Skip to content

Drift Prevention vs Detection: Does a Polling Approach make sense At Scale?

Many organizations typically rely on pull-based GitOps tools (e.g. Argo CD) to detect and remediate drift on their Kubernetes clustes. This approach allows clusters to diverge before reconciling them on the next polling interval. For the last 4 years, Rafay customers have benefited from an architecturally different approach that focuses on true drift prevention, backed by robust detection capabilities across both cluster blueprints and application workloads.

Info

In a previous blog, we discussed how ArgoCD's reconcilation works and its best practices.

Drift Block

Architectural Pillars: Cluster Blueprints + Workloads

Rafay’s Kubernetes Management platform combines two complementary governance layers:

Hierarchy

1. Cluster Blueprints

This defines standardized, version-controlled baselines for cluster add-ons (e.g. logging, monitoring, network policies, service mesh).

-> Blueprints

2. Workloads

This defines and govern application deployment lifecycles through GitOps-driven YAML or Helm templates.

-> Workloads

Both layers are enforced on customer's Kubernetes clusters by Rafay’s Kubernetes operator operating in each cluster and connected to the centralized Rafay Controller via outbound-only TLS-based gRPC over port 443. Learn about the architecture here

Drift Prevention: Governance at Change-Time

Rather than waiting for periodic polling to detect drift, Rafay has taken the architectural approach to prevent unauthorized changes before they occur, using a combination of in-cluster enforcement and centralized policies:

Cluster Blueprint Enforcement

Any out-of-band modification—like deleting a managed add-on—is Blocked (denied outright) at the cluster's admission controller. An audit trail is generated and the administrator is notified of attempted drift. This approach guarantees that managed configurations cannot be altered inappropriately—even by cluster admins—regardless of how.

Blueprint Drift

Workload-Level Enforcement

Similarly, application-level drift policies can be enforced. If someone manually alters a deployment or manifest in-cluster, Rafay’s enforcement mode can prevent the change or at least alert the team—keeping application state aligned with the Git-defined workload blueprint.

Workload Drift

These enforcement policies ensure consistent, predictable, and compliant state at both infrastructure and app layers.

The Role of Detection & Remediation

In addition to preventing/blocking drift, Rafay also supports drift detection augmented by remediation rules where necessary:

  • Notifications and audit events provide complete traceability of attempted drifts.
  • Administrators can review and approve updates via Git-based workflows, reinforcing compliance pipelines.
  • Detection-only mode allows gradual adoption—organizations can begin with notifications and evolve toward strict enforcement over time.

Why In-Cluster Enforcement Beats Polling-Based Models

Pull-based tools like Argo CD employ an external reconciliation loop model that requires it to periodically query each cluster’s API server to compare "live state" with "Git state". While effective, this model exhibits several limitations compared to Rafay’s enforcement-first approach:

1. Real-Time Enforcement, Zero Latency

Polling models detect drift only after the next reconciliation cycle (often every few minutes), during which drift remains present. Rafay prevents drift at the moment it happens, eliminating windows of inconsistency or security exposure.

2. Reduced API Load & Central Bottlenecks

With enforcement localized inside the cluster, there’s no need for the central controller to poll hundreds of clusters. This avoids overwhelming Kubernetes API servers and eliminates noisy logs common in reconciliation-heavy environments .

3. Distributed, Scalable Enforcement

Each cluster executes governance locally, with centralized policy distribution via gRPC. This scalable model avoids performance bottlenecks found in centralized reconciliation engines as environments grow.

4. Stronger Security Posture

Only outbound TLS (port 443) is required from clusters to the controller—no need to expose API servers externally, simplifying firewall configuration and reducing attack surface  .

5. Unified GitOps Workflow

Both blueprints and workloads are version-controlled in Git. Changes flow through pull requests, reviews, and webhook triggers, ensuring any deviation outside this chain is either blocked or logged—enforcing change-management discipline and traceability.

Summary: Prevention First, Detection & Remediation Second

Rafay’s architecture flips the traditional GitOps model on its head. Instead of waiting to detect drift and then remediate, Rafay enables drift prevention first, backed by detection and remediation where necessary. Enforcement happens instantaneously within the cluster, supported by a centralized policy source and full audit visibility.

Compared with pull/poll-based tools like Argo CD—which can only correct drift after it occurs, Rafay ensures configuration consistency across both add-ons and application workloads, with far lower API load, stronger security, greater scalability, and tighter compliance.

Here are some examples of Real‑World Outcomes in Enterprises that use Rafay:

  • Platform teams can enforce baseline configurations—including network policies, OPA Gatekeeper, observability tools, or security agents—across all clusters without exception.
  • Application teams are required to deliver updates through GitOps pipelines; any manual in‑cluster deviation is flagged or blocked.
  • Since enforcement is handled by in‑cluster operator logic rather than central polling, the system scales universally—even in hundreds of clusters.
  • Outbound‑only architecture simplifies network security and maintains zero‑trust posture.
  • Drift prevention policies paired with rich audit trails deliver strong compliance evidence—even for regulated environments.