Industry · AI / ML

Ship AI to production.
Without torching the runway.

For an AI startup, the cloud bill is mostly GPUs, and a GPU doesn't care whether it's serving requests or sitting idle at 3 AM. We help venture-funded AI teams cut GPU spend, scale LLM inference cleanly, and stand up MLOps that survives the jump from demo to production, without hiring a platform team you can't afford yet.

Book a GPU Cost & Scaling Review → DevOps Services

Why AI Startups Are Different

GPUs are the budget. Spend them like it.

Biggest Wins

GPU Cost Control

GPU compute is the single largest line item for almost every AI startup, and idle GPUs bill exactly the same as busy ones. We attack utilisation first: spot and reserved capacity, MIG partitioning, right-sizing, quantisation, and continuous batching. See our deep dive on Kubernetes GPU cost for LLM inference, or start with the cloud waste calculator.

Spot GPUs for training, reserved and committed for steady inference
MIG partitioning and time-slicing to pack small models
Right-sizing: stop paying for an A100 to serve a 7B model
Quantisation (INT8, FP8) and continuous batching for throughput

Runtime Cost Governance

Cutting the bill once is easy; keeping it down as usage grows is the hard part. We put FinOps guardrails around live AI spend, so cost is a number product owns, not a month-end surprise. More in FinOps for AI runtime cost governance and what is FinOps.

Cost-per-inference and cost-per-token dashboards
Per-model, per-tenant, and per-feature spend attribution
Budget and anomaly alerts wired to Slack, not the invoice
Showback so product can price features on real unit cost

Model Serving & Inference

Serving a model in a notebook is not serving it to a million users. We design inference infrastructure that autoscales on real demand, scales idle models to zero, and handles the cold-start problem before it pages you at midnight.

vLLM, TGI, and Triton serving with continuous batching
Autoscaling on queue depth and tokens-in-flight, not CPU
Scale-to-zero for long-tail models, pre-warming for hot ones
Multi-model routing and GPU sharing on one cluster

MLOps Pipelines

MLOps for an early-stage team isn't a twenty-tool platform, it's reproducibility and a deploy path you trust. We wire training, evaluation, and deployment into one pipeline so a new model can't silently ship a quality regression to production.

Versioned data, code, and config for reproducible runs
Evaluation gates that block regressions before deploy
Model registry with one-click rollback
The same CI/CD your application code already uses

Reliability & On-Call

AI systems fail differently: out-of-memory on long context, GPU node death, provider rate-limits, silent quality drift. We bring real SRE practice to AI as part of our SRE services, including the patterns in our Azure SRE Agent production playbook.

Latency and availability SLOs with burn-rate alerts
Runbooks for GPU failure, OOM, and provider throttling
Graceful fallback to a smaller or cached model
On-call that pages on user impact, not CPU noise

Prototype to Production

The demo that wowed the board runs on one GPU and a prayer. We harden it into something that survives real traffic, real uptime expectations, and real burn-rate scrutiny from your next round, without over-building for scale you don't have yet.

Hardening notebooks and scripts into deployable services
Load and cost modelling against your growth curve
Secrets, access, and security before enterprise buyers ask
Burn-rate discipline: infra spend mapped to runway months

How We Work

Senior hands, then a clean hand-off.

Most AI startups don't need a permanent platform team. They need senior operators for a focused window, and a system their own engineers can run once we leave. Every engagement is shaped around that.

1. Assess — about two weeks

We instrument your GPU utilisation, trace where the spend actually goes, and pressure-test your inference path against the growth you're forecasting. You get a prioritised findings list with real currency attached — cost-per-inference, wasted GPU-hours, projected spend at 10x traffic — not a generic best-practices deck.

2. Build — four to eight weeks

We implement the highest-leverage fixes: right-sizing and spot migration, a proper serving stack, cost-attribution dashboards, and the CI/CD and observability to hold it together. We work in your repositories and your cloud accounts in the open, alongside our broader cloud and FinOps practice, so nothing we build is a black box.

3. Hand off — and stay reachable

Your engineers get the runbooks, dashboards, and on-call playbooks, plus a short retained-advisory window for the next scaling wall. For teams that want ongoing coverage, we offer fractional DevOps and SRE support, so a two-person infra function behaves like a ten-person one. The numbers from past engagements live in our case studies.

Why InfraZen

Infra people who think in runway months.

We're a DevOps, SRE, and cloud consultancy, not an ML research lab and not a staffing agency. That matters for AI startups because your bottleneck usually isn't the model, it's everything around it: the pipeline, the serving layer, the bill, and the pager.

We weigh every recommendation against burn. A 40% GPU saving that buys three more months of runway often matters more than a marginally faster p99, and we'll say so. We'll also tell you when the expensive option is the right one, and when you simply don't need the reliability, or the Kubernetes, that a bigger company would.

We work across AWS, GCP, Azure, and the newer GPU clouds, on both regulated systems and move-fast startups, so we know which corners are safe to cut and which will fail an audit two years from now. If the honest answer is "delete this service" or "you're not ready for a feature store yet," that's the answer you'll get. If you're weighing DevOps and SRE more broadly, start with our DevOps and SRE service pages.

Paying too much to serve too few requests?

Book a free 30-minute review. We'll look at your GPU utilisation, your inference path, and your burn rate, then tell you honestly where the money is leaking and what we'd fix first.

Book a Call →

From the blog: Kubernetes GPU Cost for LLM Inference · FinOps for AI Runtime Cost

Frequently asked questions

Why is GPU cost the biggest line item for AI startups?

For most AI startups, GPU compute is 60-80% of the cloud bill, and it's the line item that scales fastest with usage. On-demand H100s run roughly $3-5 per GPU-hour, and an idle GPU bills at the same rate as a busy one. The waste is rarely the model, it's utilisation: GPUs sitting at 15% while you pay for 100%. Fixing scheduling, batching, and right-sizing usually cuts the bill 30-50% before you touch the model itself.

Should we run GPUs on spot, reserved, or on-demand?

It depends on the workload. Training and batch jobs that can checkpoint and resume belong on spot capacity, where you save 60-90% and simply tolerate interruptions. Steady-state production inference belongs on reserved instances or committed-use discounts, because the traffic is predictable and your API can't vanish mid-request. On-demand is for spikes and experiments only. Most startups get this backwards: on-demand for everything, then a surprise invoice at month-end.

How do you scale LLM inference without over-provisioning GPUs?

Continuous batching plus autoscaling driven by real queue depth, not CPU. Serving frameworks like vLLM and TGI batch requests dynamically, so one GPU serves many concurrent users instead of one at a time. You then autoscale replicas on tokens-in-flight or queue latency, and scale idle models to zero. The hard part is cold-start: a large model can take minutes to load, so you keep a minimal floor of warm replicas or pre-load weights to cut that delay.

What does MLOps actually mean for an early-stage AI team?

For a seed or Series A team, MLOps is not a twenty-tool platform. It's three things: reproducible training runs (versioned data, code, and config so any model can be rebuilt), an evaluation gate before anything ships (so a new model can't silently regress quality), and a deploy path identical to the rest of your software. Feature stores, model registries, and drift monitoring come later, when the pain justifies them, not before.

How do you keep AI infrastructure reliable without a big SRE team?

You buy leverage instead of headcount. Define two or three user-facing SLOs (inference latency and availability), put burn-rate alerts on them so the pager only fires on things that matter, and automate the top failure modes: GPU node loss, out-of-memory on long context, and provider rate-limits. AI systems fail differently from web apps, so the runbooks cover model rollback and fallback to a smaller model, not just restarting the pod.

Ship AI to production. Without torching the runway.