Pricing Models and Build vs. Buy

A flat monthly fee per user. Example: $50/user/month. Simple to budget. Common for productivity tools (AI writing assistants, meeting notetakers). The risk: you pay for seats whether people use them or not. Works well when adoption is high and usage is roughly uniform across users. Breaks down when usage is highly variable (power users vs. light users) — you end up subsidizing inactive seats.

You pay per action: per API call, per message, per document processed, per minute of audio transcribed. Example: $0.002 per 1,000 tokens processed. Scales directly with usage — you never pay for idle capacity. The risk: unpredictable costs if usage spikes. A single batch job or viral moment can generate a $2,000 bill overnight if you don't have usage caps set. Always set spending limits on consumption-priced tools.

One fixed price for unlimited access — or access up to a very high limit. Example: $299/month for unlimited AI responses. Predictable budgeting. Best when you have high, consistent volume. The risk: vendors often use 'fair use' clauses to throttle heavy users or quietly downgrade model quality at high volumes. Read the fine print on what 'unlimited' actually means — it often has asterisks.

Customer service chatbot, email drafting, meeting summaries, document Q&A — these are solved problems. Dozens of mature tools exist. Building your own would take months and cost hundreds of thousands of dollars to reach feature parity with a $200/month SaaS product. Buy. Focus your engineering resources on your actual differentiated problems.

Getting to value in 2 weeks vs. 6 months is often the right tradeoff even if the vendor solution is imperfect. Especially true for early-stage experiments where you're not sure the use case will stick. Use vendor tools to validate, then consider building once you've proven the workflow.

If your workflow has domain-specific requirements no vendor has solved, your data structure is proprietary, or the competitive advantage depends on the AI being yours — building is justified. Examples: a proprietary pricing model, a medical diagnosis assistant trained on your specific patient population, a recommendation engine built on your unique product catalog.

If a tool costs $0.01 per transaction and you process 10 million transactions/month, that's $100k/month. At that scale, building on top of a foundation model API directly (and optimizing your usage) often drops costs by 80-90%. The build vs. buy crossover point is usually somewhere between $5k and $50k/month in vendor fees.

The most common winning strategy: Buy a vendor tool to validate the workflow and prove ROI. Once you've confirmed it works and the volume justifies it, build a leaner custom solution on top of foundation model APIs (OpenAI, Anthropic, etc.). You get speed-to-market from the vendor and cost efficiency from your own implementation. This is the path most successful mid-size companies take.