AI’s cost curve is flattening the wrong way. While attention remains fixated on GPUs and token rates, the real budget killers—power, observability, orchestration, evaluation, and egress—are compounding beneath the surface.
OpenAI has committed to $300 billion in compute contracts from Oracle, requiring 4.5 gigawatts of continuous power—more than the output of two Hoover Dams.
AI infrastructure already consumes more than 4% of U.S. electricity, with data centers responsible for over 2.2% of total U.S. CO₂ emissions as of 2024.
A representative enterprise AI stack might spend 20–40% on observability, 10–25% on evaluation, and only 15–35% on actual inference.
A team spending $10,000/month on model usage can see total costs scale to $45,000–55,000/month after factoring in orchestration, retries, logs, test data, and governance.
AI data centers globally are projected to emit 2.5 billion metric tons of CO₂ between now and 2030—on par with annual emissions from commercial aviation.
61% of U.S. adults report concern over AI’s energy consumption; yet most enterprise budgets track only token counts, not total system costs.
The future of AI at scale won’t be determined by the speed of GPUs but by the ability to measure and control the full-stack cost structure. Power is becoming a competitive differentiator. Observability is no longer optional. And routing, governance, and task-based costing are table stakes for ROI.
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The True Cost of AI
The costs that determine AI’s ROI are not where most leaders look.
At the height of the Internet boom, Global Crossing was hailed as a foundational tech player. After going public in 1998 at $19/share, its stock peaked at over $60/share. By 2001, it had built a global fiber network that connected 27 countries, over 200 major cities, spanned 100,000+ route miles, and housed 700+ Points of Presence. Its $47 billion market cap seemed justified—until it wasn’t.
That same year, Global Crossing filed for bankruptcy. The company that spent $12–15 billion building the Internet’s backbone was sold for roughly $250 million—just 2% of its total buildout cost.
The same thing is happening now: most frontier models cost more to deliver services than they generate in revenue. Sound familiar? That’s the nature of new technologies—companies take moonshots. Some end up becoming the next Google. Others create unexpected benefits like memory foam, infrared ear thermometers, and myriad other ancillary inventions.
Infrastructure can be strategic. But it is not inherently valuable. Results are. AI leaders should take note.
The New Billion-Dollar Bet
OpenAI reportedly signed a deal with Oracle to purchase $300 billion in compute capacity over five years—surpassing even Nvidia’s five-year revenue totals:
Fiscal Year | Nvidia Revenue |
|---|---|
2025 (est.) | $130.5B |
2024 | $60.9B |
2023 | $26.97B |
2022 | $26.91B |
2021 | $16.68B |
The Oracle deal would demand 4.5 gigawatts of electricity—more than the output of two Hoover Dams, or equal to the residential consumption of 4 million U.S. households. For context, that’s about the same energy draw as the entire Boston metro area during peak demand.
The market isn’t just chasing model performance—it’s building an industrial-grade backbone. But that scale comes at a hidden cost.
What Is the True Cost of AI?
Most companies budget for GPUs (the processors that run AI from NVIDIA and others) and model inference (inference is what happens when you ask ChatGPT a question). That’s only a fraction of the actual cost. If you don’t understand the full picture, you risk underestimating AI expenses.
True enterprise AI cost includes:
Observability & Monitoring: Logs, traces, LLM-specific metrics, eval pipelines
Evaluation: Online/offline tests, human-in-the-loop review, golden sets
Data Transfer (Egress Fees): Charges from cloud providers when moving data out of their networks (e.g., to the internet or another provider).
Embeddings & Vector DB I/O: Storage and movement of high-dimensional representations for search and retrieval.
Orchestration: Agents, function calls, retries
Governance: Redaction, policy checks, Role-Based Access Controls (RBACs), audit trails
Vendor Coupling: Lock-in premiums from bundled infra + models. If you are signing multi-year contracts for AI, keep in mind that historically costs go down over time not up despite the topic of this newsletter.
