An open-source, general-purpose policy engine for unified, context-aware policy enforcement across the stack.
Open Policy Agent (OPA) is an open-source policy engine that enables unified, context-aware policy enforcement across cloud-native and traditional infrastructure. It solves the problem of fragmented, hardcoded policy logic by providing a declarative language (Rego) to define rules that answer questions about authorization, compliance, and resource management. OPA decouples policy decisions from application code, allowing policies to be managed and updated independently.
Platform engineers, DevOps teams, and security architects building or managing cloud-native infrastructure, Kubernetes clusters, microservices, and multi-cloud environments who need consistent policy enforcement.
Developers choose OPA because it provides a single, unified policy engine that works across the entire stack, eliminating the need for disparate, hardcoded policy logic. Its declarative Rego language is purpose-built for policy, and its CNCF graduation ensures enterprise-grade stability and community support.
Open Policy Agent (OPA) is an open source, general-purpose policy engine.
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Rego allows writing flexible, context-aware policies without hardcoding, as demonstrated in access control examples on the Rego Playground.
OPA applies consistent policies across Kubernetes, Terraform, Docker, and more from a single engine, enabling uniform governance.
Policies are separated from application code, allowing independent updates without redeploying services, improving maintainability.
With REST APIs and native Go SDKs, OPA integrates easily with services in any language, as shown in the integration documentation.
Rego is a custom, purpose-built language with unique semantics, requiring significant time to learn compared to more common configuration formats.
Deploying and managing OPA as a separate service adds infrastructure overhead, including scaling, monitoring, and ensuring high availability.
Policy evaluation can introduce latency, especially in distributed setups with large data sets, necessitating performance tuning for high-throughput scenarios.