Token Alchemy: Turning Ideas into Hard Numbers

As organizations explore modernizing legacy applications, integrating inference-based functionality could be a game changer. But while everyone loves to demo generative AI, few are talking about what it actually costs to run at scale. Other than, it looks expensive.

From Cool Demo to Scalable Reality

Let’s talk about the hard part.

Once you move beyond a clever prototype and start considering inference in a production setting, several challenges appear. First, there’s latency and performance, your model needs to return results fast enough for real-world use.

Then there’s infrastructure. Do you run this on CPUs? GPUs? Where? And let’s not forget model size, fine-tuning, and security.

Today, I’d recommend starting with inference before diving into fine-tuning. But one question tends to dominate stakeholder discussions. What does it actually cost to run?

Whether you’re using OpenAI’s GPT models, your own LLaMA instance on Azure, or Hugging Face models via containers, the real question is, What’s the dollar cost per inference? And more importantly, what’s the cost per business transaction?

Three Paths to Inference Deployment

Let’s break down the three most common paths for running inference:

Option 1: API-Based Inference (Token-Based Services)

• How it works: You consume a model via a managed API (like OpenAI, Azure OpenAI, or Cohere). You pay per token used.
• Pros: No infrastructure overhead, rapid setup, great for experimentation and burst workloads.
• Cons: Limited control over performance, latency, and data governance. You’re locked into model choices and pricing.

Option 2: Containerized Inference (Self-Hosted Models)

• How it works: You run models like LLaMA or DeepSeek in your own cloud (or even on-prem) using GPU VMs.
• Pros: Full control over the model and tuning, consistent performance, and easier cost predictability at scale.
• Cons: High setup complexity, need for ML engineering expertise, GPU cost volatility, and you carry the burden of uptime and scaling.

Option 3: Hybrid Model (Burstable Inference)

• How it works: You run a base level of dedicated GPU capacity and burst to an API when demand spikes.
• Pros: Balances cost and performance, reduces latency under load, and provides fallback capacity.
• Cons: Requires orchestration logic and potentially dual billing models, with added complexity to monitor.

What’s the Cost Per Business Transaction?

This is where it gets real.

An API request or a GPU inference run is not a business outcome. To justify the investment to leadership, you need to tie this back to actual workflows.

Use Case: Construction Site Safety Inspection with AI

Here’s a process you could automate with generative AI:

1. A construction site photo is uploaded.
2. A 10MB safety policy document is ingested.
3. The model identifies any safety violations in the image by comparing it against the policy.
4. A risk register is generated with identified issues and proposed mitigations.
5. Tasks are created for the site manager to resolve each issue.

As a ballpark estimate lets say it costs on average of $250 to perform a site inspection and takes about 3 hours per visit.

What would it look like if you could automate most of this and do it daily across every construction site and only send a human when high-risk sites are identified ?

Token Math: Estimating Inference Cost with Real Data

Let’s get into the numbers. A quick and dirty way to estimate inference costs is what I call “token math.”

Assumptions,

• Policy document: ~10MB of text → ~40,000–50,000 tokens
• Photo (analyzed for context): ~500–1,000 tokens
• Prompt: 100–300 tokens
• Output (structured data, tasks, risk register): 500–2,000 tokens

That gives us a total token count per job of ~11,000–53,000, depending on prompt structure and policy complexity.

Now let’s look at the cost,

• Containerized GPU run (e.g., LLaMA 8B on Azure low-end GPU VM): ≈ $0.72 per scan at the high end of token usage
• API-based inference with a similar model ≈ $0.05 per scan

This doesn’t include supporting cloud infrastructure (storage, networking, orchestration), but those are relatively predictable costs that most teams already model.

So even at the higher end, $0.72 vs. $250 per inspection? That’s an eye-popping reduction. Even if you only automated part of the process and cut site visits in half, the ROI becomes clear.

What’s the Takeaway?

As you consider deploying generative AI in production, especially for inference-heavy use cases, the deployment model you choose has a dramatic impact on cost and flexibility.

• APIs are great for speed and scale
• Containers give you control and cost predictability
• Hybrid models offer balance, if you’re ready for the complexity

But no matter the tech stack, the business case is won or lost on how clearly you map tokens to transactions, and dollars to outcomes.

Links to head over to if you want to read some more

Hugging Face Inference Endpoints – Hugging Face

Read about responsible AI if you are interested Responsible AI: Ethical policies and practices | Microsoft AI

If your want to build workflow solutions and inject GenAI check out CoPilot Studio https://www.microsoft.com/microsoft-copilot/microsoft-copilot-studio

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