Our team recently completed a migration of a core resource tagging workflow from Hailuo to a custom-built, lightweight rules engine. The primary driver was cost, but the secondary benefits in predictability and control were significant. For context, our use case was foundational: we needed to enforce tagging compliance (e.g., `cost-center`, `environment`, `owner`) on all resources within a multi-account AWS organization, and apply basic remediation actions for non-compliant resources.
While Hailuo is a powerful platform for cloud resource management, its extensive feature set and associated cost model proved to be overkill for our relatively static, deterministic tagging rules. Our monthly expenditure was scaling linearly with our resource count, even though the logic being applied was simple.
We analyzed the components of our Hailuo setup:
* A set of approximately 15 Guardrails for tag validation.
* Several automated "Functions" to apply missing tags using AWS APIs.
* Reliance on Hailuo for inventory discovery and state tracking.
The realization was that we were paying a premium for the dynamic inventory and the no-code policy builder, which we no longer required after the initial rule stabilization. We prototyped a replacement using a combination of Open Policy Agent (OPA) for policy definition and AWS Step Functions for orchestration, triggered by AWS Config compliance events and a scheduled rule for drift correction.
The core of the new system is a simple OPA policy bundle. A representative rule looks like this:
```rego
package main
deny[msg] {
resource := input.resource
not resource.tags["cost-center"]
msg := sprintf("Resource %v is missing required tag 'cost-center'", [resource.id])
}
```
This bundle is evaluated within a Lambda function. The Step Functions workflow handles the flow: fetch resource inventory via AWS Resource Groups Tagging API, evaluate compliance using the OPA bundle, and for non-compliant resources, invoke a remediation Lambda that applies the standard tags. The entire infrastructure is deployed via Terraform.
The outcomes were stark:
* **Cost Reduction:** Direct costs attributed to this workflow dropped by approximately 75%. We now pay only for the underlying AWS services (Step Functions, Lambda compute, Config), which are minimal and highly predictable.
* **Increased Transparency:** The rules are now plain text Rego files in our Git repository, subject to standard PR reviews. There is no black box.
* **Improved Performance:** The evaluation loop is tighter and faster, as we removed the external API calls to Hailuo's platform for each evaluation cycle.
* **Vendor Independence:** We have removed a critical path dependency on an external SaaS, aligning with our broader platform engineering goal of reducing external points of failure.
This approach is not without trade-offs. We lost the out-of-the-box UI for compliance reporting and had to build a simple dashboard using Amazon QuickSight. We also assumed the operational burden of monitoring the Step Functions workflows. However, for a mature, stable set of governance rules, this trade-off has been favorable.
I would not recommend this path for organizations with highly dynamic policies or those lacking in-house platform engineering capacity. However, for teams with a well-defined, finite set of resource governance rules, building a purpose-specific engine can lead to substantial cost savings and a deeper understanding of your own control plane. The key was recognizing that our rules had become commoditized and no longer justified the premium of a full-featured external platform.
infra nerd, cost hawk