DevOps Consulting for AI & ML Startups
AI startups burn cash in a way SaaS never did: GPUs are 10–30× the price of general compute, and they idle exactly like the cheap kind. At the same time, model serving is a genuinely hard scaling problem — cold starts measured in minutes, latency budgets measured in milliseconds — and the path from a researcher’s notebook to production is usually a person, not a pipeline. Infrastructure is the difference between runway and a bridge round.
The GPU bill is your second-biggest expense
Training boxes idle between runs, inference fleets are sized for peak and never scale down, and nobody can say which experiment or customer the spend belongs to. GPU waste routinely rivals payroll.
How we fix it — Scheduling and queueing so training GPUs run hot or shut off, spot/preemptible capacity with checkpointing for fault-tolerant runs, inference autoscaling — to zero where cold starts allow — and per-team, per-model cost attribution so the burn has names on it.
Model serving falls over or overspends — usually both
LLM and vision workloads have brutal cold starts and spiky demand: scale too slowly and requests time out; keep everything warm and you pay for a GPU fleet doing nothing at 4 a.m.
How we fix it — A serving platform tuned for the workload: request batching, model caching on warm nodes, KEDA-driven autoscaling on queue depth rather than CPU, GPU sharing (MIG/time-slicing) for smaller models, and latency SLOs so scaling decisions trace to user experience, not vibes.
Research-to-production is a person, not a pipeline
Models ship by a researcher handing a notebook to whoever knows the cluster. Deploys take days, rollbacks are folklore, and nobody is certain which model version is serving which customer.
How we fix it — CI/CD for models: versioned artifacts and reproducible container builds, automated evaluation gates before promotion, progressive rollout with instant rollback, and every model in production traceable to the exact code, weights and data that made it.
Model weights and training data are unguarded crown jewels
Your weights are the company. They sit in an open S3 bucket, on researchers’ laptops, and on shared GPU boxes everyone can SSH into — while enterprise customers start asking how their data is isolated from your training runs.
How we fix it — Least-privilege access to weights and datasets, encryption at rest and in transit, isolated training environments, and an auditable line between customer data and anything that trains — the security story that passes enterprise procurement.
Infrastructure that extends your runway.
For an AI startup, platform engineering is unit economics. The same cluster, scheduled properly, does twice the work — and investors and enterprise buyers both read your infrastructure maturity as a proxy for whether the company survives scale.
- GPU economicsUtilisation dashboards per team and model, spot capacity with checkpointing, scale-to-zero inference — burn that tracks usage, not fear of cold starts.
- Serving SLOsLatency and availability targets for inference, with autoscaling driven by the queue — not by CPU graphs that mean nothing on GPU nodes.
- ReproducibilityEvery production model traceable to code, weights and data. When output changes, you can answer why.
- Data boundariesCustomer data provably isolated from training unless contracted otherwise — the question every enterprise buyer now asks first.
Two free tools, no signup: estimate your cloud waste with the cost calculator, or score your production posture on the security scorecard. Fixed-scope packages and prices are on the pricing page.
Frequently asked
Can you actually cut our GPU spend without slowing research?
That is the aim, and the structure of GPU waste makes it realistic: the waste is rarely in the runs themselves. It sits in idle allocated instances, missing queueing, on-demand pricing for interruptible work and inference fleets that never scale down. Scheduling and spot strategy target exactly that layer, without touching a single experiment.
Do you work with vLLM, Ray, Kubeflow and the rest of the ML stack?
Yes — we build the Kubernetes platform underneath and operate the serving/orchestration layer on top: vLLM and TGI for LLM serving, Ray for distributed workloads, KEDA for queue-based autoscaling, plus the Terraform, GitOps and observability that make it a platform instead of a pile of tools.
We train on one cloud and serve on another. Is that a problem?
No — it is often the right call, since GPU pricing and availability differ wildly between providers. We run multi-cloud setups as code with unified deployment and monitoring, so arbitrage between clouds is a pricing decision, not an operational burden.
Spend your runway on models, not idle GPUs.
A free audit of your training and serving infrastructure — utilisation, unit costs, scaling behaviour and the security of your weights — with a prioritized fix list and estimated savings.
NO SIGNUP · NO OBLIGATION · REPORT IS YOURS TO KEEP