AI projects rarely blow up because models fail. They blow up because finance teams discover a runaway bill, legal flags a compliance gap, or operations realize the team cannot support what it launched. Use this cost map to plan realistically and protect your runway.
Cost Bucket 1: People
Salaries are still the biggest line item. AI adds new roles beyond traditional engineering.
- AI operators: maintain prompts, evaluation datasets, and human-in-the-loop processes.
- FinOps analysts: monitor usage, forecast spend, negotiate vendor tiers.
- Data governance specialists: ensure privacy, labeling quality, and compliance.
- Change management: trainers, support staff, and documentation writers.
Cost Bucket 2: Infrastructure
Cloud GPUs and vector databases are obvious. Hidden infra costs include:
Evaluation pipelines
Scheduled jobs running prompts across datasets consume tokens and compute.
Observability stack
Tracing, logging, and synthetic monitoring for AI outputs.
Caching layers
Reducing inference cost requires managed caches and invalidation strategies.
Dev environments
Separate sandboxes for experimentation to avoid contaminating production data.
Cost Bucket 3: Data
Data quality is the tax we pay for intelligence. Expect to invest in:
- Labeling vendors or internal labeling squads.
- Annotation tooling and QA workflows.
- Data retention policies and deletion tooling for compliance.
- Storage and egress fees for moving large datasets between vendors.
Cost Bucket 4: Risk & Compliance
AI expands the surface area for audits and liabilities.
- Legal review of data usage rights, especially with customer-provided content.
- Security assessments for third-party AI vendors and plugins.
- Insurance premiums for errors & omissions may increase.
- Accessibility audits to ensure AI-generated content meets standards.
3-Year Budget Model
| Category | Year 1 | Year 2 | Year 3 |
|---|---|---|---|
| Talent | $420k (core team) | $540k (adds AI ops & governance) | $600k (retention + enablement) |
| Infrastructure | $240k (pilot workloads) | $360k (24/7 workloads + eval) | $420k (optimization + redundancy) |
| Data | $160k (initial labeling) | $200k (continuous improvement) | $220k (new markets, languages) |
| Compliance & Risk | $80k (policy + initial audits) | $120k (certifications, insurance) | $150k (international expansion) |
Cost Control Checklist
- Tag every AI workload with team, feature, and environment metadata.
- Establish usage budgets and alert thresholds per squad.
- Negotiate committed-use discounts with cloud and model vendors.
- Run quarterly postmortems on spend spikes; share learnings org-wide.
- Automate deprovisioning for unused experiments and stale datasets.
Final Insight
AI is neither cheap nor expensive—it is variable. The winners treat cost as a product surface: they instrument it, iterate on it, and communicate transparently with stakeholders. Do that and AI will remain a strategic asset instead of a spreadsheet surprise.