{"slug":"topaz","name":"Topaz","domain":"topaz.sh","best_rank":6,"categories":1,"entries":[{"slug":"best-relationship-based-access-control-engine","title":"Best relationship-based access control engine","rank":6,"of":7,"score":2,"appearances":2,"modelRanks":{"ChatGPT":5,"Gemini":5},"reason":"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.","reasons":[{"model":"ChatGPT","reason":"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."},{"model":"Gemini","reason":"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."}],"fixes":[{"model":"ChatGPT","fix":"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."},{"model":"Gemini","fix":"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."}],"updated":"2026-07-16","api":"https://modelsagree.com/api/v1/best/best-relationship-based-access-control-engine.json"}],"page":"https://modelsagree.com/product/topaz","check":"https://modelsagree.com/check?q=Topaz","updated":"2026-07-17T08:03:51.047Z","attribution":"modelsagree.com, CC BY 4.0"}