AI infrastructure costs are skyrocketing! Companies are struggling with cost management. How can we use AI wisely? AI Creator's Path News: Explaining inference cost measures. #AICost #CloudCost #AIInfrastructure
Video explanation
"Invisible money" when using AI? We'll teach you the tricks to save wisely!
Hello and welcome to my blog, where I explain AI technology in an easy-to-understand way for beginners! I'm your friend, John.
Recently, you often hear the word "AI" in the news and on TV commercials. AI can automatically draw pictures and write sentences, making our lives more and more convenient. Companies are also actively using AI, which is resulting in the creation of new services and making work more efficient.
Behind the scenes of these AI successes lies a technology called "cloud computing." Cloud computing is a service that allows you to easily use computer computing power and data storage space over the Internet. It's very convenient because you can rent only the amount of high-performance computers you need without having to prepare your own.
In fact, by 2025, the amount of money spent worldwide on cloud services (technically known as IaaS or PaaS; IaaS: a service that rents out IT infrastructure such as servers and networks, and PaaS: a service that rents out the environment for creating apps) is expected to reach a whopping $909 billion (that's an enormous amount in Japanese yen!), up 21% from the previous year. This is because many companies are using AI and transferring data to the cloud.
But it's not all good. Especially when using AI,Inference"The cost of this task can sometimes be higher than expected. This seems to be a bit of a worry for companies that want to make greater use of AI. Today, let's take a look at what "AI inference costs" are and how you can save money wisely!
What is the difference between AI "training" and "inference"?
The words "training" and "inference" often come up when talking about AI. They sound difficult, but in simple terms they are like this.
- AI Training: This is the period during which AI is shown a lot of data and taught various things, such as "This is a photo of a cat" or "This is a photo of a dog." It's like "schooling" for AI. This training requires a lot of time and computing power, and is quite expensive. However, in many cases, once you have trained it thoroughly, you can use that knowledge forever.
- AI InferenceInference: This is the process where an AI that has completed training actually looks at new data, thinks "What is this?", and answers questions. This is the stage where the AI does its "work." For example, when you ask an AI chatbot a question, the AI understands the question and generates an answer, which is inference.
The problem is this "inference." Training requires a large investment at the beginning, but inference often requires a small, ongoing expense each time you use the AI. So, if you try to make more and more use of AI, the inference costs pile up and can end up being unexpectedly expensive.
"Inference costs" exceed budget? Unexpected pitfalls
You may be wondering, "So how is the inference cost determined?" At present, AI inference services generally use a pay-per-use system where you pay only for what you use. For example, the fee is determined based on the amount of data the AI processes (measured in units called "tokens") or the number of times the AI function is called (called "API calls").
At first glance, this "pay as you go" approach seems fair and easy to understand. However, it is very difficult to accurately predict in advance how much you will actually use. This can leave companies scratching their heads, thinking, "I tried using AI, but it's costing more than I expected..."
In fact, 37signals, a company that runs the project management tool "Basecamp," reportedly ended up with a bill for cloud services that exceeded $300 million (more than 1 million yen if calculated at an exchange rate of 150 yen to the dollar). Shocked by this, they ended up stopping using the cloud and going back to managing their own equipment.
In addition, Gartner, a well-known research firm, says that "companies that adopt AI are overwhelmed by cost estimates compared to the actual costs.The difference is 5 to 10 times"There is a possibility," he warns. This is due to price increases from service providers, overlooked costs, and poor management of AI. Even if you want to try something new with AI, it's all for nothing if your budget gets messed up.
It's not just the cloud: Find out how to save money on AI
In response to this situation, many companies are beginning to reconsider how they operate AI. Major cloud services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are very convenient, but rather than relying solely on them, there is a growing movement to consider other options.
For example, here are some options:
- Specialized hosting providers：It is a company that lends out computer environments specialized for AI processing. Since it is optimized for AI, it may be possible to operate AI more efficiently.
- Colocation Services: This is a service that allows companies to store their own servers (the computers themselves) in specialized data centers (facilities that safely store and operate many servers).
These services are said to have the advantage of having easy-to-understand fee structures and being easy to adjust to specific AI tasks. Of course, major cloud service companies are also aware of the problem of inference costs, and are developing new technologies (for example, combining special components dedicated to AI processing with components called GPUs that are good at image processing) and reviewing fee plans to use AI more efficiently and cheaply.
However, some experts are still concerned, asking, "Is it really safe to continue using AI on a large scale in the cloud? Will the costs continue to grow and become unmanageable?" This issue of cost is unavoidable if you want to ensure the long-term success of an AI project.
You can start doing it today! 5 tips to reduce AI inference costs
So how can you wisely manage your AI inference costs? Here are some practical ways to do so.
- Check your usage status carefully!: Use a tool that lets you see in real time how much AI you are using and how much it is costing you. You'll be able to see where you are overspending and where you can make savings.
- Try to estimate the cost in advance!Make a prediction: "If I use this much AI, it will probably cost about this much." This will make it easier to avoid going over budget.
- Choose your rate plan wisely!: Compare the pricing plans offered by cloud providers. Pay-as-you-go isn't always the best option. In some cases, a flat monthly fee might be a better deal.
- Consider combining techniques too!: A combination of public cloud (a cloud shared by everyone, such as Amazon AWS or Google Cloud) and private cloud (a cloud environment dedicated to a company)Hybrid Cloud" There is also the idea that it is possible to flexibly optimize costs by taking advantage of the strengths of each.
- Consult a professional!：It's also a good idea to talk to someone at the cloud service provider to see if there are ways to use AI more cost-effectively. They may be able to suggest solutions tailored to the challenges specific to your industry.
The important thing is to start taking measures early, before you're surprised by a sudden, expensive bill.
A word from John
AI is truly an amazing technology, and is full of possibilities to brighten our future. However, as the saying goes, "nothing is free," this experience made me realize that convenient things come at a certain cost.
However, there is absolutely no need to give up on using AI. What's important is to have the proper knowledge and be clever with your ingenuity. If you do that, you should be able to get the most out of this powerful tool while getting the most out of it. I hope you'll use this as an opportunity to think about how you can interact with AI!
This article is based on the following original articles and is summarized from the author's perspective:
Navigating the rising costs of AI inferencing