When I first started working on Generative AI initiatives, cost was not the primary concern.
Like many teams, we were focused on proving capability:
Can the model understand our data?
Can it generate usable outputs?
Can we integrate it into real workflows?
The early demos were impressive. The costs looked manageable. Everything worked — at least on the surface.
Things changed the moment these systems moved closer to production.
When LLMs Meet Real Usage
LLMs behave very differently from traditional software systems.
Costs don’t scale linearly with users or infrastructure. They scale with:
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Prompt length
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Context size
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Model choice
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Retry behavior
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Error handling
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User behavior
A small architectural decision — an extra paragraph of context, an unnecessary retry, a default model choice — can multiply costs without anyone noticing immediately.
By the time it becomes visible, the budget damage is already done.
Why This Is Not a Finance Problem
One of the biggest misconceptions I see is treating GenAI cost as a finance or procurement issue.
In reality, cost is driven almost entirely by technical and delivery decisions:
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Which model is used for which task
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How prompts are designed
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How often calls are made
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How errors and hallucinations are handled
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How results are cached or reused
As a Technical Project Manager, ignoring this means losing control of delivery.
Owning it means enabling sustainable scale.
The Cost Traps I’ve Seen Repeatedly
Across different teams and projects, the same patterns show up:
Overpowered defaults
High-end models are used everywhere “just in case”, even when simpler models would deliver acceptable results.
Prompt inflation
Prompts grow over time as edge cases are added, without revisiting their cost impact.
Invisible retries
Failures and hallucinations trigger silent retries, compounding cost with every error.
No guardrails
No budgets per feature, no alerts, no clear ownership of usage.
None of these are malicious.
They are natural outcomes of moving fast without cost being a first-class concern.
What FinOps for LLMs Actually Means in Practice
For me, FinOps in AI is not about cutting costs aggressively.
It’s about making trade-offs explicit.
Every GenAI system makes implicit decisions about:
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Accuracy vs cost
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Speed vs depth
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Automation vs human review
FinOps forces those decisions into the open and ties them to real business outcomes.
Once cost is visible, better engineering decisions naturally follow.
What Has Worked in Real Projects
In practice, sustainable AI delivery comes from a few disciplined habits:
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Comparing multiple models for the same use case
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Matching model capability to business criticality
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Monitoring cost per request and cost per outcome
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Treating hallucinations as both quality and cost issues
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Designing graceful degradation instead of all-or-nothing behavior
The key shift is simple but powerful:
Cost becomes a delivery metric, not a surprise.
A Shift in How I Think About AI Projects
Traditional projects end when features are delivered.
AI projects don’t.
They continue to consume value — or cost — every day they run.
That changes the role of a Technical Project Manager:
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From delivery-focused to lifecycle-focused
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From feature tracking to outcome tracking
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From technical coordination to economic stewardship
This is not theoretical. It’s operational reality.
Closing Thought
The most impressive AI systems are not the most expensive ones.
They are the ones that:
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Deliver consistent value
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Earn trust
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Scale responsibly
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And remain economically viable over time
AI Cost Management and FinOps for LLMs are not optional add-ons anymore.
They are part of what it means to run AI projects professionally.
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