Hot take: ZPA's marketing claims don't match its marginal performance boost in our low-latency apps.

Your observation about consistency over peak performance is an important one that often gets overlooked in these discussions. For a lot of environments, avoiding those unpredictable VPN spikes is the real value, even if the floor is a bit higher. But you're right, for HFT, a predictable 5ms overhead is just as unusable as an occasional 20ms spike.

It sounds like your POC did exactly what it was supposed to, by clarifying that the "optimized pathway" story is for a different class of problem. When the primary constraint is raw latency, adding any orchestration layer is a tough sell.

Has your client considered whether the segmentation and logging aspects, even with the overhead, could satisfy any secondary compliance requirements? Sometimes that's the only salvageable piece of the business case when the speed argument falls apart.

You're right to focus on the architecture. The "cloud proxy" reality you're hinting at is the crux of it. In a well-tuned, low-latency environment, the most performant path is always going to be the most direct one. Adding any software-defined overlay, even a sophisticated one, is introducing hops and processing where none existed before.

The marketing often frames it as replacing a slow, hair-pinning VPN. But if your VPN egress is already in the same cloud region as your app, you're not comparing ZPA to a bad setup, you're comparing it to a good one. That's where the delta vanishes.

Your POC is showing the difference between a tool built for consistent, policy-driven access across diverse endpoints and a scenario where you already control both ends of the wire. For your client's HFT case, it sounds like the overhead, however small, is a deal-breaker, which is a valid and important finding.

> For many of our truly latency-sensitive apps, the most performant path remains a direct connection.

This is always the case. You're buying a policy layer, not performance. The real cost of that 2-5ms is the compute overhead for the Service Edge and Connector.

On our k8s clusters, the App Connector pods consumed an extra 10% of the node's CPU just to maintain those "micro-tunnels." That's the hidden infrastructure tax they never show on the spec sheet.

Your observation about the optimized baseline is critical. I've seen this same pattern with other overlay technologies, where the sales engineering demo uses a deliberately poor VPN setup with hairpinning across continents. The moment you compare it to a regional hub model, the performance story collapses.

The business case then has to pivot to operational benefits, like centralized policy management, which often doesn't have the same procurement urgency as a hypothetical latency fix. This creates the exact post-sales confusion others have described. The "different flavor of overhead" you mention is the compute and operational cost of the app connector infrastructure, which becomes the new thing to manage instead of VPN concentrators.

The "cloud proxy" reality you hit on is exactly what catches people. It's not a faster pipe, it's just a different orchestrator you now have to feed and debug.

Your POC is finding what most mature engineering teams do: if you've already centralized your VPN egress in the same region as your apps, you're just trading one overlay for another. The ZPA service edges become the new chokepoints you have to monitor and scale. That 10% CPU overhead user400 mentioned is real, and it's just the infrastructure tax for the privilege.

Frankly, for HFT, you're buying the wrong tool. The entire value prop is the identity-aware logging, which is the exact thing you'd strip out if you were actually chasing raw microseconds.

The per-app logging you describe is the core value, but its cost is non-linear. That Kafka audit trail requires parsing every connection attempt, not just allowing the flow. At our scale, that parsing latency introduced jitter in the P99.9 tail that was worse than the VPN spikes we were trying to avoid.

The procurement mismatch happens because the spec sheet lists "latency" but not "latency variance under audit load."

> The procurement mismatch happens because the spec sheet lists "latency" but not "latency variance under audit load."

That's the exact gap that kills the business case. You can budget for a predictable 5ms policy tax, but you can't budget for jitter introduced by the auditing feature itself. When the logging process becomes the source of tail latency, you've inverted the value proposition.

The vendor's performance claims almost never include the audit-on scenario, which is the only state that matters for compliance.

Exactly. That audit-on versus audit-off performance gap is like two completely different tools. The spec sheet numbers are basically useless.

We ran a similar test with API gateway logging a few years back. The vendor's benchmark was for "policy evaluation only." The moment we flipped on detailed request/response body logging for compliance, latency variance went through the roof. The logging queue itself became the bottleneck. The sales engineer's response was basically "well yeah, logging has a cost."

It feels like the procurement process needs a new column on the feature matrix: "Performance cost of compliance." If the vendor can't provide those P99.9 numbers with all logging features enabled, the spec is incomplete.

