The annual vendor price adjustment email has arrived, and the Cloud One platform increase for 2025 is notably above the typical CPI adjustment we've seen historically. For our data infrastructure, where we instrumented Cloud One Workload Security across several thousand cloud compute instances for vulnerability management, this has a material impact on our operational security budget.
Our initial back-of-the-envelope calculation, based on the per-instance pricing tiers and our committed use discounts, indicates a year-over-year cost increase of approximately 18-22% for the same coverage. This forces a re-evaluation of the total cost of ownership, especially when considering the data pipeline we've built around its findings.
Specifically, our ETL process ingests Cloud One findings into BigQuery for correlation with asset metadata and deployment pipelines. The value proposition tightens when the core data source becomes significantly more expensive. We now need to model:
* **Alternative architectures:** Could a more modular approach (e.g., separate agent for vulnerability assessment, another for runtime protection) from different vendors be more cost-effective, even with increased integration complexity?
* **Data ingestion efficiency:** Are we paying for findings from ephemeral, low-risk development instances that could be excluded via more aggressive tagging policies without materially increasing risk?
* **Benchmark against native cloud services:** A renewed analysis of AWS GuardDuty, Azure Defender, and Google Cloud Security Command Center is warranted, factoring in the engineering effort to normalize their alert schemas into our existing BigQuery models.
The key question for this community is whether others are conducting similar analyses. I'm particularly interested in real-world data points on:
* The actual percentage increase you're seeing on your invoices, and how it varies by component (Workload Security vs. Container Security vs. File Storage Security).
* Any successful negotiation levers or revised commitment tiers you've accessed.
* Engineering efforts required to switch or supplement the data feed, especially concerning schema stability and alert fidelity.
I'll start by sharing a snippet of the cost projection query we ran against our usage data. This doesn't include the new rates, but outlines the model.
```sql
-- Simplified model for instance coverage cost projection
WITH monthly_instance_tiers AS (
SELECT
DATE_TRUNC(month, DATE) AS month,
COUNT(DISTINCT instance_id) AS total_instances,
-- Tiered pricing logic (old rates)
CASE
WHEN COUNT(DISTINCT instance_id) > 5000 THEN 'tier_enterprise'
WHEN COUNT(DISTINCT instance_id) > 1000 THEN 'tier_business'
ELSE 'tier_standard'
END AS pricing_tier
FROM `prod_sec_dataset.cloud_one_inventory`
WHERE agent_status = 'protected'
GROUP BY 1
)
SELECT
month,
pricing_tier,
total_instances,
-- Placeholder for cost function: f(tier, instances)
total_instances * 1.0 AS projected_cost_old_rate -- Replace with actual rate calc
FROM monthly_instance_tiers
ORDER BY month DESC;
```
The business case for a consolidated security data platform hinges on its cost predictability. Shifts of this magnitude necessitate a rigorous, data-driven review.
--DC
data is the product