Cloud AI costs soaring? Learn how to control your inference expenses and avoid unexpected bills! #AIInference #CloudCosts #AIBudget
Explanation in video
Heads Up! AI Can Get Pricey – Let’s Talk About “Inferencing” Costs
Hey everyone, John here! You know we love talking about all the cool things AI can do. But just like running a car costs money for fuel, using AI also has its running costs. Today, we’re diving into a specific part of that cost that’s making companies sit up and pay attention: something called “AI inferencing.” It sounds a bit techy, but don’t worry, we’ll break it down.
It seems companies are spending a LOT more on the computer power they rent from big cloud companies – we’re talking billions of dollars, and it jumped up by 21% last year! A big reason for this is that more and more businesses are using AI, and AI needs a lot of computer muscle.
First Things First: Training AI vs. Using AI (Inferencing)
Imagine you’re teaching a dog a new trick, like “fetch.” The time you spend teaching it, throwing the ball, giving treats – that’s like AI training. You’re feeding the AI lots of information so it can learn a skill. This usually costs a chunk of money upfront, but it’s often a one-time thing for that specific skill.
Now, once your dog knows “fetch,” every time you throw the ball and it brings it back, that’s the dog using its training. In the AI world, this is called inferencing. It’s when the AI takes what it has learned and applies it to new situations or data to make predictions, generate text, recognize images, or do whatever it was trained to do.
Lila: “Okay, John, so ‘inferencing’ is basically the AI doing its job after it’s been trained?”
John: “Exactly, Lila! Think of it as the AI ‘inferring’ or figuring out an answer based on its knowledge. And here’s the tricky part: the costs for this ‘inferencing’ can really add up over time, and they can be hard to predict. It’s like not knowing how many times you’ll play fetch with your dog each day – the treat bill could vary a lot!”
Why Unpredictable Inferencing Costs Are a Headache
The way companies pay for these AI inferencing services is often based on how much they use them. This might be measured in things like “tokens” (think of them as tiny pieces of information the AI processes) or “API calls” (which are like requests made to the AI to do something).
Lila: “John, can you explain ‘tokens’ and ‘API calls’ a bit more simply?”
John: “Good question, Lila! Imagine an AI that helps write emails. A ‘token’ could be a word or even part of a word. The more words the AI generates or reads, the more tokens it uses, and the more it costs. An ‘API call’ is like ringing a doorbell to ask the AI a question. If you ask it one question, that’s one call. If you have an app that asks the AI thousands of questions a day for its users, that’s thousands of API calls. The more tokens or calls, the higher the bill.”
Because these costs can be a bit of a mystery upfront, some companies get nervous. They might:
- Make their AI models less smart or powerful to save money.
- Only use their AI for the most super-important tasks.
- Or even decide not to use AI inferencing services at all.
If companies hold back like this, it could slow down how quickly AI technology gets better and more helpful for everyone.
Oops! When AI Budgets Go Wrong
This isn’t just a “what if” worry. It’s real! Some businesses have gotten surprisingly huge bills. For example, a company called 37signals, which makes a project management tool called Basecamp, found themselves with a cloud bill over $3 million! That unexpected shock led them to move their computer systems from the “cloud” (renting power from big providers) back to their own “on-premises” servers (meaning, in their own building).
Experts are even warning that companies using AI could see their cost estimates be off by a massive 500% to 1,000%! That’s like budgeting for a bicycle and ending up with the bill for a small car. These mistakes can happen because the prices from vendors might go up, or there are hidden costs, or companies just don’t manage their AI resources very well.
Finding Smarter (and Cheaper) Ways to Run AI
So, what are companies doing about it? Many are rethinking how they use cloud services.
Lila: “John, you mentioned ‘cloud services’ and earlier you said ‘IaaS and PaaS.’ What are those exactly?”
John: “Great questions, Lila! ‘Cloud services’ are basically when companies rent computer power, storage, and software from big providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud, instead of buying and managing all that equipment themselves. It’s like renting a super-powerful computer over the internet.
