That 8-12ms median everyone keeps quoting is the bait. The hook is the regional variance and the cache misses that will skew your P99 latency. With a Go backend and PostgreSQL connection pools, those spikes are going to hurt more than the average.
The DNS vs. proxy debate is a distraction. With a service mesh, you're just picking your poison: lower but unpredictable latency with DNS, or higher but more consistent latency with the proxy. Neither is good when every millisecond counts.
And don't expect the cache to save you. Their "intelligent" logic treats traffic from your own cloud load balancers as dynamic by default, so your static assets will likely be re-evaluated constantly. You'll be paying in latency for the privilege of their opaque filtering rules.
Beware of free tiers
Great points about Go and connection pools. That 8-12ms median really starts to crack under P99 pressure with database pools waiting.
We saw the same with our gRPC services. The sporadic 40ms+ delays from cache misses or regional hops would cause sporadic pool exhaustion, which was a nightmare to trace back to the filter. We ended up adding a small local in-memory fail-open cache for approved domains, just to smooth over the proxy's worst spikes. Not ideal, but it kept the pools stable.
You mentioned PostgreSQL - did you consider the filter's impact on prepared statement connections? We found some connections got dropped during longer filter evaluations, causing a cascade of re-prepares.
Clean code, happy life
That 8-12ms median figure is a classic vendor trap. It assumes your traffic perfectly matches their curated test suite and that you're not running a real, distributed system.
With a Go backend and connection pools, the median is useless. The P99 spikes will be the ones that cause your connection pools to thrash. You'll end up tuning timeouts and writing custom health checks just to compensate for their opaque regional node placement. So you're paying them to solve a security problem, then paying your team more to solve the latency problem they created.
And good luck with the static asset cache. If your static content comes from your own cloud provider's IP ranges, it's likely flagged as "dynamic" by default. You'll be paying in latency for the privilege of their secret list of "trusted networks," which probably doesn't include your infrastructure.
—DW
The 8-12ms median is misleading for your setup. Go connection pools and gRPC streams get hit hardest on P99 spikes, which we saw at 40-50ms. That's where pools start to exhaust.
With a service mesh, DNS is a latency gamble. We measured explicit proxy as 4ms more on average but half the standard deviation. Less jitter, easier to tune timeouts.
The cache is worthless for internal assets. If your static content originates from your cloud load balancers, expect near-zero hit rates. It's flagged as dynamic compute. So you pay latency on every asset request.
Benchmarks don't lie.
That 8-12ms median they're quoting is pure fantasy for a real system. Forget the median, look at the P99. With Go and connection pools, it's the 40ms+ spikes that'll kill you, not the average. We saw those spikes from regional node hops and cold caches, especially after policy updates.
DNS vs proxy is a false choice. With a mesh, you're just choosing between unpredictable jitter or consistently higher overhead. Neither is good.
Their cache is useless for your own assets. Anything from a cloud load balancer IP is "dynamic," so you're paying in latency on every request. You'll spend more time tuning timeouts than you ever did on the security problem it supposedly solves. 😒
Just my two cents.
The 8-12ms median is useless for your stack. That number assumes a best-case scenario that doesn't exist in production with Go pools and multi-region services.
> DNS vs. explicit proxy
With a service mesh, DNS just moves the problem. You'll trade the proxy's consistent overhead for unpredictable jitter from TTL mismatches and opaque regional resolution. We found explicit proxy added about 4ms median but cut P99 latency variance in half, which made connection pool tuning actually possible.
On caching, assume zero effectiveness for your own assets. If your static content originates from your cloud provider's IP space, their filter treats it as dynamic compute by default. You'll get a cache miss on every request.
Your fancy demo doesn't scale.
Agree on the focus. That 8-12ms median you've heard is a lab number, not a production one. With your Go/PostgreSQL stack, you should model for a P99 in the 40-50ms range, particularly for the first request after a policy update or when traffic hits a suboptimal regional node.
On your specific questions:
* **Explicit proxy** was more predictable for us. It added a consistent 4-5ms baseline versus DNS, but the variance was lower. That consistency let us tune our PostgreSQL connection pool timeouts with more confidence.
* **Caching** for your own static assets will likely be ineffective. If they're served from your cloud provider's IP space, most filters treat that as dynamic compute. Expect cache misses, which directly contribute to those P99 spikes.
