Catalog
concept#Machine Learning#Platform#DevOps#Security

Self-Hosted Models

Deploying and operating ML/AI models on private infrastructure instead of managed cloud services, focusing on control, data sovereignty, latency and compliance.

Self-hosted models refers to deploying and operating AI/ML models on private infrastructure rather than managed cloud services.
Emerging
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

CI/CD systems (e.g. GitLab CI, Jenkins)Monitoring stack (e.g. Prometheus, Grafana)Secret management and IAM

Principles & goals

Data sovereignty: keep raw data and models under controlled management.Isolation: clearly segment networks and access rights.Automation: automate deployments, tests and rollbacks.
Run
Domain, Team

Use cases & scenarios

Compromises

  • Insufficient patches or outdated components lead to security vulnerabilities.
  • Lack of automation increases error-proneness during rollouts.
  • Insufficient operational resources can cause downtime.
  • Version and sign model artifacts.
  • Use automated tests and canary rollouts.
  • Continuously monitor and adjust resource metrics.

I/O & resources

  • Trained and versioned model artifacts
  • Access and authorization requirements
  • Test and validation datasets
  • Deployed model endpoints
  • Monitoring and audit logs
  • Versioned deployments with rollback capability

Description

Self-hosted models refers to deploying and operating AI/ML models on private infrastructure rather than managed cloud services. It emphasizes data sovereignty, low-latency inference, compliance and full control over models, resources and integrations. Operations, monitoring and model updates must be supported by organizational capabilities.

✔

  • Full control over models, updates and access control.
  • Improved privacy and compliance capabilities.
  • Lower latency via local inference and optimized networks.

✖

  • High operational effort for infrastructure and monitoring.
  • Scaling can be more costly and complex than cloud solutions.
  • Responsibility for security and compliance rests entirely with the operator.

  • Latency per request

    Mean and p95 latency of inference requests measured under production load.

  • Availability

    Percentage system availability of the model-serving stack within a time period.

  • Prediction error rate

    Share of incorrect or deviating predictions compared to validation data.

In-house banking inference platform

Bank operates fraud-detection models fully on-premise due to regulatory requirements.

Healthcare data analysis within hospital network

Hospital runs image classification models locally to protect patient data.

Edge inference for manufacturing plants

Manufacturing uses locally deployed models for real-time anomaly detection without cloud latency.

1

Define requirements and compliance criteria.

2

Provision and segment infrastructure (network, hardware).

3

Build CI/CD pipeline for model tests and deployments.

4

Introduce monitoring, logging and alerting.

5

Test rollback and incident response plans.

⚠️ Technical debt & bottlenecks

  • Non-standardized model formats hinder portability.
  • Manual operational processes cause inconsistent deployments.
  • Outdated libraries and images increase security risks.
Hardware resources (GPU/TPU)Operational and support capacityNetwork and storage performance
  • Running models with sensitive raw data without data minimization.
  • Implementing scaling manually and reactively instead of automated.
  • Delaying security updates for cost reasons.
  • Underestimating operational effort for hardware and software.
  • Lack of traceability for model changes.
  • Assumptions about scalability without load testing.
Operating distributed systems and orchestrationKnowledge in model serving and inference optimizationSecurity and compliance expertise
Data sovereignty and regulatory requirementsLatency requirements and real-time inferenceOperational availability and maintainability
  • • Available compute capacity and procurement cycles
  • • Organizational responsibilities for security
  • • Budget for infrastructure and maintenance