AI is not just an emerging technology in the research lab anymore. You employ AI to write your emails, analyze your documents, generate your images, answer your questions, and automate your tasks. With each action that you take, the supercomputers behind the AI process your command and return an output.
This creates a new economic consideration for startups. Training a neural network can cost tens of millions of dollars, but the costs do not end after training the neural network. Each of your user requests creates a new inference load. Your costs can increase with the number of your users.
This alters the considerations of AI economics because even the most successful product may fail to become profitable because of high processing costs.
AI Is Getting Cheaper, But Usage Is Growing
The good thing about that is the inference costs have been brought down dramatically. The Stanford AI Index 2025 reports that the price for asking a model with performance equivalent to GPT-3.5 decreased from $20 to $0.07 per million tokens from November 2022 to October 2024. That is more than 280 times decrease.
At first glance, it seems that this is excellent news for startups. Indeed, reduced prices mean increased demand.
With the decreasing cost of artificial intelligence, developers have an opportunity to include more AI capabilities in their applications. You can submit more queries. Your applications can handle a greater volume of data. The agent of artificial intelligence can use multiple calls to complete one task.
You will see the difference between the decreased cost per request and increased overall inference expenses.
AI Agents Could Make the Problem Bigger
Traditional AI applications often work in a simple way. You send a prompt. The model generates an answer. The interaction ends. AI agents work differently. An agent can break a task into several steps. It can search for information, call an external tool, analyze the result, make another model request, and then produce a final answer. Every step can create another inference cost.
Imagine that one customer asks an AI agent to prepare a market report. The agent might make ten or twenty model calls before completing the task. If thousands of customers perform similar tasks every day, those small costs quickly become a major business expense.
This is why we believe startups should measure more than the price of a single API call. You should measure the cost of completing an entire user task.
Bigger Models Are Not Always Better for Your Business
The AI marketplace consistently aims at bigger and better models. This is understandable since one needs to be able to handle more complicated reasoning and analysis. But this does not mean that you always need to use the most powerful models for your product.
Classifying things may not require any expensive reasoning model. Handling a small customer query may not even require as much computing power as handling some research work.
Your team can actually save costs on this one as well as create a good architecture where your application can direct simple queries to less powerful models and complicated queries to more powerful ones.
Reasoning Comes With a Price
Reasoning models further increase the importance of inference economics. Reasoning models are able to utilize extra computational power to solve complex problems before providing an answer. This may enhance the results in some tasks, yet also increases costs and delay.
The AI Index of Stanford University drew attention to this trade-off. For instance, the token cost of OpenAI’s o1 was much higher than that of GPT-4o in the described API pricing and o1 took longer to start producing an answer.
From the perspective of a startup, there is a need for practical considerations. Is the extra quality worth the extra cost of inference? Answer this question by using information from your product, not by believing that the most powerful model will provide you with the best result.
Your Infrastructure Is Becoming Part of Your Product
AI changes the role of infrastructure. In a traditional SaaS company, infrastructure costs can feel like a background technical issue. Your engineering team chooses a cloud provider, deploys the application, and monitors performance.
With AI, infrastructure directly affects your margins. Cloud providers commonly charge AI workloads based on factors such as input and output tokens, model choice, compute resources, and usage patterns. AWS, for example, publishes separate pricing structures for models available through Amazon Bedrock.
That means your engineering decisions can have a direct financial impact. A longer prompt can cost more. A larger output can cost more. Repeating the same request can cost more. Sending unnecessary context to a model can increase your bill without improving the user experience.
For teams serving customers across Asia, infrastructure location can also matter. A fast Asia VPS server can help you place parts of your infrastructure closer to your target audience and manage latency for regional users.
Small Optimizations Can Become Big Savings
First of all, check your prompts. Do you send data that is not needed by your application? Check your application for repeated instructions. Is your system sending the whole conversation where you need only part of it?
After that, consider caching. In case you have a lot of people doing similar requests, you can use caching to avoid paying again and again for the same computation. The opportunities will vary depending on your architecture and your provider, but the logic is simple.
If your product does not need some piece of information, do not force the model to compute it again. You can try out smaller models for particular purposes. See how accurate they are. If the cheaper model produces a good result for you, you probably don’t need the other one.
Latency Matters Too
Speed is important for your users too. An excellent model that takes several seconds to give a response may give a bad user experience. An inexpensive model that provides the response instantly may be better for simpler requests.
You thus have to find a balance between three issues: quality, price, and latency. It all depends on your product. A finance research tool might tolerate higher latency if the response needs complex reasoning. A chatbot used for customer service must be able to respond instantly. Your users decide what good enough is.
Startups Need a New AI Metric
We believe startups should begin to measure a metric that wasn’t necessary for teams doing traditional SaaS applications to measure so carefully.
Cost per successful AI operation. It’s not enough to know how much you’re spending on one million tokens, but rather, how much does it cost you to make one valuable action happen for one customer?
The action may be to produce a report, process a ticket, extract information from the document, or perform an automation.
The metric directly relates the infrastructure to the business: When you spend just $0.05 on the operation that you’re charging your customer $1 for, the math is entirely different from when the feature costs $0.70.
The Next Competitive Advantage Could Be Efficiency
AI models are becoming more capable. At the same time, inference is becoming cheaper and more accessible. That combination will allow more startups to build AI-powered products. It will also create more competition.
Your advantage may not come from having access to a model that nobody else can use. Your competitors may have access to similar models. The difference could come from how efficiently you use them.
You can build smarter routing. You can reduce unnecessary context. You can cache repeated requests. You can select different models for different tasks. You can optimize your infrastructure around where your users are located.
These decisions may look small during the first months of your startup. They become much more important when you have millions of requests.
The Real Question Is Not Just What AI Can Do
The cost of inference is increasingly becoming a business issue because AI products are getting bigger in scope. Agents have multiple moves. Reasoning models require extra computational effort. Longer contexts are processed. People engage more with AI. #
On the other hand, the cost of inference continues to go down. The result is a peculiar but crucial paradox. AI can get cheaper per request while getting more expensive as a whole for your company.
- Consequently, the survival of your startup might not only hinge upon picking the right model.
- You must know how much each AI interaction costs you.
- You should know which functionality provides value to users.
- And you should create a product in such a way that scaling will not automatically mean skyrocketing infrastructure costs.
Maybe the new wave of AI startups will not only consist of businesses developing the most advanced models. Maybe it will comprise companies figuring out how to optimize every AI calculation.
