Alright, let's cut to the chase. We've been running Versa's SSL/TLS inspection in our staging environment for about six months, and while the security team loves the visibility, my team is the one paying the performance tax in latency and compute. I promised I'd share our benchmarks when we had real numbers, so here we are.
We set up a controlled test bed with a pair of their VSPs (Virtual Service Platforms) in an active/active cluster. The workload is a mix of east-west microservice traffic (TLS 1.3) and north-south user traffic. We measured with and without SSL inspection enabled on the same policies.
Here's the raw overhead we observed for a typical API transaction (percentiles from our tracing):
```
P50 Latency Increase: +12ms
P95 Latency Increase: +42ms
P99 Latency Increase: +112ms
Throughput Impact (max sustained RPS): -18%
CPU Load on VSP (additional): +22%
```
The P99 spike is the real killer. It's not just the decryption/re-encryption; it's the session setup and the extra lookups. It turns our "consistent low latency" promise into a bit of a joke for our most sensitive services.
We had to carve out critical payment and real-time sync services from inspection, which of course made the security folks grumble. The Versa support response was basically, "That's expected, scale up or tune ciphers." Not super helpful when you're on a tight cloud budget.
So, my question to the room: has anyone else done similar benchmarking? Did you find tuning knobs that actually move the needle, or is this just the hard cost of full SSL inspection? I'm starting to think a layered approach—inspecting only traffic from known risky segments—might be the only sane way to keep both performance and security happy.
- tm
Those P99 numbers really resonate with our experience. It's the hidden lookup and policy evaluation that gets you, especially with short-lived, bursty connections like you see with microservices. We found the overhead wasn't linear; it scaled horribly with connection churn.
One thing we did that helped a bit was to compile an exclusion list based not just on service, but on specific URL patterns for our internal APIs. It cut down the session inspections dramatically. But yeah, you end up playing whack-a-mole with the security team over what gets a pass.
api first
Thanks for publishing concrete numbers. Your throughput and CPU metrics are particularly useful. We've seen the -18% RPS impact holds true at scale, but the CPU overhead becomes a hard bottleneck when you approach 70% utilization on the VSPs, triggering aggressive flow control.
The >112ms P99 increase suggests the session cache isn't effective for your traffic pattern. Are you using session tickets or PSK resumption? We disabled them in our test to force full handshakes, which exaggerated the overhead, but it exposed a flaw in their cache sharding under load.
Have you broken down the latency between the MITM proxy and the policy engine? In our case, 60% of the added delay was in the L7 rule evaluation, not the cryptography.
benchmark or bust
Your numbers line up with what we see on AWS with similar appliances. That CPU tax is the real cost driver, not the hardware. It forces an overprovision by 30-40% to keep headroom, which blows the TCO.
You're right to exclude payment services, but that creates a coverage gap. The argument is always "exclude the critical path," but then what's the point?
Have you quantified the infra cost delta to support this? The -18% RPS means you need nearly 20% more instances just to handle the same load. That's the conversation starter with security.
Show me the bill
That P99 increase is brutal. Did you track if it's tied to specific microservices with high connection rates? We've seen the overhead concentrate on services that don't keep connections alive for long.
The -18% RPS impact is the number I'd take straight to finance. That's a direct 1:1 scaling cost. What's the actual monthly infra delta for that capacity hit? That's the ROI question for the security team.
Ask me about hidden egress costs.
The connection between high churn and P99 latency is well observed. While the original poster did isolate the worst latency to services with ephemeral connections, the more telling metric for finance is often the aggregate cost of that overhead across all services, not just the outliers.
You're right to focus on the infra delta. In my experience, that 20% instance increase is a starting point, but the operational costs of managing the exclusion lists, tuning the session cache, and handling the inevitable support tickets for 'mystery latency' often match or exceed the raw compute cost over a year. Has your team attempted to quantify those secondary operational expenses alongside the instance count?
Let's keep it constructive
I felt that same tension with our security team when we started building those exclusion lists. It's a tough compromise, isn't it?
How granular did you get with the URL patterns? Did you find it sustainable to maintain as your APIs changed, or did it become a full-time job?
Exactly. That "coverage gap" argument is always trotted out, but it's a red herring. The point isn't full coverage, it's risk reduction. If inspecting the payment service brings it to its knees, you've traded a security risk for an availability risk, which is arguably worse.
Quantifying the infra delta is crucial, but don't stop at the 20% more instances. The real killer is that this overhead applies to *all* your traffic, not just the stuff you actually care about inspecting. So you're paying a 20% tax on your entire data plane just to maybe catch something on a fraction of it. That's where the TCO math falls apart.
— skeptical but fair