Use case · AI SaaS
AI SaaS cost estimator
AI-native SaaS products must connect unit economics to model bills. Investors ask for gross margin after inference, not just ARR. This estimator frames COGS as token math you can defend in diligence.
You will see how different features—summaries, embeddings, agents—stack into a monthly burn picture, and how to plan infrastructure alongside model APIs.
Token usage patterns by feature
Onboarding wizards might be one-shot high-token flows, while daily active usage is smaller but more frequent. Break COGS down by feature flag to see which SKUs subsidize others.
Example SaaS workloads
| Scenario | Prompt tokens | Output tokens | Model (est.) | Cost / request |
|---|---|---|---|---|
| Meeting notes (per user) | 6500 | 900 | GPT-4o | $0.0253 |
| Inline rewrite | 400 | 200 | GPT-4o mini | $0.0002 |
| Insight card | 1200 | 350 | Claude 3.5 Sonnet | $0.0089 |
Figures use rates from config/models.php; confirm against your provider before billing decisions.
Scaling examples
-
50k MAU blended
5,000 heavy inference calls per weekday.
- Per request
- $0.0080
- Monthly (5000 req/day × 22 days)
- $880.00
Infrastructure considerations
Vector databases, background workers, and observability pipelines all carry cost. Attribute shared infrastructure proportionally in planning so pricing teams do not ignore it.
Model recommendations
Offer a default fast tier with upgrade paths. Large customers may pay for premium models—encode that in packaging rather than absorbing it silently.
Optimization recommendations
Cache repeated analyses, deduplicate team-wide prompts, and use asynchronous processing for non-interactive features to reduce synchronous token spikes.
ROI examples
If AI features lift net revenue retention by five points, you can fund higher model spend—model that uplift conservatively with cohort studies.
API budget planning
Set hard monthly caps in vendor consoles, mirror them in internal feature flags, and review variance versus forecast every sprint during early access.
FAQ: AI SaaS inference costs
Short answers mirror the structured data on this page for search engines and readers.
- How do free trials affect AI COGS?
- They concentrate burst traffic. Throttle trial users or offer limited premium model access.
- Should COGS include embeddings?
- Yes, if you regenerate them frequently. Treat embedding refresh as its own line item.
- What is a healthy inference margin?
- It varies by category—investors often want clarity more than a magic percentage. Show thoughtful routing and caching.
- How do I forecast multi-model routing?
- Use historical route percentages per environment and stress-test if the cheap route share drops.