Managed AI infrastructure for heterogeneous GPU fleets.
Durantic operates Kubernetes, Slurm, inference serving, provisioning, networking, and monitoring across bare metal, hybrid, and fragmented GPU environments.
- → Mixed GPUs, multiple providers, colocated clusters
- → Custom network fabrics and bare metal lifecycle
- → No need to build an infrastructure team
$ cat status.log
INFO Fleet telemetry online
INFO Mesh health: nominal
INFO Provisioning queue idle
AI companies are being forced to become infrastructure operators.
As AI workloads move across hyperscalers, GPU clouds, colocated bare metal, and private clusters, infrastructure is becoming fragmented. Teams are forced to stitch together Kubernetes, Slurm, provisioning, networking, monitoring, inference serving, and vendor-specific GPU tooling.
The result is operational drag: underutilized GPUs, fragile automation, slow provisioning, manual debugging, inconsistent networking, and infrastructure teams spending time on undifferentiated complexity.
- ×Mixed GPU generations across different environments
- ×Kubernetes and Slurm running side by side
- ×Custom network fabrics and non-standard topology
- ×Bare metal provisioning and lifecycle management
- ×Fragmented monitoring and hardware visibility
- ×Inference serving reliability and scaling
- ×New GPU racks arriving without proven acceptance testing
- ×Manual operations that do not compound
One operational layer for your AI infrastructure.
Managed Kubernetes
Production Kubernetes operations for GPU workloads across bare metal and hybrid environments.
Managed Slurm
Slurm deployment, operations, scheduling support, and cluster reliability for training and research workloads.
Inference Serving
Infrastructure for reliable inference deployment, routing, scaling, and operational monitoring.
Bare Metal Provisioning
Automated provisioning and lifecycle management for machines, GPUs, networking, and operating system images.
Network Fabric Operations
Custom network fabrics, routing, overlays, topology awareness, and low-level infrastructure connectivity.
GPU Fleet Monitoring
Hardware telemetry, GPU health, failure patterns, workload behavior, and fleet-wide operational visibility.
Hardware Acceptance & Burn-In
Multi-day soak testing, NCCL bandwidth validation, ECC and link-flap detection, thermal verification, and vendor-grade acceptance reports for new GPU racks and RMA evidence.
Built for the infrastructure reality AI teams face.
Most infrastructure tools assume homogeneous cloud environments. Durantic was designed for the opposite: heterogeneous GPU fleets, fragmented capacity, custom networks, and bare metal operations.
Heterogeneous by design
Operate mixed GPU generations, multiple providers, colocated clusters, and custom hardware environments through one operational model.
Bare metal-native
Durantic was built for physical machines, low-level provisioning, and infrastructure lifecycle control rather than being bolted onto cloud assumptions.
Kubernetes + Slurm + inference
Support the operational reality of AI teams that need multiple workload layers, not a single orchestrator.
Infrastructure-aware networking
Designed for custom network fabrics, routing, topology awareness, and high-performance GPU cluster environments.
Operational intelligence
Every fleet operated by Durantic improves the platform’s understanding of hardware behavior, failure modes, topology, and workload patterns.
Operations make the software stronger. The software makes operations scale.
An agent on every machine Durantic operates feeds hardware, network, and workload telemetry back to the control plane. Each fleet sharpens the platform's model of how AI infrastructure fails, recovers, scales, and performs.
Built for AI infrastructure that does not fit neatly into one cloud.
AI startups with fragmented GPU access
Operate capacity across multiple providers, colocated racks, and mixed GPU generations without building a full internal infrastructure team.
Inference providers
Run reliable inference infrastructure across heterogeneous machines, custom networking, and changing workload patterns.
Research and training clusters
Manage Kubernetes, Slurm, provisioning, and GPU fleet reliability for teams running training or research workloads on bare metal.
Sovereign and private AI deployments
Operate private or regional AI infrastructure where hyperscaler assumptions do not apply.
GPU cloud and compute operators
Add operational automation, monitoring, and lifecycle control across heterogeneous GPU capacity.
Managed infrastructure without owning the hardware.
Durantic can operate customer-owned, colocated, leased, partner-provided, or hybrid GPU infrastructure. We do not require customers to move into a single cloud or standardize on one hardware provider.
You bring or access the compute. Durantic operates the infrastructure layer.
- ●Monthly platform and operations fee
- ●Pricing scales with GPU and server count, and operational complexity
- ●Optional higher-touch support and SLA tiers
- ●Designed for long-term managed infrastructure partnerships
Assess fleet
We map your existing GPU infrastructure — providers, generations, network fabric, current ops pain points — in a working session.
30-day pilot
Durantic takes operational ownership of a subset of your fleet. You see the platform run before you commit.
Managed operations
Full managed-services relationship. We run Kubernetes, Slurm, inference, provisioning, and monitoring across the agreed fleet.
How this scales
Most infrastructure companies are either software vendors or services companies. Software vendors sell platforms and leave operations to the customer. Services companies run infrastructure with people, and scale linearly with headcount.
Durantic is both. We write the software that operates AI infrastructure, and we operate it for our customers. The operating loop is what makes the software better than anyone else’s — every fleet we run sharpens the platform that runs the next one. Over time, the operational work per server decreases.
This is how hyperscalers ran global fleets with small operator-to-server ratios. The same dynamic, applied to fragmented infrastructure outside the hyperscaler walled gardens.
Self-driving infrastructure for AI fleets — outside hyperscaler walls.
Managed operations today, with an agent-native control plane underneath. As the substrate matures, more of the work runs autonomously and the same Durantic team operates larger fleets.
Running messy GPU infrastructure?
Tell us about your fleet. We'll operate it.