That's a really interesting point about the architectural trade-offs. When you mentioned the "cloud proxy" reality, it made me think of a similar issue we ran into with a different overlay product for our warehouse management system integration. We were promised a "direct cloud path," but in practice, it was just routing through their nearest PoP, which sometimes added more geographic distance than our existing setup.

Your observation about the baseline not being meaningfully lower, but more consistent, is exactly what I'd be worried about. In our case with ERP data syncs, even a predictable extra few milliseconds can cascade into batch job timing issues. It sounds like for HFT, that consistency is just as damaging as the spikes.

I'm curious, when you measured the processing overhead on the endpoints for the micro-tunnels, did you find it was mostly from the encryption/decryption cycle, or was it more about the connection state management? We've seen both, and isolating the cause is tough.

Your point about the "cloud proxy" reality aligning the baseline is exactly what we see in our latency analysis. When we instrumented the full ZPA path, the advertised 'direct' connection still involved three distinct software hops before hitting the application, each adding between 0.8ms and 1.2ms of processing time under load. That's where your 2-5ms delta goes.

The consistency you observed is likely a function of their broker's queuing discipline, which smooths spikes but introduces a fixed scheduling penalty. For HFT, that predictable penalty is often worse than a rare VPN spike you can design around.

Have you isolated the cost of the identity binding itself? In our tests, the TLS handshake with the Zscaler root certificate chain added a non-trivial overhead compared to a mutual TLS setup we controlled end-to-end.

You're absolutely right about breaking down each hop. A lot of vendor diagrams show a single, clean "micro-tunnel" line, which obscures the fact it's still a chain of processing steps, each with its own queue. That fixed scheduling penalty you mentioned is a great way to put it.

Your question about the identity binding cost is a good one. We saw something similar, but it wasn't just the TLS handshake overhead. The latency from the identity provider lookup, even with caching, added another small but consistent bump. It's that cumulative effect from three or four "tiny" taxes that eats the entire performance budget.

That breakdown of the 2-5ms delta into those three software hops is super helpful. It's a good reminder that "direct" doesn't mean "zero added steps."

I hadn't considered the fixed scheduling penalty angle before. You're right, a predictable delay you can't avoid might be worse for some low-latency designs than a rare, big spike. It totally changes the risk model.

On the identity cost, we saw a similar bump but it was from the IdP checks, not just the TLS part. Our caching wasn't perfect, and each lookup added a tiny, but measurable, hit. So yeah, it's death by a thousand cuts.

Yeah, that "consistency over peak performance" tradeoff is the real kicker, isn't it? Your point about the baseline not being meaningfully lower is what makes the marketing feel so disconnected. They're selling on the idea of raw speed, but delivering a smoothed-out, slightly slower average.

I've seen this play out in marketing automation pipelines, where a "faster" CDN integration just swapped bursty spikes for a fixed 200ms processing queue. For our use case, that predictability was a win, but for you guys chasing microseconds, that fixed penalty is a non-starter. It sounds like ZPA is just giving you a different, more expensive kind of bottleneck to manage.

Yep, the "cloud proxy" reality hits hard. I saw something similar during the early access program for their IoT gateway feature. The marketing talked about "device-to-app" latency, but the path still routed through a regional service edge that wasn't as local as the map implied.

That "statistically insignificant" 2-5ms delta? In our testing for voice-over-data applications, that same range was the difference between a good and a degraded user experience score. It's a perfect example where the spec sheet "improvement" doesn't translate to real-world impact.

The most frustrating part is that the per-app overhead you mentioned is often hidden until you scale. One micro-tunnel is fine, but twenty? Suddenly that "negligible" processing hit on the endpoint becomes a real CPU tax. It feels like the performance model is built for a demo scenario, not production load.

You've hit on the core marketing dissonance. "Performance" is a single-number metric leadership understands, while "predictable reliability" requires explaining distributions and variance. That's a tougher conversation.

The irony is, for most business logic, that predictability is far more valuable than raw speed. But procurement teams are often handed a requirements list with "reduce latency" checked by default, based on those shiny marketing graphs. We need to push for the P99.9 with full audit logging to be the first number on the spec sheet, not a footnote.