Hey everyone, been lurking here for a bit while we try to sort out our authz mess. We're a mid-sized product team and our homegrown RBAC system is... showing its age. It's a spaghetti of conditionals in our backend services and feature flag checks.
I keep seeing Permit.io pop up in discussions, especially around externalizing authorization and the whole policy-as-code angle. The integration with our existing IdP (Okta) and the focus on product-led growth use cases seems like a good fit on paper.
But I'm having a hard time finding deep-dive, "warts and all" reviews from teams actually running it in production. The docs are slick, but I want to know about the operational bits.
* How's the performance overhead for policy evaluation at runtime, especially for fine-grained checks (like "can user X edit field Y on resource Z")?
* Are you using it just for customer-facing apps, or also for internal admin panels? We have both needs.
* What was the migration path like from an older system? Did you do a big-bang cutover or a gradual feature-by-feature rollout?
* Any surprises with their pricing model at scale?
We're leaning towards a PoC, but real-world gotchas would be super helpful before we commit. Thanks in advance!
We've had it live for about 6 months now, handling both our B2B app and internal admin tools. The performance hit on those fine-grained checks you mentioned is minimal in practice, but you need to be smart about caching user contexts. The latency is on par with a local policy engine call once you get past the initial setup.
Migration was the real beast for us. We didn't do a big bang. We tied each new Permit policy to a specific feature flag rollout using our existing setup (Optimizely). This let us migrate role-by-role and flip the switch for user segments gradually. It turned a scary deployment into a controlled experiment.
Pricing got us a little. Watch out for their "decision" count if you have high-volume background jobs or service-to-service calls. We had to adjust some architecture to batch checks. Definitely model that out during your PoC.
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We're running it for both customer-facing features and internal tools, and that's actually been a big win. The same policy definitions can govern a user editing their profile and an admin purging an account, which drastically cut down on our policy sprawl.
On the migration path, we took a hybrid approach. For our main app, we did a gradual rollout tied to feature flags like user575 mentioned. But for our new internal admin panel, we built it with Permit.io from day one. That gave the team a clean sandbox to learn the system without the pressure of migrating live user permissions, and we could then apply those patterns back to the main app migration.
The only real surprise we hit was around those fine-grained attribute checks. The performance is fine, but writing the policies for complex, multi-attribute resources (think "can edit field Y only if the account's subscription tier is Pro AND the resource is in draft status") required a solid upfront design for your resource and user context shape. It's easy to paint yourself into a corner if you don't think that through early in the PoC.
api first
Thanks for the details, this is really helpful. The idea to tie new policies to existing feature flags is smart, I wouldn't have thought of that.
Could you say a bit more about the caching you mentioned? Like, are you caching the whole user context locally, or just the policy decisions?
And yeah, the pricing model with decision counts is a bit of a curveball. Makes you think twice about how you structure background tasks for sure.
On caching, we're primarily caching the user context, not the decisions. The policy engine needs a full snapshot of user attributes and role assignments to evaluate. We use a short TTL (60 seconds) on a local in-memory cache, keyed by the user's stable ID from our IdP. This drastically cuts down on calls to Permit's API for the "load user" step, which was the real latency culprit in our initial tests.
For background jobs and the decision count concern, we ended up grouping tasks. Instead of checking permissions for 10,000 individual items in a loop, we pre-filter the dataset in our application layer using the user's known roles and tenant context, then do a single batch "can user perform action X on this set" check. It requires a bit more app logic but keeps the decision count sane.
Our monitoring showed that caching the user context reduced our 95th percentile authorization latency from ~120ms to under 15ms. The decision calls themselves are cheap if the engine doesn't have to fetch user data on every request.
Data never lies.