These costs are often subsidized by venture capital in early-stage platforms—but that subsidy is temporary. Real-world AI deployments need value-aligned cost structures, not just usage-based billing.
Enterprise AI Cost Structure
I tried to come up with a typical breakdown from my experience talking to hundreds of people deploying AI. This is a best guess—not a benchmark—but it’s a fair representation.
Layer | Potential Share | Notes |
|---|---|---|
Observability/Monitoring | 20–40% | High-cardinality logs, continuous eval pipelines |
Inference (LLMs/APIs) | 15–35% | Influenced by prompt size, compression, retries |
Evaluation (HITL + tests) | 10–25% | Cost of test data, tokenized review, QA labor |
Data Transfer & Movement | 5–15% | Cross-cloud retrieval, batching inefficiency |
Vector DB & Retrieval | 5–10% | Embedding storage and real-time search QPS |
Orchestration/Agents | 5–10% | Retry logic, tool use, fan-in/fan-out |
Governance & Security | 3–8% | PII redaction, audit compliance, policy checks |
Scenario: A team starts with $10K/month in model inference spend. They scale usage 5×.
Without optimization, their total monthly cost balloons to $45–55K:
+$14K: Logs, traces, evaluation pipelines
+$9K: Reviewer time, golden set maintenance
+$5K: Retrieval, egress, data movement
+$7K: Combined cost of vector DB, orchestration, and governance
+$10K: Growth in inference due to prompt expansion and retries
The takeaway: You don’t have an LLM cost problem. You have a system architecture problem.
Implementation Playbook: Cost Control Without Sacrifice
Abstract Models via Routers - Use tools like LiteLLM or OpenRouter to standardize APIs. This enables fallback logic, A/B testing, and rapid model switching—without code rewrites.
Edge Metering + Access Control - Enforce limits at the API gateway (e.g., Kong Konnect). Kong acquired OpenMeter, enabling native usage metering—ideal for agent workloads.
Define a Unit of Value - Normalize spending to cost per task, not per token. For example: “cost per reconciled invoice,” “cost per generated PRD.” Budget to outcomes, not volume.
Optimize Context Usage - Summarize, cache, and cap max token limits. Use RAG to limit prompt bloat. Enforce per-feature context budgets.
Make Observability Outcome-Aware - Monitor latency, failure rate, and cost per request. Tie metrics to cost per outcome across products and teams.
Automate Guardrails - Use retry budgets, timeouts, and circuit breakers. Enforce rate limits per user. Prevent long-tail cost explosions.
Build FinOps for AI - Align product, platform, and finance teams. Tag costs per feature. Run weekly budget variance reviews. Negotiate committed-use discounts.
Where AI Leaders Go Wrong
Avoid these common pitfalls:
Assuming inference is the only major AI cost driver
Scaling usage before defining a unit of value
Using duplicate observability tools that ingest the same data
Locking into a single provider with no exit path
Treating evaluation as a project milestone vs. ongoing process
Business Value of Better AI Operations
Done right, AI operations deliver significant gains:
30–50% lower TCO via routing, right-sized context, and telemetry consolidation
Vendor leverage through portable APIs and eval abstractions
Faster experimentation with native metering and automated guardrails
Revenue enablement by productizing internal APIs into usage-priced offerings
Final Thought
In AI, cost isn’t just an output metric—it’s a design decision. Smart orgs track every dollar spent against business outcomes, not just tokens burned. In the end, the companies that master system-level cost visibility will be the ones left standing—just ask the ghost of Global Crossing.
Kong Konnect + OpenMeter (API control + usage‑based monetization) -Unify enforcement, security, and metering so you can productize APIs/LLMs/event streams and bill precisely—built for agentic, high‑variability workloads.
LiteLLM (LLM router for portability) - A lightweight abstraction to call many models with one API, enabling fallbacks, A/B tests, and rapid provider switching.
OpenRouter (Unified LLM gateway) - Route to multiple frontier and open models with dynamic policy and failover; useful for cost/quality routing and uptime resilience.
