Your AI application can be cheap to build and surprisingly expensive to run.
The culprit is often tokens.
Every prompt sent to an LLM consumes input tokens. Every response generates output tokens. As usage grows, those small per-token charges can turn into thousands of dollars in monthly inference costs.
So how much do AI tokens actually cost—and how can you keep the bill under control?
What Is AI Token Cost?
An AI token is a small unit of text processed by an LLM. Depending on the model and provider, you're typically charged separately for input tokens and output tokens.
The basic calculation is:
Monthly AI cost = (Input tokens × input price) + (Output tokens × output price)
For example, processing 100 million tokens per month at $0.50 per million tokens would cost approximately $50.
But real-world costs aren't always that simple.
Long prompts, large context windows, excessive output, repeated requests, and inefficient model selection can quickly increase token consumption.
Why AI Token Costs Become a Problem at Scale
A chatbot handling a few hundred requests per day may barely move your budget.
Now imagine:
- Thousands of daily users
- RAG applications sending long context with every query
- AI agents making multiple model calls per task
- Customer-support workflows generating lengthy responses
- Code-generation tools processing large files
Your token volume can multiply long before you realize it.
And that's when cost per million tokens becomes an important metric—not just the headline API price.
How to Reduce AI Token Costs
You don't always need a more expensive GPU or a cheaper API. Start by improving how efficiently you're using tokens.
Use the right model Don't send every request to a large frontier model. Smaller open models can handle classification, summarization, extraction, RAG responses, and many other production workloads.
Reduce unnecessary context Sending thousands of irrelevant tokens with every request increases your bill without necessarily improving the answer.
Control output length Longer responses mean more output tokens. Set sensible limits where possible.
Compare cost per useful output A model that costs more per million tokens isn't necessarily more expensive if it produces substantially more useful output per request.
Consider managed open-model inference If your workload doesn't require proprietary frontier models, an OpenAI-compatible inference API can provide access to open models without requiring you to manage GPUs yourself.
A Lower-Cost Alternative
Packet.ai's Token Factory provides managed inference for open models with per-token billing, including models such as Llama, Qwen, DeepSeek, and Mistral. Current launch pricing starts at $0.06 per million tokens, with input and output metered separately and scale-to-zero for variable workloads.
That means you can focus on building your AI application instead of managing GPU infrastructure.
The Bottom Line
AI token costs aren't just about finding the lowest price per million tokens.
The real goal is to get the most useful output for every token you pay for.
If your AI workload is growing, measure your token consumption, optimize your prompts and model selection, and compare managed inference against running GPUs yourself.
Want to go deeper?
Check this out then -> AI Token Cost: What Is a Token and What Does It Actually Cost in 2026
