Modern AI makes it easier than ever to build impressive products.
What it does not make easy is running those products sustainably once real users show up.
They can fail due to the cost of running them which grows faster than the value they deliver.
Building Is Cheap. Inference Is Not.
Most AI discussions focus on models, prompts, and architecture.
But the real constraint shows up after launch: inference cost.
Unlike traditional software, AI systems:
- Get more expensive as usage increases
- Charge per interaction, not per deployment
- Punish poorly scoped features at scale
If inference strategy isn’t considered early, a product that works technically can become financially unviable very quickly.
Where Overengineering Hurts the Most
Often times teams reach for complex AI systems too early:
- Multi-agent workflows before understanding real usage
- Heavy RAG pipelines without clear retrieval needs
- Always-on inference where simple logic would work
- AI added everywhere instead of where it actually matters
These choices usually come from good intentions but, they lock products into high, recurring costs that are hard to unwind later.
The Missing Layer: Product and Brand Systems
One of the most overlooked factors in AI cost control is product clarity.
When UX, language, and brand systems are unclear:
- Users overuse AI features
- Inputs become noisy and inefficient
- Inference volume grows without increasing value
Clear workflows, intentional triggers, and well-designed interfaces reduce unnecessary AI calls and improve outcomes at the same time.
Good design isn’t just aesthetic. It’s a cost-control mechanism.
How I Think About Sustainable AI Products
I now approach AI-enabled products with a few guiding principles.
1. The workflow is the product
AI should support a specific decision or action and not exist as a generic capability.
If removing the AI doesn’t break the workflow, it probably doesn’t belong there yet.
2. Inference should be intentional
Treat AI calls like a metered resource.
That means:
- Gating AI behind meaningful actions
- Caching results where possible
- Using the cheapest model that gets the job done
- Deferring or batching inference when appropriate
3. Start narrow, then earn complexity
Ship the smallest useful AI feature first.
Real usage data will tell you where sophistication is actually needed and where it’s just theoretical.
The Real Scaling Problem
Scaling AI products isn’t just a technical challenge.
It’s a product, design, and financial one.
Teams that treat AI as infrastructure - scoped, intentional, and measured - build products that last longer, cost less, and actually serve users.
I’m curious how others here are thinking about inference strategy as part of product design.
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