Sentinel vs a DIY ELK+Wazuh stack - total cost of ownership over 3 years?

Having recently completed a detailed TCO analysis for a client migrating from a self-managed security stack to a managed SIEM, I believe the conventional wisdom of "DIY is always cheaper" requires rigorous re-examination, particularly at scale. The comparison between Microsoft Sentinel and a combined ELK (Elasticsearch, Logstash, Kibana) + Wazuh stack is a frequent point of debate, but often centers on licensing costs alone. A true three-year TCO must account for personnel, data ingestion, storage, compute, and the opportunity cost of maintenance versus innovation.

To frame the discussion, let's establish a hypothetical but realistic environment for modeling:
- **Data Volume:** 500 GB of log ingestion per day (a mix of Azure/cloud, network, endpoint).
- **Retention:** 90 days hot, 1 year total (with cold tier for DIY).
- **Team:** Requires coverage from at least 2-3 platform/security engineers for DIY operations.

The primary cost vectors for a DIY ELK+Wazuh stack over 36 months are:

* **Infrastructure Compute & Storage:**
* The Elasticsearch data nodes for this workload, with replication, require significant resources. A typical deployment might use 6-8 `Standard_D8s_v4` Azure VMs for hot data, plus object storage for cold archives.
* Associated costs: VM compute, premium SSD managed disks for performance, network egress, and Kubernetes cluster management if containerized.
* **Personnel Operational Burden:**
* Elastic Stack upgrades, version compatibility management with Wazuh, index lifecycle management, and performance tuning.
* Security hardening, certificate rotation, and patch management for the underlying OS and all open-source components.
* Building and maintaining custom parsers (Logstash grok filters, Elasticsearch ingest pipelines) for unsupported log sources.
* **Scalability Engineering:**
* Architecting for high availability and disaster recovery adds complexity and duplicate infrastructure costs.
* Scaling events (e.g., sudden log volume spikes) require manual intervention or sophisticated automation.

```hcl
# Example Terraform snippet for part of the core infrastructure.
# This illustrates the declarative overhead, not a full deployment.
resource "azurerm_linux_virtual_machine" "es_data_node" {
count = 6
name = "es-data-node-${count.index}"
size = "Standard_D8s_v4"
admin_username = var.admin_user
resource_group_name = azurerm_resource_group.siem.name

os_disk {
caching = "ReadWrite"
storage_account_type = "Premium_LRS"
disk_size_gb = 1024
}

source_image_reference {
publisher = "Canonical"
offer = "0001-com-ubuntu-server-jammy"
sku = "22_04-lts-gen2"
version = "latest"
}
}
```

In contrast, Microsoft Sentinel's cost model is predominantly consumption-based (per GB ingested) with fixed-cost automation and logic apps. The key differentiators in the TCO analysis are:

* **Elimination of Infrastructure Management:** No VMs, disks, or Kubernetes clusters to provision, scale, or patch for the SIEM itself. This directly reduces cloud spend and engineering time.
* **Integrated Data Connectors:** While not universally free, the managed connectors for Microsoft 365, Azure AD, and Azure resources significantly reduce parsing and normalization effort.
* **Inherent Scalability and HA:** The platform handles ingestion spikes and provides SLA-backed availability without additional design or cost.
* **Opportunity Cost:** The engineering hours saved from maintaining the data plane can be redirected to higher-value activities like threat hunting, playbook development, and security posture improvement.

A simplified three-year financial model for our 500 GB/day scenario would show the DIY stack with a lower initial annual software cost (zero, excluding support subscriptions), but with a steep and recurring personnel curve. Sentinel's costs are more predictable and linear, heavily tied to ingestion volume. The crossover point where total invested costs favor one model over the other is highly sensitive to your organization's fully burdened labor rates and existing Azure commitment (e.g., Azure Arc for on-premises sources).

I am particularly interested in community data points on a few specific areas:
* Real-world figures for the **FTE allocation** required to keep a production-grade ELK+Wazuh stack operational, including alert tuning and content updates.
* Experiences with **long-term storage** strategies for Elasticsearch data beyond 90 days that balance cost and accessibility for retroactive hunting.
* Detailed breakdowns of **Sentinel costs for non-Azure data sources**, especially from hybrid or multi-cloud environments, after the first few free GB per source type.