economics

Calculate the current unit economics for your AI product.

Before you start

Ask the founder:

"What do you think your gross margin is on each action? Take a guess."

Wait for the answer. Write it down. Then run the calculation. Founders almost always overestimate margin. Showing the real number after they've committed to a guess lands harder.

If they say "I have no idea" — proceed. But get the guess first if there is one.

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Current model pricing (June 2026)

Anthropic Claude

Model	Input $/MTok	Output $/MTok	Cache read $/MTok	Batch (50% off)
Opus 4.8	$5.00	$25.00	$0.50	$2.50 / $12.50
Sonnet 4.6	$3.00	$15.00	$0.30	$1.50 / $7.50
Haiku 4.5	$1.00	$5.00	$0.10	$0.50 / $2.50

Cache write cost: 1.25× input rate for 5-min TTL, 2.0× for 1-hr TTL.

OpenAI

Model	Input $/MTok	Output $/MTok
GPT-4.1	$2.00	$8.00
GPT-4o	$2.50	$10.00

Google Gemini

Model	Input $/MTok	Output $/MTok
Gemini 2.5 Pro	$1.00	$10.00
Gemini 2.5 Flash	$0.30	$2.50

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Steps

1. Get pricing from the codebase

grep -rn "CREDIT\|credit_cost\|COST\|FREE_CREDITS\|signup_bonus\|price\|PRICE" --include="*.py" --include="*.ts" -i

Find:

• How much users pay (e.g. $5 = 100 credits → $0.05/credit)
• Credits per action (generation, image, regen, etc.)
• Free credits on signup

2. Estimate tokens per action

Read the actual prompt files if they exist. Otherwise use these baselines:

Action type	Input tokens	Output tokens
Short generation (tweet, subject line)	500–1,000	100–200
Medium generation (email, summary)	1,000–3,000	300–800
Long generation (article, report)	2,000–6,000	800–2,000
Classification / extraction	500–2,000	50–150
Chat turn (with history)	2,000–8,000	200–500

3. Calculate cost per action

API cost = (input_tokens / 1,000,000 × input_price)
         + (output_tokens / 1,000,000 × output_price)

Revenue  = credits_charged × price_per_credit
Margin   = (revenue - api_cost) / revenue × 100

4. Calculate cache impact (if using prompt caching)

Cache only helps when you have a large repeated prefix (system prompt, document context, few-shot examples).

Standard cost     = input_tokens / 1M × input_price
Cached cost       = cached_tokens / 1M × cache_read_price
                  + uncached_tokens / 1M × input_price

Savings per call  = standard_cost - cached_cost
Break-even calls  = cache_write_cost / savings_per_call

Example with Sonnet 4.6 and a 10,000-token system prompt:

• Standard: 10,000 / 1M × $3.00 = $0.030 per call
• Cache read: 10,000 / 1M × $0.30 = $0.003 per call
• Cache write (5min): 10,000 / 1M × $3.75 = $0.0375
• Break-even: $0.0375 / $0.027 savings = 1.4 calls

Any repeated system prompt over ~2,000 tokens with more than 2 calls per session: cache it.

5. Check batch eligibility

Anthropic's Batch API is 50% off all models. Use it for:

• Bulk processing (document analysis, content moderation, bulk generation)
• Anything that doesn't need a real-time response (under 24hr turnaround)
• Background jobs, nightly processing, bulk exports

If more than 20% of your API spend is on non-real-time work, batch processing alone could halve that portion.

6. Output the table

UNIT ECONOMICS — [your product]
════════════════════════════════════════════════════════════════
USER PRICING
  [price] = [credits] credits → $X.XX per credit
  Signup bonus: [N] free credits

ACTION ECONOMICS
────────────────────────────────────────────────────────────────
Action              Credits   Revenue   API Cost   Margin
────────────────────────────────────────────────────────────────
[action name]         [N]     $X.XX     $X.XXX     XX%
[action name]         [N]     $X.XX     $X.XXX     XX%
────────────────────────────────────────────────────────────────

CACHE SAVINGS (if applicable)
  System prompt tokens: [N]
  Savings per call: $X.XXX
  Monthly savings at [volume] calls: $XXX

FREE TIER DAMAGE (N signup credits)
  Conservative user:   costs you ~$X.XX
  Typical user:        costs you ~$X.XX
  Heavy user:          costs you ~$X.XX

PAYBACK POINT
  One paying user covers ~X non-converting free users
════════════════════════════════════════════════════════════════

7. Flag anything concerning

Red flags:

• Any action with margin below 50% — flag it prominently
• Free tier cost above $1 per signup — flag it
• Large repeated system prompt with no caching — calculate the monthly savings they're leaving on the table
• Non-real-time work running on live API instead of batch — flag the cost delta

Margin benchmarks (2026):

• 70% gross margin — healthy for an AI product

• 60–70% — acceptable, industry average for AI-augmented SaaS
• 50–60% — watch this closely, leaves little room for infra and support costs
• <50% — pricing problem or model choice problem, fix before scaling

8. Pricing sensitivity (if asked)

Show margins at ±20% credit price. Helps decide whether to reprice without rebuilding the whole spreadsheet.

Also show the "upgrade model" scenario: what margin looks like if they move one tier up or down (e.g. Sonnet → Haiku, GPT-4.1 → Gemini 2.5 Flash). The cheapest model that meets quality bar is often not the one they're using.