We Tried to Build Our Own AI Model — It Nearly Killed Our Startup

We thought building our own large language model would give us a competitive edge. In reality, it almost ended our company.

Our idea was simple: create an AI assistant that could analyze complex business documents and generate insights in seconds. We were convinced that to stand out, we needed to own the intelligence behind it. That meant training our own model, hosting our own infrastructure, and avoiding third-party APIs entirely.

At first, it felt like the bold, visionary move every serious AI startup should make.

Then the bills arrived.

Training even a moderately capable language model required far more than a few GPUs and some clever engineering. Costs escalated quickly. Cloud compute usage spiked during experiments. The talent required to optimize training pipelines was expensive and hard to hire. And despite months of work, our model was still underperforming compared to commercially available alternatives.

It could summarize documents, but inconsistently. It struggled with reasoning. It hallucinated facts. Every improvement required retraining, which meant more time and more money.

We weren’t building a product anymore. We were running an AI research lab.

Three months in, a large portion of our seed funding was gone, and we still didn’t have something customers could reliably use. One evening, while reviewing another alarming infrastructure bill, we asked a simple question that changed everything:

Why are we building the model instead of building the product?

That question led us to explore LLM-as-a-Service platforms.

Instead of training from scratch, we integrated a production-grade model through an API. Within days, we had better natural language understanding than our custom model. Within weeks, we added retrieval-augmented generation so the system could reference our proprietary knowledge base. Accuracy improved. Hallucinations dropped. Performance stabilized.

Most importantly, our costs became predictable.

We shifted from massive upfront infrastructure spending to usage-based pricing that scaled with our customers. When usage was low, costs were low. When revenue grew, AI costs grew alongside it. The financial pressure that once threatened our survival turned into a manageable operating expense.

But the biggest change wasn’t technical. It was strategic.

Before, our roadmap revolved around model training milestones. After adopting LLM-as-a-Service, our roadmap focused on user experience, integrations, and workflow automation. We stopped worrying about GPUs and started talking to customers again.

And customers didn’t ask whether we trained our own model.

They asked whether the product solved their problem.

There was another unexpected advantage. Every time the model provider released an upgrade, our product improved automatically. Better reasoning, larger context windows, faster responses — all without retraining or redeploying anything on our side. Innovation became something we inherited rather than something we had to fund.

It felt like we had gone from trying to build a power plant to simply plugging into the grid.

That doesn’t mean building your own model is always the wrong choice. Companies with massive proprietary datasets, strict regulatory requirements, or deep research budgets may benefit from owning their models. But for most startups, and even many enterprises, LLM-as-a-Service delivers the majority of the value at a fraction of the cost and time.

In our case, it saved the company.

Today, we’re profitable. Not because we created the most advanced language model, but because we focused on creating the most useful product. AI became our infrastructure, not our identity.

Looking back, the lesson was clear. Intelligence is becoming a utility. Just as companies no longer build their own data centers, most won’t build their own foundation models. They’ll build applications on top of them.

We stopped trying to own the intelligence.

We started renting it.