AI is not too expensive, uncontrolled inference is. This post breaks down why costs, latency, and behavior feel unpredictable today, and how programmable inference gives teams the control they’ve been missing.
George Nie
December 2, 2025
Over the past two years, the cost per million LLM tokens has dropped dramatically, in some cases by ~99% according to Stanford research summarized by TechCrunch. And yet, across startups and enterprises, AI spend is climbing faster than ever.
Why? Because the bottleneck isn’t training anymore. It’s inference: the real-time, recurring cost of serving AI to users at scale.
Forbes calls this shift the rise of the AI inference economy, and McKinsey projects the industry will pour $3.7T–$7.9T into AI compute infrastructure by 2030.
Here’s the reality:
LLMs aren’t inherently expensive.
Uncontrolled inference is.
Most teams never realized they could control inference, the same way they control compute, caching, or storage. That’s beginning to change.
When teams say “AI is too expensive,” they’re usually pointing at the wrong culprit.
Model prices have dropped. Open-source models have caught up. More efficient architectures are emerging every month. The real issue is that most teams haven’t yet recognized the advantages of controlling inference, such as
● Which model gets used
● When to escalate to higher-quality or low-latency tiers
● How much “thinking” the model should do
● When RAG or guardrails should be applied
● How to route or fall back when requests vary in complexity
Inference, in practice, it’s a layer you can engineer, tune, and optimize, just like every other part of your stack.
Teams pay too much, not because models are overpriced, but because the inference layer is left unchecked.
It’s common to push every request through the most powerful model available.
But newer reasoning models are significantly more expensive:
● McKinsey estimates that OpenAI’s o1 costs ~6× more than GPT-4o at inference time
●TechCrunch reports some reasoning models reaching ~$600 per million output tokens
These models are incredible. But they should be used selectively, not universally.
Even without long context windows, token usage balloons fast through:
● verbose chain-of-thought
● agentic systems producing long internal traces
● prompts that encourage models to “think step-by-step” even when unnecessary
As Forbes notes, longer sequences and heavier compute can increase cost 100×–10,000×. That is to say, small inefficiencies compound quickly.
Without routing rules:
● Simple tasks use expensive models
● Retries hit the same high-cost endpoint
● Workloads don’t match model capabilities
This is one of the biggest sources of preventable costs, and one of the easiest wins.
RAG, governance, and guardrails provide:
● accuracy when grounding to your data
● safety/compliance for sensitive content
● consistency for regulated use cases
But when applied by default to every request, they add:
● extra inference calls
● vector DB lookups
● higher latency
● unnecessary request overhead
You want to use these modules strategically, where they introduce value, not indiscriminately.
An Inference Strategy is the missing layer that turns inference from a runaway cost center into a controllable, predictable system.
At its core, it’s simply making conscious decisions about:
● Model Tiering: Use the right-sized model for the right task.
● Routing Rules: Escalate to stronger models only when needed.
● Token Budgeting: Avoid unnecessary verbosity and overthinking.
● Selective RAG / Governance: Apply safety or retrieval only where accuracy, compliance, or risk demand it.
● Fallback Logic: Ensure you don’t repeat expensive calls unnecessarily.
No heavy technical jargon. It’s just structured decision-making for how AI should behave across your product.
And once teams adopt this mindset, their inference layer becomes intentional, not accidental.
Let’s keep this extremely straightforward.
100,000 monthly requests.
Every request uses a premium reasoning model. Guardrails and RAG applied to every call. This ends up in high, unpredictable cost.
● 80% of requests → efficient model (fast, affordable)
● 15% → mid-tier frontier model
● 5% → premium reasoning model
● RAG + guardrails applied only when needed
This leads to: meaningful cost reduction, no loss in user-facing quality, and lower latency where it matters.
Same product. Same user experience. Just more control. And much lower cost.
CLōD exists to give developers and teams control over inference without complexity.
Through a single unified API, you can:
● Control which model each request uses
● Control speed, cost, and latency
● Control routing rules & fallback
● Control when governance or RAG are applied
● Control model behavior with predictable outcomes
All vendor-agnostic, all under one roof.
This aligns directly with our core narrative:
More control. Less complexity. Your models, your rules.
“LLMs aren’t too expensive. Uncontrolled inference is.”
Hidden costs come from defaults, not decisions.
And the solution isn’t to spend less on AI, it’s to take control of the inference layer.
Teams that adopt an inference strategy build better products, with more predictable costs, and far more confidence in how AI behaves in production.
👉 If you want to see how an effective inference strategy can be implemented through a single API, give it a try by joining the CLōD platform today.
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