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 (bookkeeper, 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. Your finance team reconciles usage data from your product analytics or billing system against compute spend from AWS, GCP, or Azure and brings it into ClariFi, 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 handle your ai books, taxes, and CFO support. Once you start, your finance team is live within 2 business days.
