AI gateway load testing shows concurrency, not average load, is decisive

At a glance

What this is: This is a benchmark analysis showing that AI gateway usability depends on burst concurrency, with the tested gateway maintaining sub-second latency under a 5,000-connection surge.

Why it matters: This matters because identity, policy, and access checks on LLM and MCP traffic have to survive peak load, or teams will bypass the gateway when it slows tool use.

By the numbers:

• A five-minute test with 5,000 simultaneous connections held 0.83-second p99 latency through Highflame.
• LiteLLM took 17.2 seconds at p99 while holding 5,000 connections open at once.

Context

AI gateway performance is a concurrency problem before it is a throughput problem. In practice, teams do not experience steady request rates, they experience short bursts when many developers, CI jobs, or agent workflows call the same control point at once.

For identity and governance teams, that means the gateway is part of the trust path for LLM and MCP traffic, not just a traffic relay. If policy inspection adds visible delay during spikes, users route around it or disable it, which turns a control into a bottleneck.

The article’s benchmark is therefore about operational viability, not just speed. The tested gateway’s ability to hold latency flat under burst load is the difference between a control that stays in line and one that quietly gets bypassed.

Key questions

Q: How should security teams test an AI gateway before putting it in front of agents and developers?

A: Test it under peak concurrency, not only at average load. Use bursty runs that keep many sessions open at once, then measure p99 latency, request completion, and queue behaviour. The gateway is only trustworthy if policy checks remain fast enough that users do not bypass them when traffic spikes.

Q: Why does an AI gateway become a security issue when latency rises under load?

A: Because slow enforcement stops being effective enforcement. If policy inspection adds seconds during a burst, users and agents experience the gateway as friction and may reroute around it, which weakens governance at the exact moment risk is highest.

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: What should teams do when an AI gateway is too slow for morning-rush usage?

A: Prioritise an architecture that keeps many requests in flight at once and keeps policy inspection on the hot path. If the design cannot preserve low tail latency during fan-out, it should not be the default enforcement point for production AI traffic.

Technical breakdown

Why concurrency matters more than average load

Average requests per second hides the behaviour that users actually feel. AI gateways sit on the critical path for tool calls, model requests, and policy checks, so the real stress comes when many sessions arrive together and work piles up faster than the runtime can schedule it. A design that looks fine at low steady load can still collapse when connections stack up, because queue growth, lock contention, or slow request handling multiplies tail latency long before the average shifts. In this benchmark, the difference appears in the p99, not the mean.

Practical implication: Profile burst concurrency, not just average throughput, before placing an AI gateway in front of production traffic.

Hot-path policy inspection under burst load

Inline policy inspection means the gateway evaluates a request before forwarding it to a model or tool. That is a governance control only if it stays fast enough to remain on the hot path. Once inspection adds seconds instead of milliseconds, users experience it as friction, not protection, and operational pressure increases the chance that teams will move traffic around the control. The article shows that the inspection cost itself is not the problem. The problem is whether the gateway can absorb simultaneous demand without turning inspection into a queueing problem.

Practical implication: Treat policy enforcement latency as a control-quality metric, not just a performance metric.

Concurrent runtimes and tail latency

The architectural divide here is between runtimes that can keep many requests active at once and runtimes that stall when one request blocks progress. Compiled, concurrent designs such as Rust async or Go goroutines can preserve responsiveness because they do not force all work through a single execution lane. Interpreted or effectively serialised designs are more vulnerable to tail blowouts under fan-out, especially when one slow path holds others behind it. The result is not merely slower service, but uneven trust enforcement under load.

Practical implication: Favour gateway architectures that preserve parallel progress when multiple agents, developers, or jobs call at once.

NHI Mgmt Group analysis

Concurrency, not average throughput, is the real governance threshold for AI gateways. A gateway that keeps up during quiet periods but fails under simultaneous arrivals is not a dependable enforcement point. Identity and policy checks for LLM and MCP traffic only matter if they stay on the hot path when everyone lands together. Practitioners should treat burst capacity as a control requirement, not a comfort metric.

Tail latency is the signal that governance is starting to fail. When p99 response times jump from sub-second to multi-second, the gateway is no longer invisible and users begin to route around it. That is especially relevant where the gateway sits between developers or agents and privileged tools, because delay invites bypass behaviour. The control problem is not only performance, but user trust in enforcement.

AI gateway architecture is now part of identity governance for agentic workflows. Tool calls, model access, and policy inspection are increasingly the same path, which means the gateway is acting as a real-time identity and authorisation checkpoint for non-human activity. That makes runtime design a governance decision, not just an infrastructure choice. Teams need controls that preserve enforcement under fan-out, not only under steady state.

Burst resilience should become a named design property in AI security programmes. The article points to a useful concept: burst-safe enforcement, meaning policy controls that remain effective when concurrency spikes. That is the operational boundary where many AI security assumptions break, especially for shared developer platforms and agent workflows. Practitioners should require evidence of burst-safe enforcement before trusting any gateway to mediate production AI use.

From our research library:

• Claude Code-assisted commits leaked secrets at a rate of 3.2%, more than double the human-only baseline of 1.5%, with peaks reaching 31 secrets per 1,000 commits in August 2025, according to the State of Secrets Sprawl 2026.
• Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to the State of Secrets in AppSec.
• Read next: AI Security Platform Buyer's Guide

What this signals

Burst-safe enforcement: AI gateways now need to prove that policy checks stay effective during the few seconds when many users, jobs, or agents arrive together. A design that only works at average load creates a governance blind spot right where identity and authorisation controls matter most.

Tail latency should be tracked as a security control signal, not just an engineering metric. When enforcement becomes slow enough that users notice, the programme has already crossed from governance into workaround territory.

Teams running shared developer platforms and agent workflows should treat concurrency testing as part of AI access control validation, because the control path and the user path are now the same path.

For practitioners

• Measure peak-concurrency behaviour, not just averages Run load tests that hold many simultaneous connections open long enough to expose queue growth, p99 drift, and request loss under realistic rush conditions.
• Test policy inspection on the hot path Verify that inline checks remain fast enough that developers and agents do not perceive the gateway as the reason their tool calls slow down.
• Validate gateway behaviour under fan-out Simulate CI bursts and chained agent workflows so you can see whether one execution lane, blocking call, or queue threatens the whole control point.
• Set latency thresholds for governance controls Define an acceptable p99 ceiling for gateway-mediated policy decisions and fail any design that turns enforcement into a multi-second wait.

Key takeaways

• Burst concurrency, not average traffic, is what determines whether an AI gateway remains usable as a governance control.
• A gateway that looks healthy at steady state can still become a bottleneck when many developers, jobs, or agents arrive at once.
• Practitioners should validate p99 latency, request completion, and hot-path inspection under peak fan-out before trusting the control in production.

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.
• Tail Latency: Tail latency is the slow end of a system's performance distribution, usually measured with metrics such as P95 or P99. Security teams care about it because the outliers are often the scans, checks, or reviews that block production workflows and erode trust in the control.
• Concurrency: Concurrency is the number of requests or sessions a system can keep active at the same time. In gateway design, it exposes whether the runtime can manage overlapping work without queue collapse, lock contention, or a sharp rise in user-visible delay.
• Hot Path: A hot path is the part of a system that executes so frequently or so critically that small inefficiencies become visible to users or operational teams. In security tooling, hot paths often include scanning, matching, filtering, and policy checks that must stay fast to remain effective.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, agentic AI identity, and secrets management. It helps practitioners connect identity control to real operational decisions across modern AI programmes.