AI is getting expensive.
Not only because of model APIs, GPU bills, vector databases, cloud platforms, observability tools, and managed services, but also because we often start building with the most expensive architecture before we understand the problem.
But here is the good news: today, a software engineer can learn, prototype, and even launch serious AI systems with a $0 software stack.
Of course, “$0” does not mean magic.
You still pay for hardware, electricity, domains, bandwidth, production servers, or paid APIs when you scale. But for learning, prototypes, internal tools, demos, MVPs, and self-hosted experiments, there is now a powerful free-to-start ecosystem.
