The Real Cost Is Never Just Build Cost
Most founders estimate AI projects using only initial development quotes. That's incomplete. The actual cost includes model usage, data operations, observability, and ongoing iteration.
Cost Buckets to Track
1. Build and Integration
- Product and engineering time
- Data pipeline setup
- Prompt/model integration and testing
2. Runtime and Infrastructure
- API usage or model serving cost
- Storage and vector database costs
- Monitoring and logging overhead
3. Operations and Maintenance
- Quality evaluation and regression testing
- Prompt/model updates
- Incident handling and reliability engineering
4. Governance and Compliance
- Security controls and audits
- Data retention/privacy compliance
- Vendor risk reviews
Practical Advice
- Start with one high-ROI workflow
- Define success metrics before implementation
- Review cost per successful outcome, not per API call
If you want, we can help you model realistic AI cost scenarios before committing budget.