Topic starter
02/09/2026 3:57 pm
TL;DR: A five-minute, 5,000-connection burst test showed a gateway held 0.83-second p99 response times and 14,331 sustained requests per second, according to Highflame, while interpreted gateways fell into multi-second latency and request loss. For practitioners, the finding is that peak concurrency, not average throughput, determines whether inline policy inspection stays usable on the hot path.
Editorial analysis by NHI Mgmt Group, based on content published by Highflame: “AI Gateway Load Testing: What Breaks at Concurrency (and How to Fix It)”.
By the numbers:
- LiteLLM took 17.2 seconds at p99 while holding 5,000 connections open at once.
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.
Q: Why does an AI gateway become a security issue when latency rises under load?
A: Because slow enforcement stops being effective enforcement.
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.
Practitioner guidance
- 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.
Bottom line: Burst concurrency, not average traffic, is what determines whether an AI gateway remains usable as a governance control.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
This topic was modified 2 days ago by NHI Mgmt Group
02/09/2026 3:58 pm
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.
A few things that frame the scale:
- 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.
A question worth separating out:
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.
👉 Read our full editorial: AI gateway load testing shows concurrency, not average load, is decisive
This post was modified 2 days ago by NHI Mgmt Group