Engineering Blog
Why AI Cost Is the New Bottleneck — And How Efficient Inference Changes What’s Possible
As AI moves from pilots to production, inference cost becomes the real constraint. Efficient inference shifts the economics and unlocks scale.
For the last two years, the conversation has been about capability
Bigger models, longer context windows, and more impressive demos.
But as organisations move from experimentation to real-world deployment, a different constraint shows up quickly: cost.
Not the headline cost of training a model — but the ongoing, operational cost of running AI every day.
Inference is where the money is spent
Most enterprises are not training large language models from scratch. They are running inference:
- Answering questions
- Powering chatbots
- Analysing documents
- Supporting agents that operate continuously
These workloads run 24/7, at scale, and often with strict latency requirements.
That means the economics of inference — cost per token, power per request, and throughput per watt — determine whether AI remains a pilot or becomes core infrastructure.
Power, not models, is now the limiting factor
Across the world, AI deployments are running into hard limits:
- Power availability
- Cooling capacity
- Energy costs
Dense GPU clusters are expensive to run and increasingly difficult to place, especially outside hyperscale environments.
This is forcing a shift in thinking. Instead of asking “what’s the biggest model we can run?”, organisations are asking:
- How many users can we support concurrently?
- What is our cost per interaction?
- Can we afford to scale this globally?
Efficient inference changes the equation
Inference-optimised systems — designed specifically to maximise tokens/sec/watt — fundamentally change what’s possible.
When each AI interaction consumes less power and delivers more throughput, organisations can:
- Support more users at the same cost
- Deploy AI into customer-facing workflows
- Operate in regions where power is constrained
- Plan budgets with confidence
This is why inference efficiency is becoming the foundation of serious AI platforms.
The takeaway
The next phase of AI adoption won’t be won by the biggest models alone. It will be won by platforms that deliver predictable performance, scalable economics, and sustainable operations.
Inference efficiency isn’t a technical detail — it’s the difference between AI as an experiment and AI as a business capability.