Systems | Development | Analytics | API | Testing

Testing a FastAPI app that calls OpenAI, Anthropic, or Gemini gets expensive fast. The problem is not just the API bill in production. It is all the repeated traffic in development: prompt tweaks, CI runs, regression checks, and the load tests you keep putting off because every run burns tokens. Hand-written mocks do not help much once the app is doing multi-step LLM work.

IT leaders today are in a high-stakes gridlock between the pressure to invest in new AI solutions and legacy systems that aren’t equipped to support them. With the pace of work today, long modernization projects are often untenable. They disrupt your workflows. They use up your team’s resources. And they sometimes fail to deliver results. So how do successful organizations move forward? They rethink their approach.

It is Monday morning. Your VP of Engineering just forwarded a company-wide memo: every team needs to demonstrate AI adoption by end of quarter. At the same time, you learned last week that your QA budget was trimmed by 15%, because leadership assumes AI will "make testing more efficient." And your developers? Thanks to Copilot, Cursor, and Claude Code, they are now shipping 76% more code per person than they were two years ago.

And more to the point… why do I need to read a whole blog post on it? Two good questions. Well when we’re debugging, stepping removes the guesswork by letting us watch the logic unfold step-by-step. We can pause the code, go through the execution one instruction at a time and isolate the exact point where the bad stuff happens. This is one of the most reliable ways to understand why a bug happens, not just where it shows up. It also shines a microscope on our code flow, showing us.

Most teams know they are spending money on AI in production. Far fewer realize how much they are spending outside production. It’s easy to get lost as you evaluate which model has the best responses, is fast enough, and cheap enough to run in production. That is because the AI bill usually shows up as a giant blob. It is easy to see the total.

Enterprise product teams are squarely in the throes of a massive shift. New Gartner research crystalized that companies using AI tools for product management are reaping a significant 35% productivity gain and achieving a two to three-year time-to-market advantage over those who wait.

As organizations scale their AI initiatives from experimentation into production, CTOs face a pivotal architectural challenge as storage emerges as one of the most common—and most expensive—constraints. While organizations continue to invest aggressively in GPU compute, studies consistently show that infrastructure inefficiencies outside the GPU account for the majority of wasted AI spend.