The Anatomy of an AI Agent - Part 4
What You Need to Know About Pricing Up Your New Toy
As the hype train keeps on rolling, the “experts” on LinkedIn are not telling you about the thing that matters the most with all this new agent technology - the price.
In Part 3 we were still deep in the weeds with how agents think. Today, I’m leaving the coding alone and dropping into finance mode.
As part of building the Templonix agent framework, I’ve also built a lifecycle costing model to go with it. In early 2025, this is by far the most valuable utility my framework offers - the ability to quickly prototype agentic use cases and get a feel for the art of the possible, the risks, the issues and the costs. We’re so early in the adoption curve of modern agentic systems, who wants to commit to spending any money on these new toys without a degree of certainty with the ROI?
Today I’m going to break this model down for you, weigh up its pros and cons, and show you how you can apply the same principles for your own projects.
My Four Pillars of AI Agent Economics
Let’s start with a picture.
Here are the four pillars I believe you need to predict and manage costs if you want to invest in agents. There’s always going to be variations to this, but right now, this is what you need to get you started.
Let’s get into it.
Sovereignty Costs - The Biggest Decision of All
The strategic choice between cloud-based and on-premise AI deployments is critical. While cloud-hosted models offer convenience and scalability, they come with long-term cost uncertainty, security risks, and potential vendor lock-in.
Businesses seeking control over their AI infrastructure must evaluate Sovereignty Costs—the upfront capital investment required to build and operate an on-premise AI agentic solution.
Bringing AI inference and operations in-house isn’t cheap at all. Unlike the pay-as-you-go model of cloud AI services, where businesses pay for token consumption and API-based inference, an on-prem deployment necessitates purchasing and maintaining dedicated infrastructure. Not to mention employing very clever people to look after it.
Key Sovereignty Costs to think about include:
📌 Hardware Investments
AI inference at scale requires high-performance computing resources. Enterprises deploying on-premise solutions must invest in:
AI-optimized GPUs (e.g., NVIDIA H100, AMD MI300) or TPUs for model inference.
High-speed networking hardware to support low-latency inference.
Redundant power and cooling infrastructure to maintain data center reliability.
Edge computing hardware, if real-time local processing is needed.
📌 Language Model Licensing and Training Costs
Unlike cloud-based solutions that offer AI models via subscription or API calls, an enterprise opting for sovereignty must either train their own model or license an existing open-weight model such as Llama 3.1, Falcon, or Inflection AI’s enterprise models. Costs associated with this include:
Model acquisition or fine-tuning fees
Data preparation and labeling costs
Ongoing model retraining and optimisation
Compute power for model refinement and inference
📌 Software Licensing & AI Stack Management
An on-premise AI solution requires an integrated software stack, including:
LLM serving frameworks (e.g., vLLM, TensorRT, or Hugging Face Inference APIs).
Vector databases for embedding storage (e.g., Pinecone, Weaviate, FAISS).
Orchestration tools for agentic reasoning (e.g., LangChain, AutoGen).
Security and compliance software for data governance, access control, and encryption.
Monitoring and logging tools to track AI agent behavior and performance.
📌 Infrastructure Setup & Ongoing Maintenance
Running a self-hosted AI solution is not just about hardware—it requires a long-term operational expenditure (OpEx) commitment:
Data center space rental or build-out costs
IT staffing and AI engineers to maintain, optimize, and troubleshoot AI operations.
Energy costs, which are a major concern, as AI models consume significant power.
Storage & backup solutions, ensuring AI models and vector embeddings are securely maintained.
Why Invest in AI Sovereignty?
Despite the significant upfront investment, on-prem AI deployments can offer long-term strategic advantages that outweigh cloud-based dependencies:
1️⃣ Lower inference costs at scale
The Inflection AI and GAI Insights study found that self-hosted inference can be 10% to 60% cheaper than cloud-based LLM inference within three years.
