Shared services — such as BigQuery, Datadog, Kubernetes, and CI/CD — represent a significant portion of cloud spending in most organizations. Without proper allocation, these costs lead to inefficiencies and misaligned accountability across teams.
This article outlines three common approaches to allocating shared costs and their implications on cost control and operational maturity.
Option 1: Centralized Allocation to Platform or Infra Team
In this approach, all shared costs are assigned to a central cost center, often the platform or DevOps team.
Implications:
- Application teams perceive shared services as free.
- Overconsumption occurs without visibility into impact.
- When cost control becomes urgent, application teams often lack the operational knowledge to reduce usage quickly.
- Optimization becomes reactive and time-consuming.
Example:
BigQuery slots are purchased centrally. Teams query without restriction. Once the platform team needs to optimize, usage patterns are deeply embedded, and reducing cost requires weeks of coordination and effort.
Option 2: Static Percentage-Based Allocation
Here, shared costs are divided equally (or by arbitrary ratios) across application teams. For example, if there are three services, each is allocated 33% of Datadog or BigQuery costs.
Implications:
- Over-consuming teams are undercharged.
- Efficient teams subsidize inefficient ones.
- Cost signals are diluted; teams have no direct feedback loop on their usage.
- Static allocation doesn’t adapt to changing workloads.
Limitation:
Cloud billing tools like AWS Cost Explorer or GCP Billing Reports don’t provide sufficient granularity to measure actual usage per team, especially for shared or abstracted services.
Option 3: Dynamic Allocation Based on Measured Usage
This method uses actual usage metrics — such as logs, API calls, or namespace-level metrics to allocate shared costs proportionally.
Example:
Datadog API request logs can be grouped by service or team. The ingestion cost can then be allocated based on real volume per team.
Implications:
- Teams are charged based on their actual usage.
- Cost accountability increases.
- Optimization becomes part of regular operations.
- Overuse patterns are visible early.
Challenges:
- Requires detailed telemetry and tagging.
- Manual implementation is complex and rarely sustainable.
- Tooling is necessary for accuracy and consistency.
Advanced Layer: Align Shared Cost Allocation with Business Metrics
Dynamic allocation creates a foundation for further analysis, such as comparing usage to business outcomes — e.g., revenue, number of users, or traffic served.
Use case:
If a team consumes 50% of BigQuery capacity but is responsible for 80% of revenue, high spend may be justified. Conversely, a high-cost, low-revenue service may indicate poor ROI.
This enables:
- Prioritization of optimization efforts.
- Cost-to-value mapping for shared infrastructure.
- More informed budgeting discussions.
Summary of Trade-Offs
| Method | Simplicity | Accuracy | Accountability | Operational Cost |
|---|---|---|---|---|
| Centralized (Platform Team) | High | Low | Low | Low |
| Static % Allocation | Medium | Low | Low | Medium |
| Dynamic Allocation by Usage | Low | High | High | High |
Conclusion
Shared cost allocation is not optional in a mature FinOps practice. While centralized or static allocation may be simpler to implement, they introduce blind spots and inefficiencies that affect cost control.
Dynamic, usage-based allocation provides the necessary granularity to align cost with consumption. Though it requires tooling and effort, it’s a prerequisite for sustainable optimization and accurate cost accountability.