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An API is a tool that lets different software programs talk to each other and share information. Think of it as a messenger that takes your request, delivers it to another program, and brings back the answer you need. APIs make it possible for apps to work together smoothly.

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.

You should use a hosted authentication provider. With two people, you cannot afford the engineering time to build authentication securely. Hosted providers handle password hashing, token management, breach response, and compliance standards that would take you months to implement correctly. They're also cheaper than paying developers to maintain custom auth code over time. The strongest argument against this is that authentication is core to your product's security model. By delegating it, you depend on a third party's infrastructure, pricing decisions, and policy changes. If your provider raises rates significantly or experiences an outage, you have limited recourse. You also lose direct control over user data and authentication flows, which matters for compliance-sensitive applications. Building internally means you own your security destiny, even if it's more work initially.

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 company's AI spending is difficult to predict due to three fundamental factors that distinguish artificial intelligence from traditional technology investments. First, AI projects have highly variable timelines. While conventional software follows established development patterns, AI requires experimentation and iteration. Models may underperform unexpectedly, requiring substantial additional investment to improve accuracy. You cannot know upfront how many iterations you'll need before achieving acceptable results. Second, computational costs fluctuate based on factors beyond your control. As your models scale or process larger datasets, infrastructure expenses can spike dramatically. Cloud providers adjust pricing, and newer hardware becomes available, creating constant pressure to upgrade systems. Unlike software licensing with fixed contracts, these costs remain dynamic and difficult to forecast. Third, talent retention significantly impacts budgets. AI specialists command premium salaries in a highly competitive market, and losing key personnel means retraining costs and project delays. Unexpected departures force accelerated hiring at premium rates, inflating expenses unpredictably. To manage these uncertainties, adopt a staged funding approach rather than committing to fixed annual budgets. Build contingency reserves of 20-30 percent into AI initiatives. Establish clear performance milestones that trigger go-or-no-go decisions before committing additional resources. Finally, partner with external consultants to validate project feasibility early, reducing the risk of expensive course corrections later.

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.