Clustra is the product layer between customer-owned infrastructure and the applications that need to use private models. It standardizes how models are launched, accessed, traced, and operated.
Each layer is useful on its own. Together, they create the operating surface enterprises need to make private AI usable by application teams without losing control.
A guided operating surface for launching and managing private model workloads inside customer-controlled environments.
One governed endpoint for applications, agents, and internal tools that need to reach approved private models.
Request-level visibility into how models and agent workflows behave in production without sending activity outside the customer boundary.
Infrastructure and runtime monitoring tuned for private AI operations, capacity planning, and incident response.
The pilot conversation should make deployment assumptions explicit: where Clustra runs, what the customer owns, what Clustra handles, and which prerequisites need review.
Fast pilot with enterprise governance
Customer owns
Account, network, identity, logs, approvals
Clustra handles
Platform rollout, gateway, model runtime, evidence
Prerequisites
Approved cloud account, private network path, identity owner
Data residency and internal platform standards
Customer owns
Private regions, storage, security tooling, retention
Clustra handles
Deployment workflow, routing, observe/monitor setup
Prerequisites
Compute capacity, image/artifact path, logging target
Low-egress or owned-infrastructure AI workloads
Customer owns
Datacenter, hardware, network, identity, operations
Clustra handles
Platform packaging, launch path, readiness reporting
Prerequisites
Runtime nodes, storage, ingress path, admin access model
Sensitive workloads with constrained outbound access
Customer owns
Connectivity rules, change windows, evidence retention
Clustra handles
Private gateway pattern and operating controls
Prerequisites
Approved package path, local registry/cache, review owners
Disconnected environments where external dependency is not acceptable
Customer owns
Offline environment, transfer process, local evidence store
Clustra handles
Offline deployment package and validation checklist
Prerequisites
Disconnected install path, model artifact transfer approval
| Mode | Customer owns | Clustra handles | Prerequisites | Best fit |
|---|---|---|---|---|
| Public cloud | Account, network, identity, logs, approvals | Platform rollout, gateway, model runtime, evidence | Approved cloud account, private network path, identity owner | Fast pilot with enterprise governance |
| Private cloud | Private regions, storage, security tooling, retention | Deployment workflow, routing, observe/monitor setup | Compute capacity, image/artifact path, logging target | Data residency and internal platform standards |
| On-premise | Datacenter, hardware, network, identity, operations | Platform packaging, launch path, readiness reporting | Runtime nodes, storage, ingress path, admin access model | Low-egress or owned-infrastructure AI workloads |
| Restricted network | Connectivity rules, change windows, evidence retention | Private gateway pattern and operating controls | Approved package path, local registry/cache, review owners | Sensitive workloads with constrained outbound access |
| Air-gapped | Offline environment, transfer process, local evidence store | Offline deployment package and validation checklist | Disconnected install path, model artifact transfer approval | Disconnected environments where external dependency is not acceptable |
Start with the planes and review boundaries. Open the full deployment-reference diagram only when architecture reviewers need implementation-level detail.
Deployment intent, approved model names, access policy, and platform change history.
Private model serving, model cache, capacity scheduling, and health signals inside the customer environment.
Application and agent requests enter through one governed gateway before reaching private model frontends.
Request traces, usage attribution, audit history, and readiness signals for security and platform review.
Detailed infrastructure, runtime, gateway, and agent layers. Best reviewed on desktop or with architecture stakeholders.
Read bottom to top
Consumer side
LangChain, LlamaIndex, AutoGen, CrewAI
Prefect, Airflow, Temporal
Retrieval pipelines
Coordination
OpenAI-compatible
Public endpoints
Load balancer
REST + streaming
Single public trust boundary
OpenAI-compatible
Private frontends
Private frontend map
Platform services
Live sync
Jobs + logs
Traces + metrics
Private serving
Private, per model
ClusterIP only
Lifecycle + scaling
Mounted per worker
Deploy scheduling and policy
Manifest repos
Pre-pull jobs
Cloud, on-prem, bare metal
AWS · GCP · Azure · On-prem · Bare metal
DB / cache / object storage
Foundation
NVIDIA, AMD
Clustra keeps model frontends private and gives application teams one governed access path. The result is a cleaner operating model for teams, agents, and sensitive workloads.
A platform team selects a model, validates requirements, prepares configuration, and launches it through Clustra Deploy.
The deployment exposes a clear model name for application teams while raw deployment details stay behind the platform boundary.
Applications and agents connect through Clustra Gateway, where auth, routing, rate limits, and usage controls are enforced.
Clustra Observe captures request behavior while Clustra Monitor tracks the infrastructure and runtime health underneath.
Private AI succeeds when the operating model is clear after the first deployment: who owns access, what models are live, what traffic is flowing, where capacity is constrained, and how changes are reviewed.
The exact schema is mapped during deployment, but enterprise reviewers should see the evidence model early: who used which model, what policy applied, how it performed, and where the record is retained.
We will map the platform to your infrastructure boundaries, first workloads, access model, and production-readiness goals.