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# Portainer v3 Templates — AI Gap Analysis
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## Overview
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The official Portainer v3 templates (`templates.json`) contain **71 templates** across the following categories:
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| Category | Count | Examples |
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|---|---|---|
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| Database | 10 | MySQL, PostgreSQL, Mongo, Redis, CrateDB, Elasticsearch, CockroachDB, TimescaleDB |
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| Edge/IIoT | 14 | Softing EdgeConnectors, OPC Router, TOSIBOX, EMQX MQTT, Mosquitto, Node-RED, Litmus Edge |
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| Web/CMS | 8 | Nginx, Caddy, WordPress, Drupal, Joomla, Ghost, Plone |
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| DevOps/CI | 5 | Jenkins, GitLab CE, Dokku, Registry |
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| Monitoring | 4 | Grafana, Datadog, Sematext, Swarm Monitoring |
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| Messaging | 1 | RabbitMQ |
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| Storage | 3 | Minio, Scality S3, File Browser |
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| Serverless | 2 | OpenFaaS, IronFunctions |
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| Other | 6 | Ubuntu, NodeJS, Portainer Agent, OpenAMT, FDO, LiveSwitch |
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## AI Template Count in Official Repo: **0**
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There are **zero purely AI/ML-focused templates** in the current v3 template list.
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### Closest to AI
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- **Litmus Edge** (#70, #71) — Described as "enables industrial AI at scale" but is an OT data platform, not an AI deployment.
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- **Elasticsearch** (#13) — Used in vector search / RAG pipelines but is a general-purpose search engine.
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---
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## v2 Coverage Map
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This repository now provides **26 AI templates** organized into 9 sub-categories, mapped against the 4 AI infrastructure positioning pillars:
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### Pillar 1: GPU-Aware Fleet Management
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| Template | What It Proves |
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|---|---|
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| NVIDIA Triton | Multi-framework model serving across GPU fleet with dynamic batching |
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| vLLM | High-throughput LLM inference with tensor parallelism across GPUs |
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| NVIDIA NIM | Enterprise-grade NVIDIA-optimized inference microservices |
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| Ray Cluster | Distributed GPU scheduling across head + worker nodes |
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| Ollama / LocalAI | Single-node GPU inference engines |
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### Pillar 2: Model Lifecycle Governance
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| Template | What It Proves |
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|---|---|
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| MLflow + MinIO (Prod) | Versioned model registry + S3 artifact store + PostgreSQL tracking |
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| Prefect | Governed pipeline orchestration with scheduling, retries, audit logs |
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| BentoML | Model packaging with versioning and metrics endpoints |
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| Label Studio | Data labeling with project-level access control |
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| MLflow (standalone) | Experiment tracking and model comparison |
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### Pillar 3: Edge AI Deployment
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| Template | What It Proves |
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|---|---|
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| ONNX Runtime (edge profile) | CPU-only inference with memory/CPU limits for constrained devices |
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| NVIDIA Triton | Supports Jetson via multiarch images, model polling for OTA updates |
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| NVIDIA DeepStream | Video analytics pipeline for factory-floor cameras |
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### Pillar 4: Self-Service AI Stacks
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| Template | What It Proves |
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|---|---|
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| Open WebUI + Ollama | One-click ChatGPT-like deployment, no CLI needed |
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| Langflow / Flowise | Visual drag-and-drop agent builders |
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| n8n (AI-Enabled) | Workflow automation with AI nodes, accessible to non-developers |
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| Jupyter GPU | Notebook environment for data science teams |
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### Architecture Diagram Workloads
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| Diagram Node | Template(s) |
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|---|---|
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| LLM Fine-Tune | Ray Cluster |
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| RAG Pipeline | Qdrant + ChromaDB + Weaviate + Langflow/Flowise |
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| Vision Model | DeepStream, ComfyUI, Stable Diffusion WebUI |
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| Anomaly Detection | DeepStream (video analytics), Triton (custom ONNX/TensorRT models) |
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---
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## Remaining Gaps (Future Work)
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| Gap | Why It Matters | Potential Addition |
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| ARM/Jetson-native images | True edge AI on embedded devices | Triton Jetson images, ONNX Runtime ARM builds |
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| Air-gapped deployment | Industrial environments with no internet | Offline model bundling scripts |
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| Model A/B testing | Production model governance | Seldon Core or custom Envoy routing |
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| Federated learning | Privacy-preserving distributed training | NVIDIA FLARE or Flower |
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| LLM evaluation/guardrails | Safety and quality governance | Ragas, DeepEval, NVIDIA NeMo Guardrails |
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---
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*Generated: March 2026 — For use with Portainer Business Edition and Community Edition*
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