When you ask ChatGPT a question and it responds in seconds, you're not seeing magic. You're seeing the result of a process that cost OpenAI millions of dollars in electricity, servers, and compute time. And most companies talking about "implementing AI" today have no idea this cost exists.
Because there are two phases in artificial intelligence: one everyone knows about (training), and another nobody mentions until the bill arrives. That second phase is called inference.
And it's happening right now, every time you use facial recognition, every time Netflix recommends a show, every time your bank detects fraud. Inference is AI in action. And understanding it is the difference between using AI intelligently or burning through budget with no results.
Training vs. Inference: the difference nobody explains
Imagine you're teaching a kid to identify animals. You show them thousands of photos of dogs, cats, horses. You say: "This is a dog. This too. And this." Thousands of times. That's training.
Now, you take the kid to the zoo. They see an animal they've never seen before and say: "That's a buffalo!" You didn't specifically train them on buffalos, but they learned to identify patterns (four legs, horns, large size) and reached a conclusion. That's inference.
In artificial intelligence, it works exactly the same way:
Training: you show the model millions of examples. You teach it patterns. This happens ONCE (or a few times, when you update the model).
Inference: the model uses what it learned to respond to new data. This happens ALL THE TIME.
The trap is that most companies only plan for training. Then they discover that inference — the real, daily, constant use of the model — costs them 10 times more than expected.
Why you should care (even if you're not technical)
If you're a CEO, CFO, operations manager, or make technology investment decisions, this directly affects you:
1. The cost isn't one-time, it's perpetual
Training a model is expensive, yes. But you do it once. Inference happens every time a user asks a question, every time you process a transaction, every time you analyze data. And if your business scales, inference costs scale too.
Real example: ChatGPT processes millions of queries per day. Each query is an inference. OpenAI spends hundreds of thousands of dollars DAILY just keeping the service running (servers, electricity, processing). It's not training that kills them financially — it's continuous operation.
2. Not all AI models are equal in operational cost
There are models that are cheap to train but expensive to operate (like giant language models: GPT-5.2 Pro, Claude Opus 4.6, Gemini 3 Pro). And there are models that are more expensive to train but much more efficient in inference (like Gemini 2.0 Flash or smaller specialized models).
Choosing the wrong model can mean the difference between a profitable AI project and one that eats your entire IT budget.
3. Inference speed defines user experience
If your app takes 30 seconds to respond to an AI query, the user leaves. Fast inference requires specialized hardware (servers with GPUs, code optimization). And that has a cost.
So when someone tells you "let's implement AI in our product," the question isn't just "how much does it cost to train the model?" The question is: "How much will it cost to keep it running every day, at the speed our users expect?"
Three types of inference (and when to use each)
Not all inference is equal. There are three main forms, and choosing the right one can save you a fortune:
1. Real-time inference (online)
What it is: The user asks a question, the model responds instantly.
Examples: ChatGPT, Claude, Gemini, facial recognition on your phone, virtual assistants, real-time fraud detection.
Cost: HIGH. Requires always-on servers, fast hardware, low latency.
When to use it: When user experience depends on an immediate response. If you're slow, you lose the user.
2. Batch inference
What it is: You collect all the day's queries and process them at once, at night or during off-peak hours.
Examples: Monthly sales data analysis, accumulated invoice processing, product recommendations that update once a day.
Cost: LOW. You can use shared servers, slower processing but cheaper.
When to use it: When you don't need an immediate response. If you can wait hours (or even a day) for the result, this is your option.
3. Continuous inference (streaming)
What it is: The model is constantly processing a stream of information without human intervention.
Examples: Industrial sensor monitoring, anomaly detection in telecommunications networks, automated trading systems.
Cost: MEDIUM-HIGH. Runs all the time, but doesn't necessarily respond to users.
When to use it: When the system needs to make automatic decisions based on constantly changing data.
Real examples of inference in action
Healthcare: diagnosis faster than a radiologist
A US hospital implemented an AI model to detect lung cancer in CT scans. The model was trained on millions of images. That took months.
But now, every time a patient gets a CT scan, the model analyzes the image in less than 10 seconds and flags possible anomalies. That's real-time inference.
Result: doctors can review many more cases per day, and detect problems that previously went unnoticed. But for it to work, the hospital needs powerful servers that process images instantly. The monthly operational cost is tens of thousands of dollars.
