GPU clusters
Multi-cloud GPU capacity with drivers, operators, and scheduling handled. Spot where it's safe.
What we build
LLM & AI infrastructure
GPUs are the easy part. Utilisation is the discipline that keeps the roadmap funded.
The problem
The model works. The infrastructure bill doesn't.
Between a notebook and production sit GPU clusters that cost more per hour than servers cost per month. Inference has to stay fast while the meter runs.
What we build
Inference serving
Serving stack tuned to p99 targets. Batching and caching that hold under load.
Training pipelines
Reproducible runs with checkpointing and queueing. A failed job resumes, never restarts.
Model ops
Registries, versioning, and A/B serving. Which model answered is never a mystery.
GPU economics
Utilisation dashboards and cost-per-token. Idle GPUs get reclaimed automatically.
AI observability
Latency, throughput, and quality signals on one pane. Regressions caught before users notice.
How an engagement runs
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Profile
Your models, traffic, and latency targets. The hardware follows the workload, not the hype.
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Design
Cluster topology, serving stack, and scaling strategy, with the cost curve modelled upfront.
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Build
Same platform discipline as the rest of your infra: code, pipelines, observability.
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Tune
Utilisation, batching, and spot mix optimised against real traffic. The bill bends down.
- 0
- Training GPU cost between runs, via a scale-to-zero node group
- 2 modes
- Cloud-managed or NVIDIA-native GPU, same codebase, one Helm flag
- 3 clouds
- Same platform deployed unchanged to AWS, Azure and Google Cloud
Why SIPATECH
We build it. We run it. We stay.
Engineers, not deck-writers
Every recommendation comes from real production experience. We've designed and operated systems at the scale most teams only read about.
Full-stack, not single-discipline
DevOps, SRE, platform engineering, security, and FinOps under one roof. No handoffs between vendors, no gaps between disciplines.
We leave you self-sufficient
Our goal is to make ourselves unnecessary. Every engagement ends with documentation, runbooks, and a team that understands what it runs.
On the merits
Managed or open-source, decided on the merits.
Every build-versus-buy call comes down to cost, control, and who carries the pager. We make it on evidence, not on a vendor relationship or a default.
Total cost, not sticker price
Licence, run-rate, and the engineering hours to operate it. The cheap option is often the expensive one.
Managed where it earns its keep
Pay someone else to run it when your team's time is worth more elsewhere. Not as a reflex.
Open-source without the tax
Self-host when control and cost justify it, with the operational plan to back it. No orphaned clusters.
No lock-in by default
Portable foundations first. You stay free to switch when the economics change.
Right tool per layer
The best database, queue, or runtime for the job, not one vendor's whole catalogue.
Decisions you can audit
Every build-versus-buy call written down with its reasoning. Revisit it when the numbers move.
Ready when you are
Thirty minutes with the architect who would build it. No deck, no account manager, no follow-up sequence.