Where your models meet the metal.
Headquartered next to Data Center Alley, we design, build, and operate the MLOps pipelines, cloud infrastructure, and GPU clusters behind machine learning — sourcing capacity from neo-cloud and hyperscale providers to match your workload.
Multi-cloud & neo-cloud
AWS, GCP, Azure, and GPU-native providers, matched to the workload instead of a single vendor's catalog.
Day-2 operations
On-call response, cost and utilization tracking, and capacity planning that keeps running long after launch.
On-prem to hyperscale
Colocated racks, private clusters, and public cloud, run as one operating model rather than separate silos.
Three layers, one team.
Most teams end up stitching these together from separate vendors, contractors, and whoever on staff last read the docs. We run all three as one connected practice, so decisions in one layer account for the other two.
MLOps
Pipeline design, model serving, experiment tracking, CI/CD for ML, and drift monitoring — the operational layer that keeps a model working after the demo.
Cloud Infrastructure Solutions
Architecture, migration, and ongoing management across AWS, GCP, Azure, and specialized providers, sized to the workload rather than a default instance catalog.
HPC Infrastructure Management
Cluster provisioning, job scheduling, interconnect tuning, and capacity planning for large-scale training and inference — including sourcing capacity from neo-cloud GPU providers alongside traditional infrastructure.
The neo-cloud layer, explained
A newer category of providers — GPU-native clouds built specifically for AI workloads — now compete directly with the traditional hyperscalers for training and inference capacity. Neither is a default answer; the right mix depends on the workload.
Hyperscalers (AWS, GCP, Azure)
- Broad managed services beyond compute — databases, networking, identity, compliance tooling
- Mature support organizations and SLAs
- GPU capacity often reserved, queued, or priced at a premium
- Easier to justify to security and procurement teams already using them
Neo-clouds (GPU-native providers)
- Faster access to current-generation GPUs, often at lower cost per hour
- Built around training and inference, not general-purpose workloads
- Thinner managed-service layer — more falls to whoever operates the cluster
- Newer companies: fewer years of track record, variable support depth
// capacity, wherever it lives
On most engagements, the client holds the commercial relationship directly with the provider — the contract, the invoice, the SLA — while we operate the infrastructure on top of it. That keeps vendor and pricing risk where it belongs, and puts us where the ongoing work actually lives.
That includes day-2 operations — monitoring, cost and utilization tracking, on-call response when nodes fail, and capacity forecasting so a shift in a provider's available inventory doesn't catch you off guard — and, where it matters most, a multi-provider abstraction layer: Kubernetes paired with something like SkyPilot, or our own Terraform modules, so your workloads aren't hard-wired to one neo-cloud's specific API. Providers expose their platforms very differently, and that layer is what keeps switching one a configuration change instead of a rewrite.
How an engagement runs
The same four stages, whether we're standing up a first training cluster or taking over infrastructure that already exists.
Assess
We audit current infrastructure, workload profiles, and spend. You get a clear picture of where time and budget are going before we propose anything.
Architect
We design the target state across cloud, neo-cloud, and HPC — sized to your actual training and inference patterns, not a generic reference architecture.
Deploy
We build the pipelines, provision the clusters, and run the migration, in coordination with your existing team wherever one exists.
Operate
We stay on as the operating layer — monitoring, on-call, cost tuning, and capacity planning — for as long as you need us to.
Built on tools your team already knows
We work in the open-source and standard commercial tooling most infrastructure and ML teams already touch, rather than a proprietary platform you have to relearn.
orchestration & scheduling
ml platform & tracking
infrastructure as code
observability
training & inference
Questions we get early
If something's missing here, it belongs in a conversation instead — reach out and ask directly.
What's the difference between MLOps and regular DevOps?+
DevOps practices generally assume your artifact is code that either works or doesn't. ML artifacts are models that degrade quietly — data drifts, performance decays, and retraining pipelines need their own CI/CD. MLOps borrows DevOps discipline and applies it to that different failure mode.
What's a neo-cloud, and why would I use one instead of AWS, Azure, or GCP?+
Neo-clouds are providers built specifically around GPU compute for AI, rather than general-purpose infrastructure. They can offer faster access to current-generation GPUs and different pricing, at the cost of a smaller platform and less mature managed tooling. The right call usually depends on the specific workload, not a blanket preference.
Do we need a long-term contract?+
No. Assessments are scoped and priced upfront as fixed engagements. Ongoing operations run on a rolling agreement with a defined notice period, not a multi-year lock-in.
Can you work alongside our existing infrastructure or DevOps team?+
Yes — that's the more common setup. We typically take on specialized ML infrastructure and capacity work, while your team keeps ownership of day-to-day application development. We'll scope the split in writing before starting.
Do you handle on-prem or colocated hardware, or only cloud?+
Both. HPC infrastructure work often involves a mix of on-prem clusters, colocation, and cloud or neo-cloud capacity — we manage the mix, not just one piece of it.
How is an engagement priced?+
Assessments are fixed fee, scoped to the size of your environment. Ongoing infrastructure management runs as a retainer sized to the workload. You'll get a number before any work starts, not after.
Tell us what you're running.
Thirty minutes is usually enough to tell whether we're a fit. No pitch deck — just your environment and what's not working about it yet.