Yep, that gap is real. We had the same shock - our shiny Grafana dashboards showing 30ms pings, while users in APM were seeing 90ms+ on API calls.
The fix was embarrassingly simple. We built a tiny canary service that mimics a real user session, opening a keep-alive connection and sending a trickle of data. Deployed it alongside the ICMP monitors. The difference on day one was laughable. ICMP took the cheap path, the canary got the optimized lane within seconds.
Now I don't trust any backbone latency number that doesn't come from a traffic clone. Synthetic checks are just noise if they don't bleed like real traffic.
You're right to ask for specifics. Hop count alone tells you nothing. I've seen paths with fewer hops but crossing three congested peering points, while a longer AS-stable route was consistently faster.
For testing, you need to match your real traffic profile. If your app sends 1500-byte packets every 10ms, your test should too. A 64-byte ping on a five-second timer is just checking if the lights are on.
—AF
Exactly. You've identified the core measurement problem. Your data shows a plateau before and after the failover, but never captures the cliff in between.
For a business app, a few lost packets during a switch absolutely matter if they're part of a stateful transaction. A user hitting submit on a form, a payment callback, an API call writing to a database - these aren't resilient to micro-outages. The point is supposed to be seamless transitions, but the only way to verify that is to measure with something that looks like real traffic, not periodic pings.
We ran a test where we flooded a TCP connection with small, sequential messages during a forced failover. The ICMP monitors saw nothing. The TCP stream showed 400ms of stalled data and three retransmitted packets. That's a timeout for a lot of frontend logic.
Show me the query.
Those micro-events you logged are the whole story. The smoothed graphs aren't just missing them, they're actively designed to hide them. The vendor's dashboard smooths because their SLA probably averages over 5 minutes.
But your real app doesn't experience averages. It experiences that exact packet loss on a payment confirmation. The "improved average" is a marketing metric, not an engineering one.
Just saying.
That 30% gap you saw? It's probably a best case scenario. The real problem is when your traffic doesn't look like the "golden path" they optimize for.
We tagged a set of low-volume, long-polling service connections and found they never triggered the premium routing logic at all. They got stuck on the standard path indefinitely because they didn't fit the expected traffic profile. The synthetic probes, being short bursts, got the fast lane every time. So your gap could be zero for your tests and massive for your actual workload.
The architecture change you mention is the real cost here. You end up redesigning apps to generate the "right" kind of traffic instead of the backbone adapting to your needs.
prove it to me
Thanks for laying out your methodology so clearly. That 72-hour baseline is smart, it's something I need to do.
When you say "regular traceroute and mtr runs to compare hop count", did you see any correlation between fewer hops and lower latency on Cato? Or was it more about the stability of those hops being more important?
Your methodology is sound, but I'd question the utility of hop count as a primary metric. On a private backbone, a stable 4-hop path can be worse than a dynamic 6-hop one if those extra hops avoid a congested core link.
The correlation we observed was weak. More important was identifying which specific intermediate PoPs introduced jitter. Our mtr data showed that latency variance spiked predictably when traffic traversed certain regional aggregation points, regardless of total hop count. Have you broken down your latency distributions by the ingress/egress PoP pair to see if that pattern holds?
Data is the only truth.