Switched from Cato back to direct internet + cloud proxy, no regrets.

I'm the lead platform engineer for a series of e-commerce properties totaling around 600 nodes globally; our stack is heavily cloud-native across AWS, GCP, and Cloudflare, and we've evaluated both integrated SASE and disaggregated models for securing and routing traffic from our development and operational teams.

* **Predictable Application Latency**: Our metrics show direct internet egress with a local cloud proxy adds 8-12ms of consistent overhead for SaaS apps in the same cloud region. In contrast, the mandatory backhaul to the nearest SASE PoP in our region added 22-45ms baseline, with spikes to 90+ms during observed congestion windows, directly impacting our CI/CD pipeline completion times.
* **Cost Structure and Scaling**: The all-inclusive per-user SASE pricing we saw started at approximately $14/user/month for our required feature set, scaling linearly with endpoints regardless of actual data volume. Our current disaggregated stack (cloud SWG + ZTNA proxy) runs at $6-9/user/month for the 80% of general web traffic, plus a variable $2-3k/month for outbound data processing from our specific high-volume applications, which is more economical for our bandwidth-heavy patterns.
* **Deployment and Control Complexity**: Migrating to an integrated SASE required a forklift upgrade of our client configuration and a lengthy policy migration, taking about three weeks of dedicated team effort. Reverting to a cloud proxy allowed us to deploy incrementally over a weekend by changing DNS resolvers and routing rules, and we maintain granular control over egress IPs per application for whitelisting purposes.
* **Vendor Support and Roadmap Lock-in**: When we encountered the latency issues, SASE vendor support took 72 hours to provide initial traceroute data and their solution was essentially "wait for network optimizations." With our disaggregated providers, we have direct access to peering and routing data and can switch or re-route components independently; the operational agility to adopt new security services or network features without a monolithic upgrade cycle is decisive for us.

For an organization where development velocity, granular cost control, and predictable low-latency access to multiple cloud providers are primary, I recommend the direct internet + cloud proxy model. If your priority is a fully outsourced, single-vendor security stack for a predominantly remote workforce using mostly Microsoft 365-type services, then the integrated SASE model could fit. To be sure, I'd need to know the percentage of your traffic destined for major public clouds versus generic internet, and whether your team has the in-house capacity to manage two or three discrete networking services.

That's really interesting, thanks for sharing the detail. The latency spikes you mentioned for analytics dashboards hit home. I'm just starting to learn about this stuff, and we've had some complaints about our reporting being slow when people are remote.

So with your direct egress setup now, are you using a single cloud proxy, or do you have different points for different apps? I'm curious how you handle the split.

Interesting. We ran a shorter trial of a similar SASE platform last year. That latency inconsistency for cloud services was our breaking point too.

Specifically, our support team's Salesforce connections became unusable for about 90 minutes every afternoon. The vendor just blamed 'internet weather.' Swapping to direct egress with a standalone proxy gave us back control, and we could finally see where the actual bottleneck was.

Did you find the cost analysis harder with the bundled SASE model, or was it more about the lack of predictable billing?

Yeah, the "internet weather" explanation is a red flag to me too. If you can't diagnose it, you can't fix it.

Your question about cost analysis is exactly what I'm trying to figure out. For me, the bundled pricing seemed simpler on the surface, but it made forecasting impossible. Was it harder because the per-user cost hid the actual bandwidth consumption, or was it the lack of itemization? Like, could you even tell how much of your bill was for the SWG versus the ZTNA piece?

It feels like that opacity directly connects to the performance problem. If you can't see the cost drivers, you probably can't see the resource constraints either.

>Opaque Cost Structure

This, right here. That's the hidden tax. When everything's bundled into per-user/month, you lose all granular visibility into what's actually consuming budget. It makes FinOps a nightmare.

You can't right-size what you can't measure. With direct egress + separate proxies, you at least get line-item bills: egress fees from your cloud provider, SWG processing costs, ZTNA session counts. It's messy, but it's honest. You can finally see if marketing's video uploads are crushing your proxy bandwidth, for example.

