Cutting AI infrastructure costs by 40%: a shared LLM inference platform
IASWITCH designed a shared, auto-scaling LLM inference platform that routes each request to the cheapest model able to handle it. Result: GPU costs cut by 40%, with guaranteed response times.
In brief
- Before: each team ran its own models, GPU costs rising fast
- After: one shared platform, smart routing and capacity matched to demand
- Result: –40% AI infrastructure costs, latency targets met
- Stack: vLLM, Kubernetes, GPU
The challenge
The client needed to serve several language models to several internal teams. Each team deployed its own models without pooling: the GPU bill grew every month and response times were uneven.
The solution
We built a multi-tenant inference platform on Kubernetes with vLLM, per-request autoscaling and smart routing that sends each request to the right model: a small model by default, a larger one only when needed.
The results
GPU costs cut by 40%, latency controlled through explicit objectives (SLOs), and a single observable platform for all teams.
What it means for an SME
Even without dozens of teams, the principle applies: send each request to the cheapest model that can answer it, and only pay for capacity when it is used. It is often the first cost lever on an AI bill that is getting out of hand.
Frequently asked questions
How do you cut the cost of a generative AI platform?
By pooling inference, with per-request autoscaling and multi-model routing (small model by default, large model when needed). IASWITCH cut GPU costs by 40% this way.
What is a multi-tenant inference platform?
It is a single platform that serves multiple models to multiple teams, with isolation, pooled GPU resources and centralized observability.
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