AI Accounting & CFO
Accounting & CFO Services for AI Startups
GPU and inference cost accounting, R&D credits on compute and engineering spend, gross margin after cloud costs, and AI-specific unit economics that investors ask about.
AI startups burn cash in ways traditional SaaS doesn't. Compute costs scale with usage — sometimes faster than revenue. Gross margin after cloud costs is the number every investor asks about. StartupCFO runs AI-company books that separate training, inference, and operating compute, maximize your R&D tax credit on engineering and compute spend, and give you a real gross margin story for your next round.
What's Hard About AI Finance
The ai-specific challenges that generic bookkeeping services miss.
GPU and inference cost allocation
Training compute, inference compute, and platform overhead separated so gross margin reflects per-query economics, not just platform cost.
R&D tax credits on compute and wages
Engineering wages, cloud compute for model development, and contractor research costs all qualify. Up to $500K/yr in payroll tax offset.
Capitalize vs. expense model development
Internal-use software (ASC 350-40) vs. R&D expense. We apply consistent policy and document it for audit.
Revenue recognition on usage-based pricing
Token, query, or seat-based billing recognized correctly under ASC 606, with deferred revenue schedules that hold up in diligence.
Fundraising at pre-revenue or early revenue
Investors evaluate burn rate, training-cost efficiency, and time-to-insights. We model fundraise scenarios tied to compute spend.
How StartupCFO Helps
One integrated team — accountant, CPA, and fractional CFO — running the right ai playbook.
- Monthly close with compute costs allocated training/inference/ops
- R&D tax credit study maximized on engineering and compute spend
- Gross margin reporting net of cloud and inference costs
- Usage-based revenue recognition (ASC 606) for tokens, queries, seats
- Runway and burn modeling tied to compute assumptions
- Fractional CFO support for fundraising, pricing, and cap-table strategy
AI startup metrics we track
- Gross margin after GPU/cloud costs
- Cost per query, per token, per active user
- Training spend vs. inference spend over time
- R&D tax credit eligible spend (rolling estimate)
- CAC, LTV, and compute-adjusted payback
- Runway under 3 compute-growth scenarios
Frequently Asked Questions
Can we claim R&D tax credits on cloud and GPU spend?
Yes — US-based AI companies can claim R&D credits against payroll tax, up to $500,000 per year. Qualifying spend includes engineering wages (US), contractor research (65% of cost), and cloud compute used in model development. We run the full study and file Form 6765 with your return.
How do you handle model training costs — capitalize or expense?
For most pre-product-market-fit AI startups, training compute is R&D expense. Post-GA, internal-use software rules (ASC 350-40) may apply for platform development. We set a consistent policy, document it, and stick to it so auditors and investors see the same story.
Do you track per-query and per-token economics?
Yes. ClariFi pulls usage data from your product analytics or billing system and matches it to compute spend from AWS, GCP, or Azure — so you see contribution margin per query, per user, and per cohort.
What metrics do AI investors care about most?
Gross margin after compute (not just GAAP gross margin), training-cost efficiency (cost per capability unit), usage growth vs. spend, and runway under several compute-growth scenarios. We build every board pack around these.
Ready for AI-Native Accounting?
Book a free consultation and we'll walk through how we'd run your ai books, taxes, and CFO support — live within 48 hours.
