FinOps vs. Service Economics.
Different Problems. Different Tools.
FinOps tools optimize cloud infrastructure spend. Service Economics Intelligence tells you what services actually cost to deliver. Here's how they differ.
The Core Difference
FinOps and Service Economics Intelligence solve different problems for different teams.
FinOps Tools
Cloud cost management
Answers: "How much are we spending on cloud infrastructure and how can we reduce it?"
- AWS, Azure, GCP cost optimization
- Reserved instance recommendations
- Resource tagging and allocation
- Client profitability
- Service delivery margins
Service Economics Intelligence
Delivery cost management
Answers: "What does it actually cost to deliver services to this client?"
- Human + AI + tool cost unification
- Client and engagement attribution
- Service-level margin calculation
- Blended delivery composition
- Pricing decision support
Key Differences
| Category | FinOps Tools | DigitalCore |
|---|---|---|
| Primary Focus | Optimizing cloud infrastructure spend | Understanding service delivery economics |
| Cost Attribution | To resources, tags, accounts | To clients, engagements, services |
| AI Cost Handling | Often treated as another cloud service | Tracked as delivery cost alongside human labor |
| Revenue Context | Not included (cost-only view) | Full P&L with revenue and margin |
| Decision Support | "How do we reduce cloud spend?" | "Is this engagement profitable?" |
Why AI Cost Management Is Different
FinOps treats AI as infrastructure
Most FinOps tools treat AI/LLM costs as another cloud service: something to monitor, tag, and optimize. They can tell you total spend, but not which client drove it.
Service Economics treats AI as delivery cost
AI costs behave like variable labor, not infrastructure. They should sit alongside human labor in your service cost structure, attributed to the work that caused them.
The key question: Is your AI spend COGS (cost of delivering services) or overhead? If it's COGS, you need service economics, not just FinOps.
Which Do You Need?
You run cloud infrastructure for products
→ FinOps tools are your primary need
You deliver services using AI and humans
→ Service Economics Intelligence is your primary need
You do both
→ FinOps for infrastructure, DigitalCore for delivery economics
Feature Comparison
See exactly where each approach excels.
| Capability | DigitalCore | FinOps Tools |
|---|---|---|
| Core Purpose | ||
| Service delivery cost tracking★ Key Differentiator | Native | No |
| Client/engagement attribution | Native | Limited |
| Human + AI + tool cost unification★ Key Differentiator | Native | No |
| Service-level margin calculation | Native | No |
| Cloud infrastructure cost tracking | Limited | Native |
| AI Cost Management | ||
| Token-level cost monitoring | Native | Limited |
| AI cost per client/project★ Key Differentiator | Native | No |
| Blended delivery composition view | Native | No |
| AI COGS classification | Native | No |
| Multi-model cost tracking | Native | Limited |
| Attribution & Allocation | ||
| Costs attributed to engagements★ Key Differentiator | Native | No |
| Costs attributed to teams | Native | Native |
| Costs attributed to services | Native | Limited |
| Chargeback models for AI | Native | Limited |
| Financial Integration | ||
| Service P&L generation | Native | No |
| Revenue tracking | Native | No |
| Budget vs. actual variance | Native | Native |
| Margin alerts | Native | No |
| Service Context | ||
| Engagement-centric architecture★ Key Differentiator | Native | No |
| SLA/KPI tracking | Native | No |
| Capacity planning | Native | No |
| Customer relationship context | Native | No |