Before you wire an AI API into production, ask structured questions — not "which model won a benchmark last Tuesday." What questions should I ask before choosing an AI API? This checklist is the short list investors and security reviewers will ask anyway.
Pricing and predictability
1. Is billing per token, per request, or flat monthly?
2. What is our fully loaded token estimate? (true cost guide)
3. What happens to our bill if traffic doubles?
4. Are there minimum commits or egress fees?
If answers scare you, read How to choose an AI API provider.
Models and capabilities
5. Which models do we need on day one vs later?
6. Is streaming supported the way our UI expects? (SSE vs WebSockets)
7. Are tool calling / JSON modes documented and stable?
Daymora exposes GPT-5, Claude Sonnet 4, and Gemini 2.5 Pro on one plan — see pricing.
Limits and reliability
8. What are RPM/TPM or fair-use ceilings? (rate limits guide)
9. Is there a public status page and SLA?
10. How do 429s behave — retry-after headers?
Privacy and compliance
11. Is customer data used for training by default?
12. Retention period for prompts and logs?
13. Subprocessors and regions? (what happens to your data)
Portability
14. How much code assumes vendor-specific JSON?
15. Can we migrate without rewriting the app? (migration guide)
Operations
16. How do we rotate keys? (API key security)
17. What does support look like at our price tier?
18. How do we test without burning production budget? (test without paying)
Scorecard (simple)
Rate each provider 1–5 on: predictable cost, integration time, model fit, privacy fit, exit cost. Highest total wins for v1 — not the flashiest model card.
Summary
Choosing an AI API is choosing a billing relationship and a data processor. Ask about money, limits, and logs before you ask about leaderboard scores. Your future self doing SOC2 will thank you.