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02/09/2026 3:57 pm
TL;DR: AI gateway overhead can stay near invisible on first-token latency, but poor streaming and per-token processing create large delays under load, especially at 100 concurrent chats and on MCP tool calls, according to Highflame’s benchmark. For identity teams, the lesson is that gateway placement now affects both model access and tool authorization paths, so policy, identity, and performance must be designed together.
NHIMG editorial — based on content published by Highflame: an AI gateway benchmark covering streamed model latency and MCP tool calls
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.
Questions worth separating out
Q: How should security teams evaluate an enterprise MCP gateway for safe AI agent access?
A: Start with control before convenience.
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.
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.
Practitioner guidance
- 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.
- 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.
- 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.
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
👉 Read Highflame's benchmark of AI gateway latency and MCP tool calls →
AI gateway latency under load and MCP tool calls: are your controls ready?
Explore further
02/09/2026 3:58 pm
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.
A few things that frame the scale:
- 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.
A question worth separating out:
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.
👉 Read our full editorial: AI gateways and MCP tool calls: what latency changes for agents
