GPT 5.6 Terra vs GPT 5.6 Sol

An API is a set of rules that lets different software programs communicate. For example, a weather app can use an API to ask a weather service for today’s forecast, then display the answer to you.

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. For a two-person startup, authentication is rarely a differentiator, but it carries outsized security, compliance, reliability, password-reset, MFA, account-recovery, and abuse-prevention risk. A provider lets you launch faster, inherit mature security controls, support social login and enterprise requirements sooner, and keep your small team focused on the product customers actually buy. The strongest argument against this is strategic dependency: authentication sits at the center of your customer relationship, and a hosted provider can create vendor lock-in, rising costs, limited customization, outage exposure, and painful migration later. If your product’s core value depends on unusual identity rules, highly regulated data handling, or deeply customized authorization, building and owning the system may be worth the investment.

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 are driven by usage, technology choices, and a rapidly changing supplier market. Unlike traditional software licenses, many AI services charge for every interaction, document processed, image created, or model trained. A successful pilot can therefore become expensive quickly when adopted across the company, especially if employees use it frequently or feed it large volumes of data. Costs also vary with the quality and speed expected. A cheaper model may handle simple tasks, while customer-facing, regulated, or complex work may require more capable models that cost substantially more. Testing, monitoring, security controls, human review, and integration with existing systems can add costs that are not visible in an initial vendor quote. Third, AI technology and pricing are evolving quickly. Vendors change model capabilities, pricing tiers, limits, and contractual terms. New competitors may lower prices, but new features can encourage broader use. Demand can also be unpredictable: a marketing campaign, product launch, or automated workflow may create sudden spikes in activity. The company can improve predictability by treating AI as a managed utility. Set spending limits, usage alerts, approval thresholds, and department budgets. Start with measurable business cases, monitor cost per outcome, and review vendors regularly. Build reusable governance, data, security, and integration capabilities so each new use case does not start from scratch.

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.