sits inside the critical path for every request, so its latency becomes part of the user experience and part of the governance model.

For identity and access teams, this is no longer just a routing problem. Once the gateway brokers both model traffic and MCP tool calls, it influences how access is authenticated, where policy is enforced, and whether tool execution remains visible enough for governance and audit. That makes gateway design relevant to NHI control boundaries, not only application performance.

The article compares gateway behaviour under realistic load rather than synthetic instant responses. It shows how buffering, per-token overhead, and connection fan-in change the operational picture once agents, assistants, and tool-calling workflows move into production.

Key questions

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: Why does gateway placement matter for policy and identity enforcement in agentic workflows?

A: Because the gateway sits on the path for both model access and MCP tool calls, it becomes part of the authorization chain for what an agent can ask and what it can execute. That means identity, routing, and inspection decisions now influence the same runtime path, so control placement changes operational behaviour as well as access governance.

Q: What are the signs that an AI gateway cannot handle real burst traffic?

A: Watch for p99 latency spikes, queued requests that never catch up, dropped calls, and large gaps between sustained throughput and burst response time. Those are the operational symptoms that the control plane is overloaded and no longer invisible to users.

Q: How should teams decide whether to centralise model access and MCP tool calls through one gateway?

A: Use one gateway only if it can authenticate, inspect, and route traffic without changing the user-visible timing of model output or tool execution. If the single control point adds buffering, creates queueing at peak load, or complicates auditability, split the boundary or redesign the enforcement path before production rollout.

Technical breakdown

Why streaming latency becomes visible before total latency does

First-token latency is the moment users decide whether an application feels responsive. In a streaming architecture, the model may already have produced a token, but the gateway can still delay delivery if it buffers output or adds work on the hot path. That distinction matters because a low median response time can hide a bad experience at the point users notice most. When concurrency rises, per-token processing and queueing can add delay even if the underlying model remains stable. For identity and policy layers, the lesson is that control insertion is not free just because the work is small in isolation.

Practical implication: Measure first-token behaviour separately from total response time before placing policy or identity controls inline.

How MCP tool calls change the gateway performance profile

MCP tool calls are not the same as model chat turns. They create a second governed path where the gateway may authenticate the caller, apply policy, and forward requests to tools or servers. That means the gateway is now part of both the model interaction and the tool execution chain. Under fan-in, the proxy can become the connection manager and policy decision point at the same time, which makes downstream pooling and forwarding behaviour just as important as raw request handling. In agentic workflows, this is where access control and throughput start to collide.

Practical implication: Treat MCP routing as a governed execution path, not a side channel, and test it at production concurrency.

Why buffering and per-token overhead create different failure modes

A gateway can fail in two distinct ways. It can buffer the stream, which makes the UI appear frozen even though tokens are already flowing. Or it can stream correctly but still add per-token work that compounds under load until latency becomes user-visible. The first is a control-path design flaw. The second is a scaling flaw in the enforcement layer. Both matter for agents because they turn a policy decision point into a performance bottleneck. If the gateway also handles authentication and inspection, the control plane and data plane must be separated carefully enough that policy does not distort delivery.

Practical implication: Check whether latency comes from buffering or per-token work, then place enforcement so it does not stall token delivery.

Breaches seen in the wild

• Hugging Face Spaces breach 2024: Unauthorised access to Hugging Face Spaces may have exposed secrets users stored for AI apps; tokens were revoked and org tokens removed.
• Dropbox Sign breach 2024: A compromised back-end service account gave attackers Dropbox Sign customer data, including API keys, OAuth tokens and MFA information.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Gateway placement is now an identity design choice, not just a performance choice: once a gateway sits between agents and both model providers and MCP servers, it becomes part of the authorization path. That means policy enforcement, API key handling, and routing all share the same latency budget. Practitioners should treat the gateway as a control surface that shapes who or what can act, not merely where traffic passes.

