The verdict
Topaz appears in 2 AI-ranked categories.
Positioning brief — for the Topaz team
Why the models put Topaz at #6 for relationship-based access control engine
- ReBAC alongside RBAC and ABAC GPT · Gemini“supporting ReBAC alongside RBAC and ABAC”
- low-latency sidecar GPT · Gemini“low-latency sidecar or edge deployment”
- graph relationships and rich contextual logic GPT · Gemini“graph relationships and rich contextual logic”
What the models credit SpiceDB (#1) with — and don’t credit Topaz
- strict consistency guarantees via ZedTokens GPT · Claude · Gemini · Grok“natively enforces strict consistency guarantees via ZedTokens to prevent the "New Enemy" race condition”
- proven massive scale Claude · Gemini · Grok“proven massive scale (billions of relationships, millions of checks/sec, used by OpenAI/ChatGPT Enterprise)”
- richest tooling ecosystem GPT · Claude · Grok“the richest tooling ecosystem (zed CLI, playground, import/export, watch API) of any ReBAC engine”
What would move the rank — the models’ fix lines, unified
- more complex than a purpose-built tuple engine GPT · Gemini“more complex than a purpose-built tuple engine”
- unsuitable for very large authorization graphs GPT · Gemini“the default embedded directory is unsuitable for very large authorization graphs”
- deep recursive graph traversals less performant Gemini“performing deep recursive graph traversals or reverse-index queries (such as listing all resources a user can access) is highly complex to write and less performant”
Restructured from verbatim model output · nothing invented · every quote machine-verified
Combines a relationship directory with local policy evaluation, supporting ReBAC alongside RBAC and ABAC while allowing low-latency sidecar or edge deployment; valuable when policies need both graph relationships and rich contextual logic.
Gemini Bridges the gap between OPA's policy-as-code standard (Rego) and Zanzibar-style directory concepts. It runs as a low-latency sidecar microservice, querying a local ReBAC directory to make authorization decisions in microseconds. This is ideal for teams wanting to combine relation-based modeling with traditional policy-based checks.
Where Topaz falls short, per the models
- GPT Its OPA-derived policy-plus-directory architecture is more complex than a purpose-built tuple engine, and the default embedded directory is unsuitable for very large authorization graphs.
- Gemini Because it relies on Rego and the OPA engine, performing deep recursive graph traversals or reverse-index queries (such as listing all resources a user can access) is highly complex to write and less performant compared to native ReBAC graph engines.
Top alternatives per the models: SpiceDB · OpenFGA · Permify · Ory Keto
Unifies the proven Open Policy Agent (OPA) engine with an embedded relationship directory, delivering sub-millisecond local decisions that combine ABAC attributes with ReBAC relationships.
Where Topaz falls short, per the models
- Gemini Authoring and maintaining dual paradigms (Rego policy logic plus directory schema trees) creates a steeper learning curve than single-DSL options.
Top alternatives per the models: OpenFGA · SpiceDB · Cerbos · Oso
Watch Topaz
Boards re-poll weekly and the models change their minds. One short email only when Topaz's standing moves — a rank change, a rival overtaking, or new reasoning from the models. Nothing otherwise.
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Topaz ranks #6 for best relationship-based access control engine by AI-model consensus. Put the badge in your README, docs or site — it updates automatically as the models re-rank.
[](https://modelsagree.com/best/best-relationship-based-access-control-engine?utm_source=badge&utm_medium=embed&utm_campaign=badge-topaz)<a href="https://modelsagree.com/best/best-relationship-based-access-control-engine?utm_source=badge&utm_medium=embed&utm_campaign=badge-topaz"><img src="https://modelsagree.com/badge/topaz.svg" alt="Topaz — ranked #6 for Best relationship-based access control engine by AI models on ModelsAgree" height="28"></a>Rankings are computed from what the models answer, re-polled on demand · raw reasoning shown verbatim · methodology