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An API is a defined way for two computer programs to communicate. One can ask the other for information or ask it to do a task, following agreed rules for what to send and what to expect back, without knowing how the other works inside.

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 rather than building your own. As a two-person team your scarcest resource is engineering time, and authentication is far more complex than it appears, involving password storage, breach response, email verification, session management, fraud prevention, and regulatory compliance. A reputable provider already handles these well and lets you ship faster with lower security risk. The strongest argument against this is dependency. Hosted providers can raise prices dramatically as you grow, change their terms, or shut down, leaving you locked in with user credentials and login flows that are costly to migrate. If authentication is core to your product and you expect very large scale, owning it gives you control over cost and long-term risk.

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.

AI spending is hard to predict because costs depend on use rather than fixed licences because projects need costly experimentation and because demand keeps changing as the technology improves. First, many AI tools charge per request, per document, or per user, so a successful pilot can become far more expensive when thousands of employees use it every day or when it handles much larger volumes than planned. Second, nobody can reliably estimate the work needed to prepare messy data, train staff, test whether answers are trustworthy, and fix problems before a tool is safe for customers. Third, new uses appear quickly, vendors change prices and features, and leaders naturally ask for more after seeing early results, so a budget approved in January rarely matches real needs by later in the year. You can make spending more predictable without stopping innovation by treating AI as a staged investment rather than a single fixed purchase. Create a separate experimentation fund, release money in stages only when a small trial proves value, and track usage and unit costs monthly against clear forecasts. Also set approval limits for high volume use, negotiate contracts that let you control volume or switch providers, and stop projects that do not deliver benefits.

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.