Prompt of the Week: Personal AI Infra Cost Audit
AI tools promise speed, savings, and smarter results. But in practice, the work doesn’t always disappear—it just shifts.
When a team uses ChatGPT, Claude, or another AI system to take over a task, the surface-level benefits are easy to see. A report gets written faster. An email reply goes out sooner. A meeting summary shows up like magic.
But what’s harder to see—and often ignored—are the extra steps behind the scenes:
Someone has to format the data.
Someone rewrites a bad output or edits the tone.
Another person checks the final draft.
And if something goes wrong, it could cost time, trust, or money to fix.
All of that work adds up. If you don’t track it, your AI projects may look efficient when they’re not. You may end up scaling tools that create more hidden costs than value.
What This Prompt Does
Not everyone is analytical of their work. Sometimes it’s hard to recognize all the small muscle-memory steps in a process. To help you with that this prompt walks you through a full audit of one AI-assisted task. Also these prompts aren’t just for the task at hand but to help you understand how to get better outputs.
For example, much of our time is spent reformatting prompts. I included a simple output to this prompt so I didn’t have to reformat. For you, it might make sense to start considering including a couple of examples of what good outputs look like.
Break the task down into each step—before, during, and after AI is involved
Estimate the time and cost for each part of the process
Identify hidden problems like rework, errors, or compliance risks
Calculate whether the workflow is actually saving money and whether it gives a result that’s better and faster than a human can do it alone
Decide whether to keep, improve, or stop using AI for that task
It uses short interview questions to help you tell the full story of the workflow. At the end, it gives you a structured output with:
A clear cost breakdown
A simple summary in bullet form
A clear verdict: keep, improve, or stop
Suggested next steps
You can paste it into ChatGPT, Claude, Gemini, or any other modern LLM.
Pro Tip: One way to avoid reformatting is to provide an example of the format you want. See what I did below for a cost table. However, you could include a report or other example as an attachment if the output is reasonably complex.
You are a helpful AI advisor. Your job is to interview me and find the full cost of one task I’ve given to an AI tool (like ChatGPT or Claude).
Ask me each section one at a time. Wait for me to answer before going to the next. Use plain language and short questions.
---
STEP 1: What's the task?
- What task are you using AI for?
- Who owns it (which team or person)?
- How often do you do it? (daily, weekly, monthly?)
- What tool or model are you using?
---
STEP 2: How does it work?
- Walk me through each step of the task from beginning to end.
- Who does what? (Who sets it up? Who checks it?)
- Is there a handoff or approval step?
- What kind of info or files are needed to start?
---
STEP 3: How much time does it take?
- For each step, how long does it take (in minutes)?
- How many people are involved?
- What part takes the longest or causes problems?
---
STEP 4: What does it cost?
- How much do you pay for the AI tool per run? (or per month?)
- Do you use other tools (dashboards, storage, RAG, etc.)?
- What’s the average hourly rate of the people involved?
---
STEP 5: Any hidden costs?
- How often do you have to fix the AI output?
- How long do fixes take?
- Has bad output ever caused problems? (delays, customer issues, compliance?)
---
STEP 6: Is it worth it?
- What do you think this task saves you each time?
- What would improve the process?
- Should this task stay with AI, be improved, or be brought back to a person?
---
✅ FINAL OUTPUT FORMAT:
**Summary (bullets):**
- Task and how often it's done
- Tools used
- Total people hours per month
- AI and tool cost per month
- Fixes and issues (if any)
- Your final decision: KEEP, FIX, or STOP
**Cost Table (use rough numbers):**
| Cost Type | Monthly Cost |
|------------------|--------------|
| People Time | $___ |
| AI Tool Cost | $___ |
| Other Tools | $___ |
| Rework/Fixes | $___ |
| **Total Cost** | **$___** |
Also list:
- 1–2 things you’d change to make this task work better
- Any risks or problems to keep an eye on
I appreciate your support.
Your AI Sherpa,
Mark R. Hinkle
Publisher, The AIE Network
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