Is it worth it? In this case, yes. Because each early diagnosis saves a life and reduces long-term treatment costs.
Finance: fraud detected before it happens
Banks process millions of transactions per day. Each of those transactions passes through an AI model that decides in milliseconds: "is this fraud or not?"
If the model is trained well once, it can identify rare patterns: a purchase in another country 5 minutes after a local purchase, atypical amounts, suspicious transaction sequences.
But each transaction is an inference. And if the bank processes 50 million transactions per day, it's making 50 million inferences. The processing cost is brutal, but the cost of NOT doing it (undetected fraud) is worse.
Retail: why Netflix knows what you want to watch
Netflix doesn't recommend shows because a human chose them for you. It recommends them because an AI model made an inference based on:
- What you watched before
- What similar users watched
- What time of day you watch
- Whether you finish series or abandon them halfway
Every time you open Netflix, the model is making real-time inferences. And since Netflix has 200+ million users, it's making hundreds of millions of inferences per day.
The system is so expensive that Netflix has entire teams dedicated to optimizing inference speed and cost. Because every millisecond they save translates to millions of dollars per year.
The three problems nobody tells you about
1. Hardware is a bottleneck
Modern AI models need GPUs (specialized graphics processors) to make fast inferences. A decent GPU for AI costs between USD 10,000 and USD 50,000. And if your model is large, you need several.
So when they tell you "implement AI," they're saying "buy or rent specialized servers that cost a fortune."
The alternative is using cloud servers (AWS, Google Cloud, Azure). But there the cost is per use. And if your model is inefficient, you'll burn through budget without realizing it.
2. Large models are slow (and expensive)
There's a direct relationship: the larger the model (the more parameters it has), the more accurate it can be. But also slower and more expensive to operate.
GPT-5.2 Pro, Claude Opus 4.6, and Gemini 3 Pro are incredibly accurate. But each inference is expensive. That's why OpenAI, Anthropic, and Google charge per use.
The solution: smaller models, optimized for your specific case. You don't need a giant model to detect fraud or recommend products. But you need a technical team that knows how to tune the model without losing accuracy.
3. Scale kills you if you don't plan
Imagine you launch a product with AI. It works perfectly with 100 users. But suddenly you have 10,000. And then 100,000.
Each new user is more inferences. More processing. More cost.
If you didn't plan the architecture to scale from the start, you'll have to redesign the entire system when it grows. And that's 10 times more expensive than doing it right from the beginning.
The mistake 90% of companies make
Most companies implementing AI today do this:
1. Hire a team of data scientists
2. Train a model
3. Put it in production
4. Get surprised when operational cost is triple what was budgeted
5. Shut down the project because "AI didn't work"
The problem isn't AI. The problem is nobody calculated how much inference costs.
The question before implementing AI is not:
"Can we train a model that does this?"
The right question is:
"Can we operate this model at scale, at the speed we need, within our budget?"
So, what do you do with this?
If you're evaluating an AI project for your company, here are the questions you need to ask BEFORE signing a contract or allocating budget:
For your technical team:
- How many inferences will we do per day/month?
- Do we need real-time responses or can we process in batches?
- What hardware do we need? Do we buy or rent it?
- How do we scale if usage grows 10x?
For your CFO:
- What's the estimated monthly operational cost (not just development cost)?
- What happens to cost if traffic grows?
- Is there a cheaper version of the model that works just as well for our case?
For your product team:
- What happens if inference takes 5 seconds instead of 1? Does the user stay or leave?
- Can we split the model into smaller, faster parts?
The uncomfortable truth
Artificial intelligence isn't free. And the real cost isn't in training the model — it's in making it work every day.
Companies that understand this are the ones using AI profitably. Those that don't burn through budget and then say "AI doesn't work."
AI works. But only if you know how much it costs to keep it alive.
Because training a model is like buying a car. Inference is the gas you put in it every day.
And if you didn't calculate how much you'll spend on gas, the prettiest car in the world will leave you bankrupt.
Is your company evaluating AI projects? The difference between success and failure isn't in the model you choose — it's in whether you understand how much it costs to operate it.
Our team at Vortwood has implemented AI in business, operational environments and highly complex processes, if you need help with it, we are here