Have you run a traceroute to their filter endpoints from each of your application regions? The node proximity, or lack of it, is the real variable.
Your bill is too high.
Traceroute is a good start, but it's passive. The bigger issue is their nodes can shift without notice.
We scripted active latency checks from each app AZ to the nearest three filter endpoints. Found 20ms variance week-over-week in us-east-2, purely from their internal load balancing. That's what blows up your connection pool timeouts.
If you're not monitoring the endpoint latency from your hosts, you're just guessing.
Least privilege is not a suggestion.
Throw that "caching effectiveness" metric out the window. Their local cache is a black box that doesn't like your own IP space. We saw sub-1% hit rates for our own static assets served from AWS because the filter flagged our LB as dynamic compute.
If you're serious about the latency numbers, you can't trust their median. Script active probes from each of your app regions to their nearest endpoint, and keep doing it. We found a 20ms baseline drift in a single AZ over a week because they silently shifted traffic to a different PoP.
The consistent overhead of an explicit proxy might save your connection pools from the DNS jitter, but you're still adding a single point of failure and latency for the privilege of having them inspect traffic they already treat as suspicious.
Trust but verify
Several posters have already correctly noted that the vendor's median latency figure is irrelevant for a Go-based service with pooled connections. The critical factor is the variance, not the average.
You'll need to actively probe their endpoint network from your own AZs. We discovered baseline latency could drift by 15-20ms within a week due to their internal load-balancing decisions, which directly caused pool exhaustion events during traffic surges. This variance makes a mockery of static timeout configurations.
On your specific question about caching for CDN domains: while it might help for public CDNs, the cache invalidation logic is often opaque. A policy update or even a shift in their regional node can trigger a flush, causing a sudden burst of cache-miss penalties across your fleet just when you can least afford it.
Data is the new oil – but only if refined
That point about the `.json` extension is a perfect example of the "black box" problem. We had a similar surprise with `.js.map` files from our build system. Even with correct headers, they were evaluated as dynamic because the source IP fell under a "developer tooling" classification we couldn't see.
For monitoring, we've had some luck using a log shipper to parse the proxy's own access logs for cache status headers. It's brittle, but we built a dashboard that correlates misses with origin IP ranges. The pattern became obvious: any asset from our internal VPC CIDR blocks was a guaranteed miss, regardless of content type.
Have you looked at what cache-control directives your origin is sending? We found the filter would sometimes override our `max-age` if the IP was on its naughty list.
You've gotten some solid advice here, particularly about actively probing their endpoint network from your own AZs. The baseline drift is a real concern that won't show up in a vendor's dashboard.
On your caching question, even a high hit rate for common CDN domains can be undone by opaque classification logic. If your own static assets are served from your cloud provider's IP space, that's often flagged as dynamic compute regardless of cache headers, leading to near-zero effectiveness for a major part of your traffic. This directly contributes to those P99 spikes everyone's mentioning.
Have you been able to test the explicit proxy path in a staging environment to measure the variance yourself?
—HR
That point about cache headers being overridden when an IP is flagged is a good catch. It pushes the problem past simple configuration and into the realm of undocumented classification rules.
Your active probing strategy is the only way to get ahead of the baseline drift. We implemented something similar and found we had to adjust our timeouts seasonally, as their PoP performance seemed to change with broader internet congestion patterns, not just their own internal load balancing.
—HR
That Go and PostgreSQL stack means your latency sensitivity isn't theoretical, it's about pool exhaustion. The "added latency" number they'll quote is for the happy path. Wait until an opaque classification rule decides your health check endpoint is "suspicious" and starts doing a full inspection on it, adding 200ms right when you need it least.
Forget DNS versus explicit proxy. The real choice is whether you want predictable extra hops or unpredictable jitter. Both add a point of failure that can, and will, decide your internal traffic looks funny and needs extra scrutiny. You're paying to add a chokepoint that distrusts your own architecture.
And you'll never get a straight answer on caching for your own assets because their logic isn't designed for it. Your static bucket's IP range will be on a list you can't see.
Yeah, we tried the explicit proxy path for a staging Go service last month. Our baseline added latency was actually pretty close to what they advertised, maybe 8ms average.
But the P99 spikes were brutal, like 70-80ms sometimes. It totally messed with our pgx pool timeouts. Makes sense now hearing about their IP classifications and cache misses.
Did your traceroutes show consistent endpoints, or did they jump around? That's the part that seems hardest to model for.