Cluster deployment
Architecture, bring-up, and lifecycle management for bare metal, Kubernetes, Slurm, GPU clusters, distributed storage, networking, identity, and secrets.
Staircase / Managed Infrastructure
From metal
to model.
Deploy, operate, and evolve the systems that carry production workloads. Staircase brings MLOps, DevOps, cluster engineering, and hands-on infrastructure operations together across on-prem, cloud, and hybrid environments.
Training orchestrationInference servingManaged DevOpsKubernetesSlurmBare metalCloud & hybrid
Full-stack operations
One operator.
Every layer.
MLOps is strongest when the platform beneath it is treated as part of the system. We work from power, firmware, networks, and storage through schedulers, runtimes, delivery pipelines, and production models.
Managed DevOps
Infrastructure as code, GitOps, CI/CD, observability, incident response, upgrades, capacity planning, and reliability engineering for general-purpose or specialized workloads.
ML platform operations
Training queues, experiment environments, distributed jobs, model registries, inference endpoints, GPU scheduling, rollout safety, and end-to-end telemetry.
Hardware & datacenter
Server selection, rack design, firmware, network and storage topology, burn-in, provisioning, troubleshooting, and operational handoff—from a closet cluster to a full datacenter room.
Cloud & hybrid capacity
Cloud sourcing and resale, landing-zone design, managed services, and workload placement across owned hardware and rented capacity without forcing the entire platform into one provider.
Machine learning operations
Keep training moving.
Put inference to work.
Staircase turns accelerators and clusters into an operating ML platform. Researchers get reproducible jobs. Product teams get reliable endpoints. Operators retain control of cost, capacity, and change.
Training orchestration
Queue and place single-node or distributed training jobs by accelerator, topology, capacity, priority, and policy across Kubernetes and Slurm environments.
Resilient execution
Standardize environments, datasets, checkpoints, retries, and artifact handling so long-running jobs can survive interruption and remain reproducible.
Experiment telemetry
Connect workload, GPU, network, storage, cost, and model metrics so teams can diagnose both ML behavior and the infrastructure beneath it.
Inference serving
Package and release models through controlled environments with GPU-aware placement, routing, autoscaling, health checks, and gradual rollout strategies.
Production lifecycle
Manage registries, versions, dependencies, rollback, drift signals, performance, and availability from initial deployment through retirement.
Platform guardrails
Encode tenancy, access, secrets, quotas, approvals, data boundaries, and audit evidence into the path from research to production.
On-premises infrastructure
On-prem is not
an edge case.
Owned infrastructure can deliver control, predictable economics, locality, and privacy—but only when hardware and software are designed as one operating system. That is where Staircase is strongest.
Closet to lab
Compact compute, storage, networking, remote access, power-aware design, and automation for a single rack or a small distributed footprint.
Shared production systems
Multi-rack Kubernetes, Slurm, GPU, storage, and service platforms with tenancy, scheduling, observability, and an operating model your team can sustain.
Rooms at scale
Capacity planning, topology, provisioning, fleet automation, failure domains, lifecycle controls, and integration with facilities and remote hands.
Deployment model
Own it. Rent it.
Combine it.
The right infrastructure boundary depends on economics, data, latency, utilization, and control. Staircase designs around the workload rather than a preferred vendor.
On-premises
Deploy and manage hardware where you need complete control, locality, steady-state economics, or an isolated operating boundary.
Cloud
Source and resell capacity, operate cloud-native services, and use elastic infrastructure for variable demand, rapid expansion, or regional reach.
Hybrid
Unify delivery, identity, observability, and workload policy across owned and rented infrastructure while placing each job where it belongs.
Why Staircase
Hardware depth.
Platform discipline.
Staircase is led by an operator who has supported physical cloud fleets, maintained distributed storage, built managed Kubernetes and load-balancing products, administered HPC systems, and secured multi-tenant AI infrastructure.
Below Kubernetes
We understand the firmware, hardware, network, storage, and failure modes hidden beneath the control plane.
Above the cluster
We design the delivery, observability, access, and operating workflows that make infrastructure useful to its consumers.
ML & HPC fluency
GPU workloads, distributed training, schedulers, model serving, and high-performance systems are first-class concerns.
Security built in
Dark Forest’s offensive and defensive expertise informs the architecture without turning operations into a compliance exercise.
Contact / Staircase
Give the workload
somewhere to go.
Bring us the model, the cluster, the empty room, or the infrastructure problem. We’ll map the path from deployment through managed operations.
sales@darkforest.agency