So everyone's talking about Zero Trust and SASE, and Perimeter 81 keeps popping up. The marketing is slick, but I'm genuinely curious: is anyone out there *actually* running this for a legit, 1000+ user, multi-cloud, production-heavy environment? Not a POC, not a 200-person marketing firm, but a place where network downtime means lost revenue in six figures per hour.
We trialed it. The concept is solid – identity-centric, integrates with IdP, etc. But when you scale, the cracks show. The "simplified" policy model becomes a nightmare of hidden precedence. Had an incident where a policy granting "Read" to an S3 bucket implicitly allowed a tunnel to be established to the VPC endpoint, bypassing our intended flow logs. Support's answer? "Working as designed." Great.
The agent's resource consumption is non-trivial. Deploy it to a few hundred developer laptops and you'll start getting tickets about battery life. Try managing the network configuration when your org has a mix of corporate-owned, BYOD, and contractor devices across 30 countries. Their API is "RESTful" in the same way a JSON dump of a CLI is.
```json
{
"policy": {
"action": "allow",
"resources": ["*"],
"users": ["*"],
"context": {"unspecified": "defaults to permissive"}
}
}
```
*(Not actual P81 config, but a sardonic representation of the vague abstractions you'll fight.)*
I want to believe the SASE future is here. But when you peel back the UI, you often find a product that works beautifully at 100 users and becomes a fragile house of cards at 1000. The core question: is Perimeter 81 a robust platform, or just a prettier VPN wrapper with a cloud dashboard? Looking for war stories, not sales sheets.
You've perfectly described the gap between marketing and operational reality. That implicit tunnel creation from what looked like a simple object storage policy is a classic example of abstraction leaks that become critical at scale.
We had a similar scaling issue, but with their network segmentation for ETL workloads. The agent's resource consumption you noted becomes unmanageable when deployed on transient cloud instances for data movement. We measured a consistent 8-12% CPU overhead per node, which directly translated to cost escalation across hundreds of pipelines.
Their API being a JSON dump of a CLI is spot on. It makes automation fragile. We built a wrapper that essentially reverse-engineered their policy precedence logic after a similar incident, because the actual merge order wasn't documented. It felt like we were maintaining the product for them.
Data is the new oil – but only if refined
The implicit tunnel creation issue you hit is a fundamental architectural flaw in their policy engine, not just a scaling quirk. It suggests their abstraction doesn't truly understand cloud resource intent. S3 Gateway VPC endpoints have a very specific networking profile, and a "Read" policy should not be able to spin up a network path that bypasses security observability.
We observed a parallel issue with GCP Private Service Connect. A policy for "access to BigQuery" could, under certain conditions, auto-provision a PSC endpoint that then advertised internal routes we never intended to expose. The problem is that these platforms, in an attempt to be "helpful," make irreversible network decisions based on overly broad policy intents. This is why we ended up building a layer in Terraform that explicitly defined every network egress point, then fed allow-lists into the SASE tool, treating it purely as an enforcement layer. You can't let it think for you.
Their support response is the most telling part. "Working as designed" means the design is wrong for complex multi-cloud. It's built for the median use case, where such implicit behavior is a feature. In a high-stakes environment, it's a critical vulnerability. You're now forced to reverse-engineer and model their precedence logic yourself, which defeats the entire value proposition of a managed service.
Boring is beautiful