Choosing an MLOps platform for on-premise use without cloud dependency means prioritizing tools that run fully self-hosted with no mandatory external API calls, which rules out most fully managed SaaS platforms and points toward an open-source stack assembled on owned infrastructure. A practical on-premise stack typically combines MLflow for experiment tracking and model registry, Kubeflow or a lighter orchestrator for pipelines, Kubernetes with the NVIDIA GPU Operator for GPU scheduling across H100 or H200 nodes, and self-hosted Langfuse for observability, all running inside a private network or a fully air-gapped environment when required. Air-gapped deployments need particular attention to container image management, since pulling images from public registries at deploy time is not possible, requiring a private registry mirrored in advance with every dependency version pinned. Storage and compute sizing should account for on-premise infrastructure not scaling elastically the way cloud does, so capacity planning for peak load needs to happen up front rather than relying on autoscaling. Licensing terms for each component should be checked carefully, since some tools have shifted from permissive to more restrictive licenses for enterprise features in recent years. Nanobase AI, an NVIDIA Inception Program member, designs and deploys fully on-premise MLOps stacks for regulated clients that cannot send data or models to any external cloud.
On-premise and air-gapped are not the same requirement
Teams often use these terms interchangeably, but they demand different levels of preparation. On-premise typically means infrastructure runs on owned hardware inside a private network, which can still reach external services when needed. Air-gapped means no external network connectivity at all, ever, which changes how every single component gets installed, updated and licensed. Confirming which of these two the actual requirement is, before selecting tools, avoids discovering mid-deployment that a chosen platform silently expects to reach an external license server or package registry that an air-gapped network cannot contact.
Component-by-component on-prem readiness
| Layer | Component | On-prem readiness |
|---|---|---|
| Experiment tracking / registry | MLflow | Fully self-hostable, no external dependency |
| Orchestration | Kubeflow, or a lighter alternative | Self-hostable on Kubernetes; verify plugin dependencies individually |
| GPU scheduling | Kubernetes + NVIDIA GPU Operator, or Slurm | Fully self-hostable, designed for owned infrastructure |
| Observability | Self-hosted Langfuse, Prometheus, Grafana | Fully self-hostable |
| Serving | KServe, Seldon, BentoML, vLLM/TensorRT-LLM | Fully self-hostable |
Most of the modern open-source MLOps stack was designed with self-hosting in mind, so on-premise readiness is rarely the blocker; the harder work is in the operational logistics of running it without any external network dependency at all, the same logistics covered in a broader on-premise LLM deployment guide.
The air-gap logistics that catch teams off guard
- Mirror a private container registry in advance, since pulling images from public registries at deploy time is not possible; every dependency version needs to be pinned and staged before the air-gapped environment goes live.
- Stage model weights and datasets physically or through an approved one-way transfer process, since downloading a model checkpoint from a public hub is unavailable inside the boundary.
- Resolve license server dependencies for any commercial component, since a tool that phones home for license validation will fail silently or refuse to start inside a fully isolated network.
- Plan for certificate and time synchronization inside the boundary, since internal services relying on TLS certificates or accurate clocks need their own internal authority rather than the public infrastructure they would normally use.
Capacity planning without elastic scaling
On-premise infrastructure does not scale elastically the way cloud does, so capacity planning for peak training or inference load needs to happen well before deployment, sized against actual GPU node counts, H100 or H200 class hardware, rather than assuming additional capacity can be provisioned on demand during a traffic spike. Under-provisioning shows up as queued training jobs or degraded inference latency during peak periods with no quick remediation available, unlike a cloud environment where autoscaling can absorb the same spike.
Frequently asked questions
Can a fully open-source stack really replace a managed cloud MLOps platform on-premise?
Yes, for most core capabilities, experiment tracking, orchestration, serving, observability, open-source tools cover the same functionality; what the client takes on in exchange is the operational responsibility a managed cloud platform would otherwise carry, the same build versus buy trade-off that applies to any MLOps platform decision.
Do licensing terms matter more for on-premise deployments?
Yes, since some tools have shifted from permissive to more restrictive licenses for enterprise features, and an on-premise deployment often needs those enterprise features, like advanced governance or multi-tenancy, more than a cloud deployment relying on a managed vendor's equivalent feature.
How much lead time does an air-gapped deployment need compared to a standard on-premise one?
Meaningfully more, since every dependency needs to be identified, mirrored and staged before deployment begins rather than resolved on demand; teams that treat air-gapped timelines the same as standard on-premise timelines consistently underestimate the logistics work involved.
How Nanobase AI helps
Nanobase AI designs and deploys fully on-premise and air-gapped MLOps stacks for regulated clients that cannot send data or models to any external cloud, including the Kubernetes GPU Operator or Slurm capacity planning that keeps the environment running without elastic scaling.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.