Anthropic Claude Sonnet 5 vs OpenAI GPT 5.6 Sol

An API is a set of rules that lets two computer programs talk to each other. One program offers certain services or information, and the API defines exactly how another program can request them—what to ask for, how to ask, and what kind of answer to expect back.

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 (like Auth0, Clerk, Supabase Auth, or Firebase Auth). For a two-person team, engineering time is the scarcest resource you have, and authentication is a solved problem with enormous security surface area—password resets, session management, MFA, OAuth flows, token security, compliance requirements. Building this yourselves means spending weeks on undifferentiated infrastructure instead of your actual product, and any subtle bug in a homegrown auth system can lead to a catastrophic breach that kills a young company's reputation instantly. Hosted providers have dedicated security teams, handle edge cases you haven't thought of, and let you ship auth in days rather than weeks. The strongest counterargument: hosted auth creates a critical dependency and recurring cost that scales with users, and migrating away later—once you're deeply integrated and have thousands of accounts—can be far more painful and risky than if you'd built on flexible, self-owned infrastructure from the start.

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 difficult to forecast for reasons that differ fundamentally from traditional IT costs, and it's worth understanding why before trying to budget around it. First, usage-based pricing means costs scale with demand rather than following a fixed subscription. Unlike a software license with a predictable annual fee, AI tools often charge per query or per token processed, so a successful product feature or a popular internal tool can quietly multiply your bill as adoption grows. Second, the technology itself is a moving target. Vendors regularly release new models with different pricing, and teams frequently switch or upgrade to stay competitive, which resets cost assumptions every few months rather than every few years. Third, usage patterns are inherently unpredictable because employees and customers are still discovering what these tools are good for. A chatbot that seems lightly used can suddenly become central to a workflow, driving costs up fast and with little warning. To manage this, set spending caps and alerts rather than fixed budgets, review usage monthly instead of annually, and require teams to justify scaling up a given use case before they do. Treat AI costs as a variable, actively monitored expense, similar to cloud computing, rather than a predictable line item.

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.