Anthropic Claude Haiku 5.5 vs OpenAI GPT 5.6 Sol

An API is a set of rules that lets one piece of software ask another for information or services in a standard way. Think of it as a published list of requests a program understands, along with the format of the answers it will send back. Your weather app, for example, uses one to fetch forecasts from a weather service.

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.

My recommendation is to use a hosted authentication provider such as Auth0, Clerk, or Supabase Auth. Authentication is a security-critical problem where subtle mistakes, like flawed password reset flows, session handling bugs, or weak credential storage, can expose every user at once. Two people cannot realistically match the attention that specialized providers give to these areas, and building it yourselves diverts time from the product that actually differentiates your startup. Hosted providers also handle compliance features, multi-factor authentication, and social login out of the box, and you can migrate later if needs change. The strongest argument against this recommendation is cost and lock-in. Per-user pricing can climb steeply as the startup grows, sometimes becoming a significant line item before revenue justifies it. Moving off a provider later means migrating password hashes, sessions, and user identifiers, which vendors often make difficult, so early dependence can quietly constrain future decisions.

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.

Our AI spending is difficult to forecast for several connected reasons, and understanding them is the first step toward controlling it. Most AI services charge by usage rather than by a fixed subscription, so every question, document, or generated report carries a small cost that multiplies with volume. A single employee experiment can look harmless, while an automated workflow that runs thousands of times overnight can quietly produce a large bill. Usage also swings with business cycles, seasonal demand, and the unpredictable ways staff discover new applications. A second cause is that the price of the underlying technology keeps changing. Providers regularly release new models, retire old ones, and adjust rates, sometimes in ways that make a more capable option cost more per task. Longer inputs, such as pasting entire contracts into a tool, raise costs further. Hidden expenses also accumulate outside the invoice itself, including data preparation, integration work, security reviews, and the internal time needed to test whether outputs are accurate. Because these costs sit in different departments' budgets, nobody sees the full picture. Set spending caps and alerts with each vendor, requiring teams to tag usage to named projects, and measuring cost per outcome rather than total spend. Finance forecasts in ranges.

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.