2️⃣ Data security & compliance
Enterprises in finance, healthcare, and government sectors can maintain full control over intellectual property, user data, and compliance-sensitive information.
3️⃣ Avoiding vendor lock-in
Running inference on proprietary cloud-hosted models exposes businesses to future cost hikes, policy changes, and service disruptions.
4️⃣ Business continuity & resilience
With a private AI deployment, organizations are less dependent on cloud providers, ensuring uninterrupted service even during provider downtimes or geopolitical disruptions.
The Cost Tradeoff: Cloud vs. On-Prem
The decision between cloud and AI sovereignty is ultimately a question of scale and risk tolerance. Businesses handling high-volume AI workloads—such as call centers, banking, and insurance automation—stand to gain the most from on-prem inference, as token-based cloud pricing quickly becomes unsustainable.
While sovereignty requires a high initial CapEx, the ability to optimise infrastructure, negotiate compute costs, and protect cognitive assets makes it a compelling long-term strategy for enterprises aiming to integrate AI deeply into their operations.
Subscription and Consumption Costs - Organised Chaos?
Subscription costs are the first type of recurring costs you’ll come up against. They’re also the easiest to account for because they are fixed costs. For me personally the only ones I have are an Eraser.io subscription for the diagramming tool in my framework and a Jira subscription for my workflow.
While subscription costs provide financial predictability, consumption-based costs introduce a level of uncertainty that can spiral out of control if not carefully managed. These costs scale with usage, meaning an unoptimised system can quickly become a financial burden. Below is a breakdown of the key variable operational expenses in AI agent development and deployment.
Here’s the seven big ticket items you need to keep an eye on:
🧠 #1. LLM Tokens
Every interaction with a large language model (LLM) incurs a token-based fee. Whether it's OpenAI’s GPT models or Anthropic’s Claude (I use both) you’ll pay for the number of tokens processed - both prompt and completion tokens. If you’re not on top of this with your coding, token consumption can explode beyond initial estimates.
One of the best ways to manage this is to use different models for different purposes - In my agent’s LLM Class it has the concept of a cheap, standard and reserved LLM call. As I build features, I work out how much horsepower I need to deliver the prompt.
I have no idea how folks get on using no-code solutions from the point of view monitoring how well their agent code is performing regarding token usage, but my little hack to to add a log trace in my LLM Class that works out the token count and price per objective that the agent completes. That way I can consider optioning my code and usage of LLM calls and know how much each deliverable costs to complete.
Being rate-limit vigilant is good practice but it’s also bloody scary. The screenshot above is from a task my agent ran that resulted in a fully formatted, 33 page Word document. It cost 12 cents. Would have taken a human hours to do the research that went in to that topic in addition to then writing it up. Orders of magnitude more expensive in labour cost than the agent. Being at the coal face building with this technology really does make you wonder what is going to happen to a lot of white-collar jobs from here on out.
🎫 #2. Other Token-based Tools
Many AI-powered services, such as transcription, and AI-enhanced analytics, charge per token processed rather than offering a fixed subscription. Examples include OpenAI embeddings, Leonardo (image generation), Jina (web search) and Whisper (speech-to-text).
This is again where you need to be aware of optimising your AI agent to avoid excessive token consumption, leading to escalating costs.
Here’s a few strategies you can apply to reduce token usage and control expenses without sacrificing performance.
1️⃣ Batching
Batching consolidates multiple queries into a single request, reducing the number of API calls and optimizing token consumption per call.
2️⃣ Token Truncation
LLM pricing is based on the total number of input + output tokens per request. You can limit token usage by setting a max token limit for responses (max_tokens) and truncating long inputs before sending them to the API.
3️⃣ Caching & Reusing API Responses
Repeated API calls for similar queries waste tokens. Implement a caching mechanism to store frequent responses. I use this technique a lot.
4️⃣ Chunking
AI models often process large texts unnecessarily. Use chunking and summarization before sending to an LLM.
