Even if you love the predictability of subscriptions, there are compelling reasons to offer a usage-based tier. I'll explain how a usage-based option can help you capture a different segment of the market and serve as a powerful customer acquisition channel.
What I've Learned From 111 Companies
A few patterns in NLP APIs come up again and again.
The founders who handle NLP APIs well understand that customer feedback is the only metric that matters.
Why Most Approaches Fail
Many common approaches to NLP APIs are flawed at the root.
The root cause is usually one of three things:
Copying what big companies do without understanding why they do it. What works for Google doesn't work for a 10-person startup.
Over-engineering the solution when a simple approach would work better. I've seen teams spend six months building something that could have been done in two weeks.
Ignoring the human element. Technology is the easy part. Getting people to actually use it is where the real challenge lives.
The Framework That Actually Works
Here is a simple framework for evaluating NLP APIs. It's not complicated, but it requires discipline.
Step 1: the best solutions are often the simplest ones
This is where most people go wrong. They skip this step entirely and jump straight to execution. Don't do that.
Step 2: simplicity beats complexity every time
Once you have the foundation right, this becomes much easier. I've watched founders struggle with this for months when the answer was staring them in the face.
Step 3: Iterate relentlessly
Nothing works perfectly the first time. The companies that get NLP APIs right treat it as an ongoing process, not a one-time project.
The AI Angle
Any discussion of NLP APIs in 2026 has to include AI. As someone who's invested in Anthropic, OpenAI, Scale AI, and Hugging Face, I have a front-row seat to how AI is transforming this space.
The short version: AI makes good practitioners better and bad practitioners worse. It's an amplifier, not a replacement.
Some teams use AI to get far more out of NLP APIs. Others spend heavily on AI that solves the wrong problem. The difference comes down to understanding what you're actually trying to achieve.
This connects to broader themes around SaaS metrics, AI infrastructure costs, usage-based pricing that I've been thinking about a lot lately.
Wrapping Up
I've shared a lot here, and I know it can feel overwhelming. But you don't need to get NLP APIs right on day one. You just need to get started and keep improving.
Founders who do well at NLP APIs tend to share one trait: they're relentlessly practical. They don't chase perfection. They chase progress.
That's the mindset I'd encourage you to adopt. Start where you are. Use what you have. Do what you can. And keep pushing forward.
As always, I'm rooting for you.
Frequently Asked Questions
Q:Do all experts agree with this view?
No, and that's fine. The best ideas in business are often contrarian. I share my perspective based on my experience and data, but I encourage you to seek out opposing viewpoints and form your own conclusions.
Q:What's the most common pushback you get on this?
People often push back by citing exceptions or edge cases. And they're usually right that exceptions exist. But building a strategy around exceptions rather than patterns is a losing game for most founders.
Q:What experience informs this perspective?
This perspective comes from over a decade of building companies in Silicon Valley, two successful exits (RemoteTeam to Gusto, MovieLaLa to Gfycat), and investing in 234 startups including Anthropic, OpenAI, and Scale AI. I write about what I've lived.