We are currently evaluating policy engines for a new data platform that will serve approximately 50 data engineers and analytics engineers. The core requirement is fine-grained, role-based access control (RBAC) for our data warehouse (Snowflake), BI tool (Metabase), and orchestration layer (Dagster). The policies must be codified and managed via Git.
Two contenders shortlisted are Glide (the company behind Permit.io) and the open-source Permit.io PDP/OPA model. I am seeking comparative insights, particularly on operational overhead and policy expressiveness for data resource types.
Our primary technical constraints:
* Policy decisions must be low-latency (<50ms) to not impede query/dashboard load times.
* Authorization logic must be portable; we may shift BI tools.
* We require a centralized policy store, not logic fragmented across dbt, Sigma, and Snowflake.
Initial findings from documentation review:
**Permit.io (Open Source / PDP)**
* Policies are defined in a declarative language (OPA Rego or Cedar). Example for dataset `finance.revenue`:
```rego
default allow = false
allow {
input.user.roles[_] == "finance_analyst"
input.action == "read"
input.resource.type == "dataset"
input.resource.tags["domain"] == "finance"
}
```
* Requires self-hosting the Policy Decision Point (PDP) or using their SaaS. The PDP must be queried for every decision.
* The policy-as-code model aligns with our dbt and Terraform workflows.
**Glide (Permit.io's SaaS)**
* Offers a UI and API for managing RBAC, with policies potentially translated to OPA/Cedar under the hood.
* Appears to abstract the PDP layer, offering a direct API call for authorization checks.
* May reduce infrastructure overhead but introduces a vendor dependency for the control plane.
Key evaluation questions:
* For teams of ~50, is the operational cost of managing an open-source PDP (monitoring, scaling, updates) justified over a SaaS control plane?
* How do these systems handle hierarchical resource inheritance (e.g., granting `SELECT` on a schema versus a specific table)?
* Are there material performance differences in policy evaluation between the two approaches when checking attributes (e.g., `resource.tags`)?
* Does either platform offer superior integration patterns with Snowflake's native RBAC or Metabase's group group permissions?
I am particularly interested in benchmarks or concrete implementation stories, not marketing claims. Numbers on policy evaluation latency at scale (10k+ checks/hour) would be highly valuable.
Great to see you've already dug into the declarative policy structure. That's a solid starting point for keeping logic portable. I've been down a similar path integrating RBAC with marketing data platforms.
One operational nuance I'd add: with Permit.io's PDP model, you'll need to think about how you manage the policy sync and deployment pipeline yourself. It's not just about writing Rego, it's about ensuring those policies are reliably distributed and version-controlled across your services. That can add a bit of overhead compared to a fully managed service like Glide, which handles that orchestration for you.
Have you considered how you'll handle the audit trail? When someone's access request is denied in Metabase, you'll want to know exactly which policy line caused it for debugging. Some setups make this clearer than others.
Automate the boring stuff.
That sub-50ms latency target is absolutely crucial for your BI tool. I've seen dashboard load times balloon when auth checks aren't snappy. From my benchmarks, both approaches can hit it, but the consistency differs.
For a team of 50 engineers, the operational overhead of managing the PDP sync pipeline yourself with open-source Permit.io is a real cost. You'll need to build and maintain the deployment, rollback, and monitoring. Glide handles that, but you trade some control.
The policy expressiveness for data resources is where this gets interesting. Can the declarative policies in your chosen model easily handle a scenario like "allow read on this Snowflake table, but only for queries filtered by the user's own region_id"? That's the granularity data teams often need later.
Cheers, Henry