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An API, or application programming interface, is a set of rules that lets one program request information or services from another. Like a power outlet, it offers a standard connection: you plug in correctly and get what you need without knowing how the system behind it works.

An API is a set of rules that lets one piece of software ask another for information or actions. For example, a weather app may use an API to request today’s forecast from a weather service, then display the result for you.

Use a hosted authentication provider. With only two people, your scarce time is better spent on the product that differentiates you, not on password storage, session handling, account recovery, multifactor authentication, and abuse defenses. A reputable provider gives you a safer baseline, a faster launch, and fewer opportunities for a catastrophic security mistake. You can revisit the decision if your scale or requirements change. The strongest argument against this is vendor lock-in: authentication touches every user and many core flows, so migrating identities, credentials, and integrations later can be costly and disruptive. If the provider’s pricing or terms become unfavorable, you may have little leverage precisely when your business depends on it.

Use a reputable hosted authentication provider. For a two-person startup, authentication is security-critical but rarely differentiating, and building it well requires ongoing work across password storage, account recovery, MFA, session management, abuse prevention, compliance, monitoring, and incident response. A provider lets you launch faster and reduces the chance that a subtle implementation flaw compromises users. Choose one with standards-based integrations and an export path to limit lock-in. The strongest argument against this recommendation is vendor dependency: pricing can rise sharply with user growth, and migrating authentication later can be difficult because password hashes, MFA credentials, sessions, and identity links may not transfer cleanly.

Your AI bill is hard to forecast because it behaves less like a software subscription and more like electricity. Many services charge for each unit of text processed, so costs rise with customer demand, document length, and the number of attempts needed to get a useful answer. A small change in a product feature or employee habit can multiply usage. Second, the price of a model is only part of the expense. Data preparation, security reviews, testing, human oversight, and integration work often arrive separately and vary by use case. Third, the market keeps moving: vendors change prices and limits, while newer models may alter both capability and consumption. Success creates uncertainty, since a cheap pilot can become expensive when widely adopted. Treat AI as a portfolio of business investments, not a technology budget. For each use case, measure cost per task alongside quality and revenue or time saved. Start with capped pilots, require ownership, and tag spending by product and team. Set budgets, usage alerts, and approval thresholds before scaling. Compare models, use cheaper options for routine work, and avoid commitments until demand is stable. Review forecasts monthly using low, expected, and high usage scenarios, and stop projects whose unit economics do not improve.

AI spending is hard to predict because usage, pricing, and implementation costs can all change quickly. Unlike traditional software, many AI services charge according to consumption: the number of questions asked, documents processed, images created, or computing resources used. A successful pilot can therefore become much more expensive when thousands of employees or customers adopt it. A second cause is uncertainty about which technology will be needed. Different AI models vary greatly in price, speed, and quality, while vendors frequently release new versions and revise commercial terms. Teams may also discover that a cheaper model is adequate for routine work but that sensitive or complex tasks require more costly systems. Third, the model itself is only part of the bill. Reliable AI often requires data preparation, system integration, security controls, testing, human review, monitoring, training, and legal or compliance work. These supporting costs are easy to underestimate, particularly when projects move from demonstration to production. Executives can improve control by funding AI in stages, with clear limits and decision points. Require each project to define expected usage, business value, accuracy needs, and total operating cost. Track spending by team and use case, set alerts and quotas, and review actual costs monthly. Negotiate vendor protections, avoid unnecessary dependence on one provider, and maintain contingency budgets for rapid growth, compliance changes, or unexpected technical work.