Permit.io vs. Cerbos vs. Oso: Fine-Grained Authorization for AI Agents

The three products answer the same question, but they model the underlying access relationships differently, and that difference shows up the moment an agent's permission logic gets past a handful of simple roles.

Cerbos is the most conventional of the three: attribute-based access control, with role-based access control still available underneath, and a mechanism called derived roles that lets a role's effective permissions change based on runtime conditions (an agent acting as an editor on a document it owns versus one it was merely shared) without hardcoding a new role for every combination. Policies are written in YAML with CEL-style conditions, not a general-purpose logic language. This is a deliberate ceiling: Cerbos does not model relationships as a traversable graph the way a ReBAC system does, so a genuinely relationship-heavy question, can this agent act on any document nested three folders below one this user owns, has to be expressed as attributes passed into the request rather than resolved by graph traversal inside the engine itself.

Oso goes the opposite direction. Its Polar language is a declarative, logic-based language that lets RBAC, ReBAC, and ABAC coexist in one policy rather than forcing a choice, and relationships are modeled as facts (this document belongs to this folder, this folder belongs to this workspace) that Oso Cloud can traverse to answer a check. The distinguishing capability for agent use cases specifically is that Oso can generate a filtered query, rather than a single yes/no answer, so an agent's data retrieval step (a RAG lookup, a search across records) can ask which rows this agent is allowed to see and get back only the permitted set, instead of running an unfiltered query and post-filtering the results in application code.

Permit.io sits between the two, both technically and in market positioning. It supports RBAC, ABAC, and ReBAC together, and lets teams author policy in either Rego (the language OPA popularized) or Cedar (the language AWS open-sourced for Verified Permissions), rather than inventing a third proprietary syntax. On top of that, Permit.io ships policy templates and guardrails specifically framed around agent behavior, described publicly as a four-perimeter model: policies that filter what reaches an LLM's prompt, policies that scope what a RAG pipeline can retrieve, and policies that gate which tools, MCP servers, and external APIs an agent is allowed to call. That framing does not change the underlying decision math, it is still RBAC/ABAC/ReBAC evaluated against a policy, but it means a team adopting Permit.io for agent use cases gets pre-shaped policy patterns for agent-specific choke points instead of building that modeling from scratch.

Where each engine sits in the call path also differs. Cerbos and Permit.io both keep the actual per-request decision local: Cerbos as a self-hosted sidecar or standalone service you run yourself, Permit.io as an open-source PDP container that syncs policy from its SaaS control plane but evaluates locally, so neither adds a network round trip to a vendor's cloud on every single tool call. Oso Cloud's default path answers checks over the network from a managed service, though its local-authorization mode (evaluating against data already synced to the caller) exists specifically to avoid that round trip when latency matters, meaning a team choosing Oso for latency-sensitive agent tool calls needs to plan for local authorization from the start rather than defaulting to the network path.

Cerbos deploys as infrastructure you run and own: a container or binary placed as a sidecar next to each service, or as a shared standalone instance, with policies loaded from files, a Git repository, or Cerbos Hub if you want the commercial management layer. Nothing about the core PDP requires a connection to Cerbos's own servers to make a decision, which matters for air-gapped or strict data-residency environments. Permit.io splits the deployment: policy authoring, testing, and audit trail live in Permit.io's SaaS control plane, while the actual enforcement point is an open-source PDP container you run inside your own environment and that syncs policy bundles down from the control plane, so a network outage to Permit.io's cloud degrades policy updates, not live decision-making, assuming the PDP already has its last synced bundle. Oso Cloud is deployed as a managed service by default, meaning there is no PDP to run or patch, at the cost of authorization data living in Oso's infrastructure unless the team specifically adopts the local-authorization pattern to keep decision data closer to the caller.

All three publish SDKs across the common backend languages (TypeScript/Node, Python, Go, Java, and others), and all three integrate at the point where application or agent code would otherwise write an inline permission check. Cerbos's integration story is the most protocol-neutral of the three: a gRPC or REST call to a PDP that could be running anywhere, with no dependency on a specific cloud or identity provider. Permit.io's integrations lean toward the agent and MCP ecosystem specifically, with documented patterns for gating MCP tool calls and RAG retrieval, plus support for teams that want to bring existing Rego or Cedar policies rather than start from zero. Oso's integration story centers on the data layer: because Oso Cloud can return a filtered query rather than a single check, it integrates most naturally where an agent's action is a data read across many rows (a search, a RAG retrieval, a list endpoint) rather than a single discrete action, which is a narrower but deeper integration surface than the other two.

