Building an On-Premise AI Stack for Manufacturing

As manufacturers embrace Industry 4.0 and smart factories, AI-driven analytics and visualization become critical. However, sensitive production data often must stay on-site for security and latency reasons. An on-premises AI stack uses open-source tools instead of cloud services, giving full data control, security, and low-latency inference. For example, on-prem AI lets a factory meet strict compliance (e.g. ISO27001, GDPR) and handle real-time defect detection right on the line. The trade-offs include higher upfront cost and in-house IT expertise, versus the cloud’s low entry price and easy scaling. In practice many organizations use a hybrid approach: keep sensitive data and real-time tasks on-prem, while using the cloud for heavy batch training or bursting. In fact, industry surveys show 82% of companies develop custom AI and 58% run AI on Kubernetes, with 90% citing open-source as critical to their AI strategy.

A fully on-prem AI DevOps stack is a self-hosted set of tools for data ingestion, model training, deployment, monitoring, and visualization that runs entirely inside a company’s private infrastructure—without relying on public cloud services. For GDPR-regulated organizations, this ensures data residency, auditability, and full control over personal or sensitive data.

What are the hardware requirements for running AI and DevOps tools fully on-prem?

• Minimum (PoC / Pilot Setup)

  • CPU: 2× 16-core x86 servers
  • RAM: 128–256 GB
  • Storage: 10–20 TB NVMe / SSD (object + database)
  • GPU: Optional (NVIDIA T4 / RTX A4000)
  • Use case: Dashboards, ETL, light ML

• Production (Single Factory / Mid-Scale)

  • CPU: 3–5 Kubernetes nodes (32–64 cores each)
  • RAM: 512 GB – 1 TB total
  • Storage: 50–100 TB (MinIO + analytics DB)
  • GPU: 1–4× NVIDIA A10 / L4 / A100
  • Use case: Predictive maintenance, vision, real-time analytics

• Enterprise / Multi-Plant

  • CPU: 10+ nodes
  • RAM: 2–5 TB
  • Storage: 200 TB+
  • GPU: Dedicated inference + training pool
  • Use case: Fleet-wide AI, simulation, heavy training

How does the AI/ML lifecycle look in an on-prem manufacturing setup?

Below we outline each stage of the AI/ML lifecycle—data collection, preprocessing, training, deployment, and monitoring/visualization—and compare common cloud services with open-source on-premise alternatives. We focus on a data-visualization use case (e.g. dashboards of sensor data, predictive-maintenance analytics), but the pattern applies broadly. First, here’s a component-by-component comparison:

Stage / Component	Cloud Services	Open-Source On-Prem Alternatives
Data Ingestion & Streaming	AWS IoT Core / Kinesis; Azure IoT Hub/Event Hubs; GCP Pub/Sub	Apache Kafka, RabbitMQ, Apache Pulsar; MQTT brokers (Mosquitto, HiveMQ); IoT platforms (ThingsBoard)
Object Storage	AWS S3; Azure Blob Storage; GCP Cloud Storage	MinIO, Ceph (S3-compatible on-prem object storage)
Data Lake / Warehouse	BigQuery; AWS Redshift; Azure Synapse	ClickHouse, Apache Druid; Greenplum, MariaDB ColumnStore; Postgres + extensions
Data Processing / ETL	AWS Glue; Azure Data Factory; GCP Dataflow	Apache Airflow (or NiFi); Apache Beam/Flink; Dask; Spark on-prem
Feature Store / Catalog	SageMaker Feature Store; AWS Glue Data Catalog	Feast (open-source feature store); Amundsen/DataHub (catalogs)
Experiment Tracking	SageMaker Experiments; Azure ML Studio Tracking	MLflow (tracking and registry); Weights & Biases (open core)
Notebooks / Development	AWS SageMaker Studio; Azure Notebooks	JupyterHub/JupyterLab; Visual Studio Code (local)
Model Training	SageMaker Training; Azure ML; GCP AI Platform	Raw compute on local GPUs/CPUs (with PyTorch, TensorFlow, etc.); Kubeflow Pipelines or Argo Workflows for orchestration
Orchestration / Pipelines	AWS Step Functions; Azure Data Factory	Kubeflow Pipelines; Argo Workflows; Apache Airflow (multi-cloud or on-prem)
Model Serving / Deployment	SageMaker Endpoints; Azure ML/AKS; GCP Cloud Run	Kubernetes (EKS alternative on-prem); Seldon Core or KFServing (KServe); BentoML, TorchServe
Monitoring (Metrics)	AWS CloudWatch; Azure Monitor; GCP Monitoring	Prometheus (metrics) + Grafana dashboards; OpenSearch or Grafana Loki (logs)
Logging	CloudWatch Logs; Azure Log Analytics; GCP Logging	ELK/OpenSearch stack (Elasticsearch+Kibana) or Loki/Tempo/Promtail
Visualization / BI	AWS QuickSight; Power BI; Looker	Apache Superset; Metabase; Redash; Grafana (for real-time metrics)
CI/CD / GitOps	AWS CodePipeline/CodeCommit; Azure DevOps	Jenkins, GitLab CI/CD, Argo CD, Tekton on-prem

All ingestion components can be deployed air-gapped, with no outbound internet access, meeting GDPR and ISO27001 requirements.

