I know what you’re thinking. “Sahin, you’re crazy. Subscription models are king.” And for a long time, they were. But the ground is shifting under our feet, and the age of AI is forcing a change. I’ve seen it from the inside, both as a founder and as an investor in over 200 companies, including some of the biggest names in AI like Anthropic, OpenAI, Scale AI, and Hugging Face. The old way of doing things just doesn’t make sense anymore.
Not long ago, I was talking to a founder who was building a new AI-powered video editing tool. They were burning through cash on GPU servers, and their costs were directly tied to how much their users were processing. They tried to fit this into a traditional SaaS subscription model, but it was a disaster. Their power users were costing them a fortune, while their casual users weren’t getting enough value to justify a monthly fee. The answer was obvious: usage-based pricing. They switched, and their business was transformed overnight.
This isn’t an isolated story. It’s a pattern I’m seeing everywhere. The fundamental economics of AI are different, and the pricing models have to adapt. In this article, I’m going to lay out my argument for why usage-based pricing is the future, and how you can optimize your AI infrastructure for both cost and performance to get ready for this shift.
The Inevitable Rise of Usage-Based Pricing
The move to usage-based pricing isn’t just a trend; it’s an inevitable consequence of the technology we’re building. The cost of delivering an AI service is directly proportional to its usage. Every API call, every generated image, every processed document has a real, tangible cost. A flat-rate subscription model simply can’t account for this variability. It’s like trying to sell electricity at a fixed monthly price, regardless of how much you use. It just doesn’t work.
Think about the companies I’ve invested in. OpenAI’s entire business is built on a usage-based model. You pay for the tokens you use. Anthropic, with its powerful Claude models, is the same. Scale AI, which provides the data infrastructure for AI, charges based on the amount of data you process. This isn’t a coincidence. It’s the only model that makes sense in the world of AI.
But it’s not just about cost. It’s also about value. With a usage-based model, customers only pay for what they use. This aligns the value they receive with the price they pay. It’s a fairer, more transparent way of doing business. It also lowers the barrier to entry for new customers, who can start small and scale up as their needs grow. This is a huge advantage in a competitive market.
Optimizing Your AI Infrastructure: A Founder’s Guide
So, if usage-based pricing is the future, how do you prepare for it? The key is to optimize your AI infrastructure for both cost and performance. This is a delicate balancing act, but it’s essential for survival. Here are some of the strategies I’ve seen work best:
1. Embrace Serverless
I’m a huge believer in serverless. For most AI applications, it’s a no-brainer. With serverless, you don’t have to worry about managing servers or paying for idle capacity. You only pay for the compute you actually use. This is a game-changer for cost optimization. It also allows you to scale your application automatically, so you can handle sudden spikes in demand without breaking a sweat.
I remember when we were building RemoteTeam, which was later acquired by Gusto. We were constantly struggling with server costs. We had to provision for peak load, which meant we were paying for a lot of idle capacity the rest of the time. If serverless had been as mature then as it is today, it would have saved us a fortune.
2. Choose the Right Models
Not all AI models are created equal. Some are more powerful, but also more expensive to run. Others are smaller and more efficient, but may not be as accurate. The key is to choose the right model for the job. You don’t need a sledgehammer to crack a nut.
For example, if you’re building a simple chatbot, you probably don’t need the most powerful language model on the market. A smaller, more efficient model will likely do the job just as well, at a fraction of the cost. This is where companies like Hugging Face are so valuable. They provide a huge library of pre-trained models, so you can find the perfect one for your needs.
3. Cache Everything
Caching is one of the most effective ways to reduce costs and improve performance. If you’re getting a lot of duplicate requests, you can cache the results and serve them directly from memory, without having to re-run the model. This can have a huge impact on your costs, especially for popular queries.
I’ve seen companies reduce their API costs by over 90% just by implementing a smart caching strategy. It’s a simple idea, but it’s incredibly powerful. Don’t underestimate it.
4. Monitor, Measure, and Optimize
You can’t optimize what you can’t measure. You need to have a deep understanding of your AI infrastructure, so you can identify bottlenecks and find opportunities for improvement. This means monitoring your costs, your performance, and your usage patterns.
There are a lot of great tools out there to help you with this. But it’s not just about the tools. It’s also about the mindset. You need to be constantly looking for ways to make your infrastructure more efficient. It’s a continuous process of improvement, not a one-time fix.
The Future is Usage-Based
The shift to usage-based pricing is happening, whether you’re ready for it or not. The companies that embrace this change will be the ones that thrive in the age of AI. The ones that don’t will be left behind.
I’m not saying it’s going to be easy. It’s a big change, and it requires a new way of thinking. But I’m convinced it’s the right way forward. It’s better for customers, it’s better for businesses, and it’s the only way to build a sustainable future for the software industry.
So, my advice to you is this: start thinking about how you can move to a usage-based model. Start optimizing your AI infrastructure for cost and performance. The future is coming, and it’s going to be priced by the drink.
Frequently Asked Questions
Do I need technical skills to optimize your ai infrastructure for both cost and performance?
Not necessarily. While technical understanding helps, the most important skills are clear thinking and the ability to break problems into smaller pieces. Many successful founders I've invested in started with zero technical background and either learned enough to be dangerous or found the right technical partner.
What are the most common mistakes when optimizing your ai infrastructure for both cost and performance?
The biggest mistake I see is overcomplicating things early on. Start with the simplest version that works, get real feedback, and iterate from there. Another common trap is copying what worked for someone else without understanding the context behind their decisions.
What tools do I need to get started?
Start with the basics. You don't need expensive software or fancy tools. A spreadsheet, a note-taking app, and direct access to your customers will get you further than any enterprise platform. Add tools only when you hit a specific bottleneck.
How do I measure success with this approach?
Pick one or two metrics that directly tie to your goal and track them weekly. Vanity metrics like page views or follower counts rarely matter. Focus on metrics that reflect real engagement or revenue impact.