Streaming gateways expose a runtime governance assumption that often goes untested: many control designs assume that enforcement can happen inline without changing the user-visible shape of the interaction. This benchmark shows that assumption only holds when the gateway forwards tokens immediately and avoids buffering or heavy per-token work. The implication is that governance teams need to distinguish between controls that are logically correct and controls that are operationally safe under real load.

Agentic workflows collapse the old separation between access and execution: MCP tool calls move policy decisions closer to the action itself, which makes the gateway an identity checkpoint for both prompts and tool use. That expands the blast radius of a poorly designed control point because it can affect not only authentication, but also the timing and reliability of agent actions. Practitioners should re-evaluate where the policy boundary belongs when tools, models, and agents share one path.

AI gateway latency is becoming a governance signal, not just an SRE metric: if a gateway only looks efficient at low concurrency, it may still be the wrong place to enforce identity, policy, or scanning controls for production agent traffic. The article shows that load reveals the real operational cost of putting governance inline. That makes the performance profile itself part of control validation for identity architects and PAM teams.

Identity blast radius expands when a single endpoint brokers everything: a gateway that centralises model access, MCP calls, and spend policy creates a convenient choke point, but also concentrates failure and policy drift. The operational question is no longer whether the gateway can route requests, but whether it can do so without becoming the hidden dependency that slows every governed action. Practitioners should design for containment, not just centralisation.

From our research library:

• 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
• DeepSeek alone generated 113,000 new exposed API keys in 2025, illustrating how new AI providers create credential exposure before security guardrails catch up, according to the State of Secrets Sprawl 2026.
• Read next: MCP Security Guide

What this signals

Gateway latency is now part of identity governance: if the control point that authenticates model and tool traffic also becomes the place where streaming stalls, then performance and policy are inseparable. Teams should evaluate whether the gateway can enforce inspection without turning delivery timing into a hidden control failure.

MCP changes the governance surface, not just the integration pattern: tool calls carry a second execution path that needs authentication, authorisation, and observability at the same checkpoint as model access. That makes gateway design a question of control architecture, not simple plumbing.

A useful benchmark for programme owners is whether the gateway changes the user experience only by a few milliseconds or whether it starts to alter workflow reliability under real concurrency. Once latency becomes visible, policy enforcement is shaping behaviour as much as the model is.

For practitioners

• Profile first-token latency separately from total response time Test gateway behaviour at the point users notice it, with streaming enabled and realistic concurrency, so buffering or per-token overhead does not hide behind a healthy average.
• Validate MCP tool-call paths as governed execution paths Measure authentication, policy enforcement, and downstream forwarding on the same path that agents will use in production, because tool calls are not equivalent to plain chat turns.
• Stress-test gateway behaviour at production fan-in Run concurrency levels that match planned agent adoption, including thousands of held conversations or tool sessions, so queueing and connection pooling limits appear before rollout.
• Separate control work from delivery work Keep inspection, policy, and metering from blocking token streaming or tool forwarding, especially where the gateway is also acting as the authentication and routing boundary.

Key takeaways

• AI gateways can be operationally invisible at low load and still become governance bottlenecks once streaming, policy checks, and tool calls share the same path.
• The article shows that concurrency exposes the real cost of buffering and per-token processing, especially where MCP tool calls add a second governed execution channel.
• Practitioners should validate gateways as part of the identity and policy plane, not only as a performance layer, because control placement now affects how agents act.

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.
• MCP Tool Call: An MCP Tool Call is a request an AI agent sends through the Model Context Protocol to use an external tool or data source. It carries the action, parameters, and context needed for execution, and it should be governed like a privileged machine-to-machine transaction because it can expose data, trigger actions, or change state.
• First-token latency: The time between sending a request and receiving the first streamed token back to the user. It is the most visible responsiveness signal in chat and agent workflows, and gateway buffering can make it worse even when the model itself is fast.
• Fan-in: Fan-in is an architecture pattern where many downstream connections are consolidated into a smaller set of upstream connections. In fleet management, it reduces server load but requires identity-aware routing so multiple managed entities do not collapse into one operational record.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.