Below we explain how these pieces fit together. The architecture is conceptually similar to a cloud stack – but we self-host each function.

How do manufacturers collect and ingest machine data in an on-prem AI architecture?

Manufacturing data often comes from sensors and machines. In the cloud this might use AWS IoT Core or Azure IoT Hub and stream services like AWS Kinesis or Azure Event Hubs. On-premises, you can instead run MQTT brokers (e.g. Mosquitto, HiveMQ) or an IoT platform like ThingsBoard to gather device data. For streaming data pipelines (e.g. telemetry, logs, events), an on-prem Apache Kafka cluster is very common. Kafka is an open-source, distributed streaming platform with high throughput and low latency, ideal for real-time ingestion. Kafka topics can collect sensor streams and feed downstream systems. (Alternative streams include RabbitMQ or Apache Pulsar.) Data from Kafka can be processed by on-prem tools (see next section).

How is manufacturing data stored and prepared for AI models in an on-prem environment?

Once data is collected, we store and prepare it for modeling. In cloud architectures one might store raw data in S3 (object store) and load it into a data warehouse like BigQuery or Redshift for analytics. On-premise, we replace S3 with MinIO or Ceph – high-performance, scalable object stores with S3-compatible APIs. For example, MinIO is a lightweight, S3-compatible storage you can deploy in your own data center. MinIO supports multi-TB datasets, encryption, and Kubernetes deployment, making it a drop-in on-prem alternative to S3. Likewise, an on-prem SQL warehouse replaces BigQuery. Open-source analytics DBs like ClickHouse, Apache Druid, or traditional systems (PostgreSQL, Greenplum, MariaDB ColumnStore) serve as a cost-effective data warehouse. For instance, ClickHouse is a columnar database optimized for fast analytical queries. The BigQuery alternatives FAQ explicitly notes that “open-source tools like ClickHouse, PostgreSQL, Greenplum… are free and reliable for analytics”. You can load cleaned data into these stores and run SQL/OLAP queries for BI dashboards.

Before training, data often needs cleaning and transformation. In AWS you might use Glue or Azure Data Factory; on-premise you can script ETL with Python/Pandas or use workflow tools like Apache Airflow or NiFi. Airflow (either on Kubernetes or on a VM) lets you schedule batch jobs: e.g. pull raw CSVs from MinIO, run Pandas transformations or Spark jobs, and write feature tables back to the database. Many teams use Jupyter notebooks (hosted on a JupyterHub cluster) during development for cleaning and visualization.

How are AI and machine learning models trained on-prem without SageMaker or Azure ML?

For training ML models, AWS SageMaker or Azure ML would spin up managed GPU instances and run training jobs. On-premise, you use local GPU servers or a Kubernetes cluster with GPUs. Data scientists develop in Jupyter notebooks (as on SageMaker Studio) using frameworks like PyTorch or TensorFlow. They iterate on models locally or in a multi-node setup. A key open-source tool here is MLflow: it handles experiment tracking, model packaging and registry. MLflow can be run entirely on-prem – with a backend database (e.g. PostgreSQL or even SQLite for small teams) and a file/object store (e.g. MinIO).

As shown above, MLflow’s architecture is flexible: it “supports different databases through SQLAlchemy, including PostgreSQL, MySQL, SQLite…”, and uses an artifact store (often S3 or MinIO) for large model files. In practice we might deploy a Postgres database on-prem for MLflow’s backend and MinIO for model artifacts. This replaces SageMaker’s built-in experiment tracker and model registry. An alternative to MLflow is Kubeflow, a Kubernetes-based MLOps platform that orchestrates end-to-end pipelines, but it is more complex. Many startups pair simple Airflow or Kubeflow Pipelines with MLflow to cover all stages.

How are AI models deployed and served on-prem in manufacturing systems?

After training, the model must be deployed to serve predictions. In the cloud one might use SageMaker Endpoints or Azure Container Instances. On-prem, we containerize the model (e.g. Docker image with FastAPI) and deploy on a local Kubernetes cluster. The cluster might be bare-metal or VMware, but it runs standard Kubernetes (just as AWS EKS does in the cloud). Serving frameworks include Seldon Core or KFServing (KServe) on Kubernetes, which simplify rolling out scalable microservices for inference. Alternatively, BentoML or TorchServe can host models in containers. For example, you could package a PyTorch model and serve it behind a REST API in Docker, with Kubernetes handling scaling.