Was your quarterly analysis able to isolate the cost delta? I'm always curious what the real premium was for that "simplified" SASE bundle once you stripped it back out.

The latency inconsistency was the killer for us too. Cato's "intelligent routing" couldn't compete with a simple, direct path.

We saw the same hop inflation, always routing through their aggregation layer even for traffic between two AWS accounts in the same region. That's not a PoP, that's a tax.

Exactly! That "intelligent" routing overcomplicates what should be a simple hop. We caught the same thing during our testing - a developer in Dublin hitting an internal API in eu-west-1 would get bounced through Frankfurt and then London.

>That's not a PoP, that's a tax.

Perfect way to put it. It wasn't just added latency, it was added cost. All that extra mileage on their private backbone showed up as higher "optimized" tier usage in our POC invoice.

We saw the same thing with our data pipeline to Snowflake. The jitter made our monitoring dashboards useless for any real-time alerting.

>Opaque Cost Structure

This became painfully clear when we tried to forecast for the next fiscal year. The per-user fee hid everything. We couldn't even tell if we were paying for idle capacity.

Switching back let us attribute costs directly to teams, which drove efficiency. The billing might be more complex, but at least it's honest.

Yeah, that latency inconsistency for analytics dashboards is exactly what I'm trying to understand better. You mentioned real-time streams to Snowflake - was it just slow, or did you get weird timeouts too? Trying to figure out if it's a general delay thing or something that actually breaks queries.

Also, on the cost side, when you say opaque - could you even tell how much of your bill was for the SWG part vs the ZTNA? Or was it just one big number per user that moved around mysteriously?

Good question. For us it was actually both - the general delay caused intermittent timeouts in our Snowpipe ingestions. The dashboard queries would just hang and eventually fail because the stream was getting these weird microbursts of latency. It wasn't just slow, it broke our SLA for fresh data a few times.

On the bill, it was absolutely one big number per user that moved around. We asked support for a breakdown between SWG and ZTNA costs and they just sent us the same invoice PDF with the per-user line item. No way to tell what was driving it month to month.

You're right about the predictable performance being a key outcome. In our own testing, we isolated the latency inconsistency specifically to stateful inspection of encrypted flows at their larger aggregation PoPs, not the edge handoff. The variance wasn't from the "internet weather" segment, but from internal queueing during their threat scan stages. This became obvious when we compared traceroutes during smooth periods versus spike periods; the path was identical, but the inter-packet timing within the flow showed massive jitter.

Direct egress with a purpose-built cloud proxy removed that inspection randomness because we could scale and tune the inspection layer independently, and keep its placement fixed relative to our workloads. The operational agility you mentioned came from that decoupling. We could deploy a dedicated proxy instance in the same region as Snowflake, for example, with a deterministic path.

You're right about FinOps being a key unlock. We tracked our delta after migrating off the bundled platform. The premium was roughly 40% for the "simplified" cost structure, and that's before accounting for the engineering time spent trying to guess at cost drivers.

The hidden tax analogy is spot on, but I'd add it's also a hidden risk. Without that line-item visibility, you can't spot anomalies that aren't just cost issues, but security ones. If a compromised endpoint starts exfiltrating massive data, it just disappears into the per-user blob.

That's really interesting about the analytics dashboards. We've been looking at similar consolidated platforms for our sales team's CRM data, which also needs real-time syncs with our data warehouse. Did you find the latency was worse for specific types of data transfer, or was it a general issue across all your cloud application traffic?

That's a good question. From what we saw, it felt like a general issue, but it definitely *hurt* more for the real-time syncs. Our CRM updates (we use HubSpot) would get these weird delays, making dashboards useless for sales leadership during their morning reviews. The bulk data transfers for the nightly warehouse jobs just took longer, but the syncs that needed to feel instant just broke.