Now, ‘IaaS’ stands for Infrastructure as a Service. Think of this as renting the basic building blocks – like virtual computers, storage, and networks. You get a lot of control, but you also have to manage more stuff.
‘PaaS’ is Platform as a Service. This is like IaaS, but it also includes extra tools and services that make it easier for developers to build and run their applications without worrying so much about the underlying infrastructure. It’s like renting a workshop that already has some of the key machines and tools set up for you.”
While these big cloud companies are super popular (they handle over 65% of what companies spend!), some businesses are now looking at:
- Specialized hosting providers: These are companies that focus specifically on certain types of services, maybe even AI, and might offer better deals or setups.
- Colocation services: This is like renting space in a big, secure data center to put your own computer equipment.
- On-premises: Like 37signals, some are bringing their IT back in-house.
The idea is that these other options might offer more predictable pricing or better ways to manage resources, so AI doesn’t break the bank.
Even the big cloud providers know these costs are a concern. They’re trying to find ways to make things more efficient and cheaper. One idea is to use special computer chips, called ‘hardware accelerators’ alongside the usual powerful ‘GPUs’ (Graphics Processing Units – originally for games, but great for AI too!), to speed up AI tasks and cut down costs.
Lila: “Whoa, ‘hardware accelerators’ and ‘GPUs’? More techy words, John!”
John: “You got it, Lila! Think of a ‘GPU’ as a super-fast calculator that’s really good at doing many calculations at the same time, which is perfect for AI. A ‘hardware accelerator’ is an even more specialized chip designed to do one particular type of task, like AI inferencing, incredibly fast and efficiently. Using these special chips can be like having a custom-built tool for a specific job, making it quicker and cheaper than using a general-purpose tool.”
Is AI in the Cloud a Forever Thing?
Despite efforts to make AI in the cloud more affordable, some experts wonder if it’s truly sustainable in the long run, especially as companies use AI more and more. If costs keep climbing, it could become a real problem for businesses wanting to use AI to grow and innovate.
Practical Steps to Tame Those AI Inferencing Costs
So, if your company is using AI or thinking about it, what can you do to avoid nasty bill surprises? Here are some smart moves the experts recommend:
- Keep an eye on things: Use tools that show you in real-time how much computer power you’re using and what it’s costing. This helps you see where you can save.
- Guess the cost: Try to estimate your costs based on how much you think you’ll use the AI. This helps you budget and avoid going over.
- Pick your price plan wisely: Cloud providers offer different ways to pay. The “pay-as-you-go” model might not always be the best. Sometimes a fixed price might be better for your needs.
- Mix and match: Consider using a ‘hybrid cloud’ approach. This means using a mix of public cloud services (like AWS or Azure) and your own private cloud resources (computers you own and manage).
Lila: “John, what’s a ‘hybrid cloud’ then?”
John: “Imagine you have some clothes you wear all the time – those are your essentials. You might keep those in your own closet at home (that’s like your private cloud or on-premises servers). But for special occasions, or when you need a lot of extra outfits quickly, you might rent them (that’s like the public cloud). A ‘hybrid cloud’ is using both your own resources for some things and rented cloud resources for others, to get the best of both worlds – flexibility and cost control.”
It’s also a good idea to talk directly with your cloud service providers. They often have experts who can help you find ways to manage costs better and might even have special solutions for your specific industry.
A Few Final Thoughts from Us
John: “It’s clear that AI is incredibly powerful, but like any powerful tool, it comes with responsibilities – and managing costs is a big one. It’s not about being scared of AI, but being smart about how we use it. A little planning can save a lot of headaches (and money!) down the road. Don’t wait for that shockingly high bill to start thinking about this stuff!”
Lila: “As someone new to all this, it’s really helpful to understand that there are costs involved beyond just ‘making’ the AI. Knowing about things like ‘inferencing’ and how it can affect the budget makes AI feel a bit more real-world and less like magic. It’s good to know there are ways to manage it!”
This article is based on the following original source, summarized from the author’s perspective:
Navigating the rising costs of AI inferencing