🛠 #3. API-based Tools
The third-party API based tools available to your agent is a takeaway menu - there’s loads. Personally, I’m only really a big use of the Google and Twitter one’s. One’s consumption based, the other is a fixed monthly cost and costs a fortune!
💾 #4. Vector Storage
Your agent can’t really work without a vector store and the choice you make is going to be down to cost and the purpose of your agent. There’s loads to chose from (Pinecone, Weaviate, ChromaDB etc.) but I use Mongo Atlas.
If you’re building for yourself, Chroma is a solid choice as you can deploy it alongside your agent. If you’re building for business, you’ll probably run up against architecture policy, standards and security. For this reason, you’d need to consider something more industrial.
The bigger platforms run on hyperscaler kit, provide analytics into your usage, have advanced security features all for a consumption cost paid per month.
💿 #5. Ephemeral Storage
Another component you don’t really want to be working without. I use Redis.
AI agents frequently require temporary storage for intermediate processing. Cloud services charge based on the amount of temporary data stored which can lead to unpredictable costs if you’re not monitoring temporary data storage. The best approach is to implement automatic data purging and garbage collection policies based on your agents behaviors.
💽 #6. Block Storage
Kinda obvious.
🖥 #7. Cloud Consumption
Running AI workloads in the cloud requires compute resources (CPUs, GPUs, TPUs). Training, fine-tuning, and even inference workloads can lead to massive cloud bills, especially when GPUs are involved. Vigilance with unused compute instances is advised at all times to avoid any unnecessary expenses.
If your AI agent runtime needs to be hosted while relying on external LLM inference via API, the best deployment strategy depends on scalability, cost, latency, and operational complexity. Here’s a few ideas:
Serverless (AWS Lambda, Google Cloud Functions, Azure Functions) – Best for lightweight, event-driven AI agents that don’t require persistent memory. Serverless auto-scales, charges only per execution, and simplifies deployment, making it ideal for AI agents that act as stateless API wrappers to LLMs. However, cold start delays and execution time limits can make it unsuitable for low-latency or long-running tasks.
Compute (VMs, Containers, Kubernetes) – Ideal for AI agents requiring persistent state, high concurrency, and ultra-low latency. Running on dedicated cloud VMs or containerized infrastructure eliminates cold starts and provides full runtime control, making it the best choice for memory-intensive agents handling large volumes of real-time interactions. However, it incurs fixed costs even when idle, making it expensive for low-usage scenarios.
Hybrid Approach (Serverless + Compute) – The best of both worlds, using serverless for request-response logic (e.g., handling user inputs and routing API calls) while relying on compute for long-lived processing and state management. This approach balances cost efficiency and performance, ensuring scalability without excessive idle costs.
In summary…
Consumption costs can quickly spiral out of control if not actively managed. A well-governed AI strategy ensures that resources are optimised, unnecessary costs are eliminated, and AI usage aligns with business objectives.
Key cost control strategies include:
🎯 Implement strict monitoring via cost dashboards.
🎯 Set usage limits for tokens, API calls, and compute resources.
🎯 Use hybrid models—on-prem for predictable workloads, cloud for burst scaling.
🎯 Regularly audit AI workflows to remove inefficiencies.
By maintaining rigorous cost oversight, enterprises can harness the power of AI without encountering unexpected financial burdens.
Labour Costs - The Human Element
Last but not least, we need to talk about the cost of production that today (still) requires us humans.
Building, deploying, and maintaining AI agents require a multidisciplinary team spanning software development, AI operations, testing, and data management. Labour costs can be significant, particularly as AI agents move from development to production and require continuous oversight and refinement.
Below are a few key roles involved in the AI agent lifecycle. Some of these are essentially brand new roles in the IT industry as a whole and pretty much don’t even exist. Yet.
AI Developers & Engineers
Who they are: Software engineers specialising in AI/ML development, backend infrastructure, and integrations. Responsibilities:
✅ Build the AI agent’s architecture, APIs, and tool integrations.
