Revenue minus cost of goods sold, expressed as a percentage. For SaaS, this is typically 70-85%.
(Revenue - COGS) / Revenue × 100
Higher is better · Unit: percentage
How to Improve
Invest in engineering efficiency to reduce per‑customer compute and storage costs. Build self‑service tools that reduce the need for professional services. Migrate to more cost‑effective cloud infrastructure or negotiate enterprise agreements. Automate quality assurance and deployment to reduce engineering overhead. Focus on product‑led delivery models that scale without linear cost increases.
Ehsan's Analysis
AI/ML gross margins are the most misunderstood metric in the current hype cycle. Investors see "AI SaaS" and expect 75%+ gross margins. Reality: inference costs (the price of running an LLM per query) make most AI companies look more like cloud infrastructure (50-60% margins) than software (75%+). OpenAI reportedly has gross margins of 40-50% at current pricing. Companies building on top of OpenAI/Anthropic APIs have a margin ceiling of 30-50% depending on markup — because model costs are their COGS. The AI companies achieving 70%+ gross margins either: (1) use smaller, cheaper models fine-tuned for specific tasks (Harvey.ai for legal), (2) cache frequent queries to reduce inference costs, or (3) charge enough premium that inference costs become a small percentage. The first approach is the most sustainable — a purpose-built 7B parameter model costs 1/100th per query versus GPT-4, and for domain-specific tasks can match quality. Your gross margin strategy IS your model strategy.
EJ
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council
Frequently Asked Questions
What is a good Gross Margin for AI/ML companies at Series A stage?
The median Gross Margin for AI/ML companies at the Series A stage is 72.7%. Top‑quartile companies (75th percentile) significantly outperform this baseline. The most important factor is consistent improvement over time rather than hitting any single target number.
How does Gross Margin differ by company stage in AI/ML?
Gross Margin typically improves as AI/ML companies mature from seed through growth stage. Earlier‑stage companies should benchmark against stage‑appropriate peers rather than comparing themselves to mature companies.
How often should AI/ML companies measure Gross Margin?
AI/ML companies at the Series A stage should track Gross Margin monthly with quarterly deep‑dive analysis. Set up automated dashboards and alerts for significant deviations from your baseline.
What factors most impact Gross Margin in the AI/ML sector?
In AI/ML, the primary factors impacting Gross Margin include product‑market fit maturity, competitive landscape intensity, customer segmentation strategy, pricing optimization, and operational efficiency. Series A‑stage companies should focus on the one or two highest‑leverage factors rather than trying to optimize everything simultaneously.
How does Gross Margin for AI/ML compare to cross‑industry benchmarks?
AI/ML Gross Margin benchmarks can differ significantly from cross‑industry averages due to factors specific to the AI/ML vertical including customer acquisition dynamics, competitive intensity, and typical deal sizes. Always compare against industry‑specific benchmarks rather than broad averages for meaningful insights.