None of the three is a configuration toggle; all three require modeling the actual resources, roles, and relationships an agent's actions touch before any check can return a meaningful answer, and that modeling work dominates the total effort far more than installing the SDK does. Cerbos's effort centers on writing and testing YAML policies with CEL conditions and derived roles for every resource type an agent can touch, plus standing up and operating the PDP fleet itself, which is real infrastructure work with no managed alternative unless Cerbos Hub is adopted for the authoring side. Permit.io's effort centers on deciding which policy language to standardize on (Rego or Cedar) if the team does not already have a preference, modeling the RBAC/ABAC/ReBAC relationships in Permit.io's data model, and configuring the agent-specific perimeters (prompt, RAG, tool access) rather than treating an agent as just another API caller. Oso's effort centers on translating the application's actual relationship graph (who owns what, what is nested under what, who was delegated what) into Polar facts and rules, which is a different kind of modeling than writing conditional role logic and tends to front-load more design work before the first check runs, though it pays off specifically when the access model really is relationship-shaped rather than attribute-shaped.

Cerbos is the most transparent of the three on price because there is effectively one number: the Cerbos PDP itself is Apache 2.0 licensed and free to run with no usage caps, on any infrastructure, indefinitely. Cerbos Hub, the commercial policy management and collaboration layer, exists as a paid product, but its pricing is not published as a flat, comparable number on Cerbos's public materials as reviewed for this piece, so a team that wants Hub specifically should confirm current terms directly rather than budgeting from a guess.

Permit.io publishes tiered, self-serve pricing scaled by monthly active users and tenant count. Its documented tiers describe a paid plan supporting up to roughly 25,000 MAU and 100 tenants across up to 5 environments, with a higher tier extending to roughly 50,000 MAU and 20,000 tenants across up to 50 environments and dedicated support. Treat these as the figures published at the time of writing, not a locked-in quote, and confirm current numbers before budgeting, since usage-based vendor pricing pages change without much notice.

Oso publishes a free developer tier for Oso Cloud, which is enough to build and test a policy end to end. Beyond that, we could not confirm a consistent, flat public price for paid Oso Cloud tiers directly from Oso's own current materials; one third-party review cites a paid tier starting near $149 per month, but that figure did not appear as a listed price on Oso's own site as reviewed for this piece, so it should be treated as a data point to verify with Oso directly, not a confirmed number.

Each engine's biggest strength tracks directly to the design choice behind it, and the limitation is usually the other side of that same choice.

There is no universal winner among these three; the right pick tracks the shape of the access model an agent actually needs enforced, and what the team already runs. A team building agents that call a genuinely diverse set of internal tools and RAG data sources, that wants agent-specific policy patterns already thought through rather than modeled from scratch, and that is comfortable running a self-managed PDP alongside a vendor's SaaS control plane, gets the most immediate value from Permit.io. A team with hard data-residency or air-gapped requirements, or one that already has infrastructure and platform engineers comfortable owning a PDP fleet and writing YAML policy, and whose access model is closer to roles-plus-conditions than a deep relationship graph, gets the most durable fit from Cerbos, in part because the core engine costs nothing to run at any scale. A team whose access model is fundamentally about relationships, an agent inheriting access through folder hierarchies, sharing chains, or delegation trees, and whose agents primarily need to filter what data they retrieve rather than gate single discrete actions, gets the most leverage from Oso, particularly now that it has repositioned specifically around agent security. A team already standardized on OPA and Rego across its broader infrastructure, not just for agents, may find that Permit.io's Rego support lets it extend that existing investment rather than adopting a wholly new policy language, which is worth weighing before treating Cerbos's or Oso's own languages as a green-field choice.

All three of these products solve for policy complexity that has already outgrown hardcoded checks. If an agent's actual footprint today is one or two internal tools with a small, stable set of callers, a hardcoded role check in the application code (this agent may call this endpoint, full stop) is not a security gap that needs a policy engine to close; it is an appropriately sized control for the actual blast radius, and standing up a PDP fleet or a managed policy cloud for that footprint adds operational surface without a matching reduction in risk. The signal that it is time to move to one of these three is usually the same signal that would push a human-facing application there: permissions that vary by resource attribute or relationship rather than a flat role, more than a couple of agents or tools that need consistent policy rather than each hand-rolling its own check, or a compliance or audit requirement to show, after the fact, exactly why a specific decision was allowed or denied. Separately, a team already running OPA/Rego at scale across non-agent services should weigh extending that existing investment (Permit.io's Rego support is the closest bridge among these three) before introducing an entirely separate policy language just for agent traffic, since running two unrelated policy stacks in parallel is its own maintenance cost.

Run these checks against one real agent workflow the team already operates, not a hypothetical, before committing to any of the three.