✅ Optimise token usage and model interactions for efficiency.
✅ Implement security, compliance, and access controls.
AI Agent Operations Staff (AI Ops / MLOps)
Who they are: The equivalent of DevOps but for AI agents, ensuring smooth deployment, monitoring, and lifecycle management. Responsibilities:
✅ Maintain live AI agents, ensuring uptime and reliability.
✅ Implement monitoring tools for AI drift detection and hallucinations.
✅ Dealing with agents that come to a “dead end” and need help.
Human-in-the-Loop (HITL) Supervisors
Who they are: Experts who review, refine, and validate AI agent responses in critical workflows (e.g., finance, healthcare, customer support). Responsibilities:
✅ Oversee AI decisions for accuracy, fairness, and compliance.
✅ Manually handle edge cases AI struggles with.
✅ Provide feedback loops to fine-tune model behavior.
AI Testers & Evaluators
Who they are: QA engineers and AI model evaluators ensuring reliability and accuracy of AI responses. Responsibilities:
✅ Develop and run automated test cases for AI-driven workflows.
✅ Identify bias, model drift, and performance bottlenecks.
✅ Validate multi-turn agent conversations for logical coherence.
Data Scientists & AI Data Experts
Who they are: Specialists in data engineering, vector search, and prompt optimisation. Responsibilities:
✅ Clean, structure, and manage high-quality training data.
✅ Build and maintain embeddings, vector databases, and search indexes.
✅ Fine-tune AI prompts and model responses to improve performance.
The Bottom Line: Labour Costs Scale with AI Complexity
Labour costs in AI agent development extend beyond coding—they include AI monitoring, human oversight, rigorous testing, and data engineering. While early-stage AI projects may operate with a small dev team, production-scale AI deployments require a diverse team to ensure quality, compliance, and operational resilience.
Businesses must factor in ongoing human oversight and model adaptation costs, as AI agents are never truly "set and forget."
The Real Economics of AI Agents
The real questions that should be asked today in 2025 about deploying agentic systems in the real world must shift from what’s possible to what’s profitable.
Building AI agents isn’t just about automation—it’s about cost control, scalability, and return on investment. Whether you’re a consultant, a researcher, or a business leader, you need to know how much an AI agent costs to run, how much it saves, and when it pays for itself.
The financial model I’ve built shows that, when applied correctly, AI agents can replace or augment knowledge workers at a fraction of the cost.
But please, you must remember this: An AI agent isn’t a magic bullet—you still need infrastructure, governance, and human oversight to make it work at scale. If you’re serious about understanding how agents have business impact, you’ll need a cost model that’s as solid as your both your code, and your conviction.
Curious About What GenAI Could Do for You?
If this article got you thinking about AI agents and their real impact, you’re not alone. Many readers are exploring this new frontier but struggle to separate reality from hype.
That’s exactly why I built ProtoNomics™—a risk-free way to validate GenAI feasibility before you commit resources. No hype. No sales pitch. Just data-driven insights to help you make an informed decision.
If you’re interested, I now run a limited number of GenAI Readiness Assessments each month. If you'd like to see what this technology could do for your business, you can Learn More Here
Or, if you're “just here for the tech” the next article in the series is 👇
Next Time on The Anatomy of an AI Agent
Part 5: Is Industrial Revolution Style Worker Displacement Real?
We’ve all heard the AI is coming for your job narrative. But let’s be honest—up until now, that’s mostly been hype.
Except… the math isn’t lying.
So, what happens next? Are we really looking at an Industrial Revolution moment for white-collar work?
In Part 5, you’ll get to see my cost model in action, working through a hypothetical (but is it?) scenario that goes beyond speculation to find out whether or not Ed Harris was right when he told Maverick:
The future is coming, and you’re not in it
Until the next one, Chris.
Enjoyed this post? Please share your thoughts in the comments or spread the word by hitting that Restack button.