Cost
How to reduce AI infrastructure cost in production
Most production AI bills can be reduced substantially without changing the user-facing feature, because the spend is concentrated in a few habits: oversized context, one expensive model doing every task, no caching, inference sitting away from its data, and idle accelerators. Work the levers in that order. Context discipline and model routing usually deliver the largest reduction for the least product risk.
The levers, in order of payback
| Lever | What it changes | Typical effort |
|---|---|---|
| Context discipline | Fewer input tokens per call through better chunking and filters | Low |
| Model routing | Cheap model for simple tasks, premium only where it wins | Low to medium |
| Prompt and response caching | Repeated prefixes and identical queries stop re-billing | Low |
| Batch and async | Non-interactive work moves to cheaper batch endpoints | Medium |
| Placement | Inference next to the data removes cross-region and cross-cloud egress | Medium |
| GPU utilization | Autoscaling, right-sizing and committed capacity for self-hosted models | High |
Instrument before you optimize
Attribute every model call to a tenant, a feature and an outcome. Without that attribution you cannot tell an expensive feature from an expensive customer, and you will cut the wrong thing.
Do not trade correctness for cost silently
Every cost change is a behaviour change. Run the same evaluation set before and after each lever, and keep the results next to the savings. A cheaper answer that is wrong more often is a support cost, not a saving.
Self-hosted is not automatically cheaper
Open-weight models on your own GPUs can beat managed pricing at sustained high volume, and lose badly at spiky low volume where the accelerators idle. Model both against your real traffic curve before committing, and include the engineering cost of running the serving stack.
How Datics helps
We run inference-economics and cloud cost reviews on live AI workloads across AWS, Azure and Google Cloud, then implement the changes inside your product.
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