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AWS and Startups

AWS and Startups: The Difference Between Support and Reality
By Skeeter Wesinger
December 27, 2025

Amazon Web Services presents itself as one of the great enablers of modern entrepreneurship. Its startup messaging promises speed, affordability, mentorship, and a clear path from idea to scale. Millions of companies, we’re told, build on AWS because it helps them innovate faster, keep costs low, and prove what is possible.

All of that is true—just not in the way most founders assume.

AWS is not a startup partner. It is a world-class infrastructure utility with a startup-friendly on-ramp. Confusing those two things is where disappointment begins.

The startup ecosystem AWS advertises does exist. There are former founders, CTOs, venture capitalists, and mentors inside the organization. But access to that expertise is neither automatic nor evenly distributed. For most early-stage founders, the lived AWS experience consists of documentation, support tickets, and account managers whose incentives are aligned with usage growth, not startup survival. The ecosystem is real, but it is gated—and those gates usually open only after external validation has already occurred.

Generative AI follows the same pattern. AWS encourages startups to innovate quickly using managed services and foundation models, reducing the operational burden of building AI systems from scratch. This is genuinely useful. It is also strategically convenient. AWS benefits when startups adopt its abstractions early, because abstraction is how lock-in begins. Pricing complexity, usage opacity, and scaling surprises tend to reveal themselves only after a product starts working—precisely when switching costs are highest.

Programs like AWS Activate are often cited as evidence of AWS’s commitment to founders. Credits, technical support, and mentorship can meaningfully accelerate early experimentation. But credits do not change fundamentals. They delay cost reality; they do not remove it. For infrastructure-heavy startups—particularly those using GPUs, data pipelines, or real-time systems—credits can evaporate in weeks. When they do, the company is left facing enterprise-grade pricing without enterprise-grade revenue.

Go-to-market support is perhaps the most misunderstood promise of all. Co-selling with AWS is possible, but it is not designed for early startups. It favors companies with reference customers, repeatable sales motions, and offerings that align cleanly with existing account strategies. In practice, partners are incentivized to sell their own services, not to shepherd unproven products into the market. Distribution exists—but usually only after distribution is no longer the primary problem.

None of this makes AWS deceptive. It makes AWS exactly what it is: a highly efficient, globally scaled infrastructure provider. AWS does not exist to reduce founder risk. It exists to provide reliable, metered access to computing resources—and it does that exceptionally well.

The danger lies in mistaking capability for commitment.

AWS will help you build faster. It will not help you decide what to build. It will help you scale globally. It will not help you survive the transition from prototype to revenue. It will let you fail quickly and at scale—but the bill will still arrive.

For experienced builders, this distinction matters. Startups that treat AWS like electricity—necessary, powerful, and expensive if misused—tend to make better decisions than those who treat it like a mentor or partner. Infrastructure accelerates outcomes; it does not improve judgment.

AWS’s startup narrative is written for investors, accelerators, and press releases. The reality is written in CloudWatch logs, cost-explorer dashboards, and late-night architecture decisions. Founders would be better served by understanding that difference early. AWS is handing out popsicles, not meals—knowing most will melt long before they ever create a mess. Only those that survive the heat earn a seat at the table.

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What Is Inference in Artificial Intelligence

What Is Inference in Artificial Intelligence?

Skeeter Wesinger December 25, 2025

When people talk about artificial intelligence, they often focus on training—the phase where a model learns from large amounts of data. But training is only preparation. The real work of AI happens later, during a phase called inference.

Inference is what occurs after an AI model has already been trained. At this point, the model is no longer learning. Instead, it is using what it has learned to make decisions, predictions, or generate outputs based on new information.

Think of training as education and inference as employment. A student may spend years learning mathematics, but once they become an engineer, they are no longer studying calculus textbooks. They are applying what they already know to solve real problems. Inference is that moment of application.

During inference, the internal structure of the model—its learned parameters—remains fixed. New data is fed in, the model processes it, and an output is produced. That output might be a medical diagnosis, a fraud risk score, a translated sentence, or a 3D reconstruction. The model does not adjust itself in the process. It simply executes.

This distinction matters because inference is where artificial intelligence actually touches the real world. It is where speed, reliability, and cost become critical. A system that took weeks to train may be expected to produce answers in milliseconds during inference. In many applications, especially in healthcare, finance, manufacturing, and defense, inference is the product.

Most modern AI systems spend the vast majority of their operational life performing inference. Training may happen once or occasionally, but inference runs continuously. Every search result, recommendation, image recognition, or automated decision depends on inference happening correctly and efficiently.

As AI systems scale, the challenge is no longer just how to train better models, but how to deploy them so inference is fast, affordable, and dependable. This is why inference infrastructure—hardware, software, and architecture—has become as important as the models themselves.

In short, training teaches an AI model how to think. Inference is how that thinking is put to work.

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