To replace cloud APIs like AWS Lambda or Azure Functions, teams sometimes use open-source function platforms (OpenFaaS, Kubeless) or simply embed logic in custom services. The end result is an on-prem inference service that your manufacturing MES or dashboard can call. In an industrial scenario, this could mean image-based defect detection running on a local GPU server, with response times in milliseconds thanks to no network hops.

How do manufacturers monitor AI systems and visualize production data on-prem?

Monitoring covers both system metrics and model/ business metrics. In AWS you’d use CloudWatch (metrics, logs, dashboards). On-prem, a standard approach is Prometheus + Grafana for system and model metrics. Prometheus scrapes CPU/GPU load or custom app metrics, and Grafana builds dashboards. For logs, teams use the ELK stack or its AWS fork OpenSearch. Prometheus/Grafana effectively replaces CloudWatch’s monitoring features (“Prometheus and Grafana offer an open source, platform-agnostic alternative” to CloudWatch). For example, sensor readouts and prediction counts can be graphed in Grafana in real time. Grafana also integrates with Prometheus to show alerting and uptime for your services.

For business intelligence and data visualization (the main use case), cloud tools like QuickSight or Power BI are replaced with open-source BI platforms. Popular choices are Apache Superset and Metabase (both have easy drag-drop dashboards) or Redash. These connect to your data warehouse (e.g. Postgres, ClickHouse) and let managers slice and visualize data. In fact, industry sources list Superset and Metabase among the top open-source BI alternatives to QuickSight. For real-time metrics dashboards (e.g. production throughput, machine status), Grafana can also be used with time-series or Prometheus data. In all cases, the visualization tier runs on-prem (e.g. Superset on a VM or container), with data pulled from the local analytics database.

Table: Cloud AI services vs. on-prem alternatives

Component	Cloud (AWS/Azure/GCP)	Open-Source On-Premises
Object Storage	S3 / Blob / GCS	MinIO, Ceph (S3-compatible)
Data Warehouse	BigQuery / Redshift / Synapse	ClickHouse, Postgres, Greenplum, MariaDB
Streaming Ingestion	Kinesis, Event Hubs, Pub/Sub	Apache Kafka, RabbitMQ, Pulsar
IoT Connectivity	AWS IoT Core, Azure IoT Hub	MQTT (Mosquitto, HiveMQ), ThingsBoard
ETL / Pipeline Orchestration	AWS Glue, Data Factory	Apache Airflow, Apache NiFi, Argo Workflows
Notebooks / Dev	SageMaker Studio, Azure Notebooks	JupyterHub, VS Code (on-prem)
Experiment Tracking	SageMaker Experiments, Azure ML	MLflow (tracking & registry)
Model Training	SageMaker Training, Azure ML	Local GPUs + ML frameworks; Kubeflow/PyTorch/TensorFlow
Model Serving	SageMaker Endpoints, AKS, Cloud Run	Kubernetes + Seldon Core, KFServing, TorchServe
Monitoring (metrics/logs)	CloudWatch / Azure Monitor	Prometheus + Grafana; ELK/OpenSearch (logs)
Visualization / Dashboards	QuickSight, Power BI, Looker	Apache Superset, Metabase, Redash; Grafana

What are the pros and cons of on-prem AI versus cloud AI for manufacturers?

• Cost: Cloud AI is Opex-based (pay-as-you-go), so it has low upfront cost and easy scaling. But fees can add up over time. On-premise requires CapEx (buying servers, GPUs) and maintenance, which is higher upfront. For stable, predictable workloads, on-prem may be cheaper long-term, but it needs careful planning (including power/cooling costs).
• Scalability & Flexibility: Cloud offers almost limitless scale on demand (ideal for training large models). On-premise capacity is fixed; scaling means buying hardware and can be slow. Many manufacturers compromise with a hybrid model or edge devices for real-time tasks.
• Data Security & Compliance: On-prem grants full control over data and infrastructure, easing compliance (e.g. GDPR, industry certifications). Cloud providers do offer robust security, but some companies still prefer to keep IP-bound data in-house. For example, defect images or patient-sensitive manufacturing data might never leave the site.
• Latency & Performance: Real-time factory AI (robotic vision, anomaly alerts) benefits from local processing. On-prem AI incurs minimal network delay (“millisecond responses” for vision/robotics). Cloud AI can introduce latency from internet hops, which might be unacceptable in a control loop. Thus time-sensitive workloads often run at the edge.
• Reliability & Connectivity: Cloud depends on internet connectivity; any outage could halt AI functions. On-prem systems can run isolated and continue operating (with their own failover). However, on-prem systems risk hardware failures – so good monitoring and backup plans are needed.
• Expertise: Cloud AI offloads infrastructure management to the provider, easing the IT burden. On-prem deployment demands in-house DevOps/DevSecOps skills to install, update, and secure the stack. For startups without a large IT team, managed cloud services can accelerate development, but lock you into that vendor.
• Interoperability: Many AI tools (e.g. MLflow, Kubeflow, PyTorch, Prometheus) are designed to be vendor-agnostic and run on any Kubernetes cluster. As CNCF notes, most organizations now run AI on Kubernetes and rely on open-source standards. On-prem setups often use the same tech stack as cloud (containers, K8s), so they can be ported to/from cloud if needed.

