AI INFRASTRUCTURE / INFERENCE PLATFORM / APPLIED AI
A model runs on two things: compute, and the design that holds it together. YOLAIZ takes responsibility for the full stack — building and operating GPU clusters, running the inference platform and token gateway on top, and delivering the systems that put them to work.
Platform status
Illustrative monitoring dashboard
Uptime
99.95%
Throughput
68.4k tok/s
p95 latency
0.91s
Active nodes
312/316
Figures shown are illustrative sample data.
Business
Compute, delivery, and application. These are one continuous stack, and a gap in any layer keeps AI out of production. YOLAIZ covers all three itself, and brings partners in where scale requires it.
Layer 01
AI INFRASTRUCTURE
Design, build and operation of GPU clusters, plus dedicated capacity on hourly or monthly terms. From high-density power and cooling through scheduler and storage selection to full SRE operations.
Learn moreLayer 02
INFERENCE PLATFORM
An LLM gateway consolidating multiple large language models behind one endpoint, inference optimisation, token cost visibility and chargeback, fine-tuning, and evaluation infrastructure.
Learn moreLayer 03
APPLIED AI
Operational AI agents, RAG platforms for Japanese-language documents, MLOps / LLMOps adoption, AI strategy and ROI modelling, internal guidelines, and capability transfer.
Learn moreBy the numbers
Since founding we have supported the compute platforms of research institutions, manufacturers, financial services firms and SaaS providers.
0 GPUs
GPUs under our operation
0%
Uptime, trailing 12 months (SLA target 99.5%)
0
Models available via gateway
−0%
Inference cost reduction (median across engagements)
Approach
Most AI projects stall on operations and unit economics, not on technology. We fix the target uptime, cost per unit and on-call model before choosing an architecture.
01
GPU acquisition cost, power, token pricing and human operations are stacked up first. We draw the line at which the business works, then select the technology. We do not propose architectures that merely run.
02
Node evacuation on failure, regression on model updates, permissions and audit logs. Everything that will certainly happen in production is written into the design before anything is built.
03
Infrastructure-as-code, runbooks and evaluation datasets are part of every delivery. Building for the assumption that the client will bring it in-house is, in practice, what produces the longest relationships.
Pricing simulator
Estimate the monthly cost of dedicated GPU capacity and inference tokens. Actual quotations vary with architecture, term and service level.
Estimated monthly
¥0/mo
Estimates only. Formal quotations are issued individually.
Discuss this configurationEstimated monthly
¥0/mo
Estimates only. Formal quotations are issued individually.
Discuss this configurationCase studies
Published in anonymised form under our clients' confidentiality agreements.
−64%
Engineering hours spent searching drawings
Manufacturer, Client A (approx. 1,000 employees)
Every design change required finding comparable historical drawings, but naming conventions diff…
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Token spend
Financial services provider, Client B
Three years of independently signed departmental AI contracts had left the organisation with no …
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Training job queue time
Research institution, Client C
Researchers were doubling as cluster administrators, and research halted every time an incident …
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The most common questions we receive are "how many GPUs should we buy" and "should we own or rent". We welcome conversations long before requirements are settled.