In summary, on-premise AI gives control and predictability at the cost of upfront investment and maintenance. Cloud AI provides agility and simplicity but trades off some control. For manufacturers with strict compliance or latency needs, the control and privacy of on-prem can be worth it. Conversely, startups wanting to move fast with limited capital may begin in the cloud and later repatriate services.

Are manufacturers actually running AI on-prem today? Industry trends and real examples

Open-source MLOps is rapidly maturing. For instance, Kubeflow and MLflow have seen broad adoption; even Red Hat’s Open Data Hub uses Kubeflow for on-prem AI workflows. The Cloud Native community launched a Kubernetes AI Conformance program to standardize AI on K8s, reflecting that “90% of enterprises identify open source as critical to their AI strategies”. Major players like VW and Spotify use hybrid architectures: VW processes sensitive data on-prem for autonomous vehicles, and Spotify uses cloud for large-scale training but plans multi-cloud to control costs. In manufacturing specifically, companies often build their own on-site analytics platforms (e.g. MES-integrated AI dashboards) using these open tools.

One concrete example: a smart factory might use Prometheus/Grafana to monitor machine KPIs (replacing CloudWatch), Apache Superset for production dashboards (replacing QuickSight), and MLflow+PyTorch for training predictive maintenance models (replacing SageMaker) – all running in the plant’s data center or private cloud.

Grids and Guides Case Study

At Grids and Guides, we’ve implemented a very similar on-premise AI stack for a manufacturing client operating under strict data-residency and latency constraints. Their requirement was clear: no production data could leave the factory network, yet leadership wanted real-time dashboards and AI-driven insights for maintenance and operations.

Check the case study for more details

Our approach was not to “rebuild the cloud on-prem,” but to select only the essential open-source components and integrate them into the client’s existing IT and MES ecosystem. We designed an on-prem architecture where machine telemetry flowed through Kafka and MQTT, landed in a MinIO-based data lake, and was modeled in ClickHouse for fast analytical queries. Apache Superset and Grafana were used to deliver live operational dashboards to plant managers, while MLflow and PyTorch supported predictive-maintenance experiments entirely within the factory network.

The key value we delivered was pragmatism over tooling: instead of introducing every possible MLOps component, we focused on what directly impacted decision-making on the shop floor—data reliability, query speed, and explainable visualizations. This allowed the client to start with analytics and visualization first, and gradually layer in AI models without disrupting production systems.

For manufacturing teams, this pattern has worked well: start with on-prem data foundations and visualization, then evolve toward AI—all while staying compliant, avoiding vendor lock-in, and keeping operational control in-house.

How can manufacturers start building an on-prem AI stack without over-engineering?

If you’re exploring a similar on-prem AI or analytics stack and want to understand how this looks in practice, we’re happy to share more details. For several client engagements, Grids and Guides maintains internal reference setups—including Docker Compose–based deployments for Kafka, MinIO, ClickHouse, MLflow, Superset, and Grafana—that teams can run inside their own data centers or private networks.

If you’d like:

• A walkthrough of the actual on-prem architecture we’ve implemented
• A callback discussion to map this stack to your factory or product roadmap
• Or access to sample Docker Compose files and deployment patterns we use with clients

You can reach out to us for a quick conversation. Even a short call often helps founders and engineering leaders clarify what to build first, what to skip, and how to phase an on-prem AI journey without over-engineering.

Should manufacturing startups build AI on-prem or in the cloud?

For non-technical founders in manufacturing, an on-prem AI strategy means assembling open-source equivalents of cloud services. While it requires more in-house effort, it avoids vendor lock-in and meets industrial requirements. In each lifecycle stage, common cloud tools have mature open-source counterparts: e.g. MinIO for S3, Kafka for Kinesis, MLflow/Kubeflow for SageMaker, Prometheus/Grafana for CloudWatch, Superset/Metabase for QuickSight, etc. Many tools are containerized and run on any Kubernetes cluster, so you can build a private MLOps stack that closely mirrors cloud architectures.

This approach has proven viable at scale: according to CNCF, a majority of organizations are successfully running AI on open-source Kubernetes platforms. By choosing and integrating the right stack of open-source tools, a manufacturing startup can achieve full AI/ML capabilities on-premises – unlocking advanced analytics and data visualization without sacrificing data security or control.