====== AI CMS / Framework / Model ======
===== Overview =====
Yes. In AI, there are equivalent concepts to CMS / Framework / Library similar to traditional programming, but they are divided into multiple packaging layers.
Goal of this page: Explain AI using the mindset of OpenCart / Laravel / SDK so developers can understand it immediately.
What it is: In AI, there are also equivalent concepts to CMS / Framework / Library similar to traditional programming.
Key idea: AI is not a single block, but is divided into multiple packaging layers, from “ready to use” to “build from the core.”
===== 1) Direct Mapping: Traditional Programming ↔ AI =====
^ Traditional Programming ^ AI Equivalent ^ Meaning ^
| CMS (OpenCart, WordPress) | AI CMS / Prebuilt AI App | Ready to use |
| Framework (Laravel, Django) | AI Framework / LLM Framework | Build AI app |
| Library / SDK | Model / Inference Library | Assemble components |
| Custom low-level code | Train model from scratch | Expensive & rare |
===== 2) AI CMS Layer – Ready to Use (No-code / Low-code) =====
What it is: Prepackaged AI applications with built-in UI + workflow + model integration.
Equivalent: OpenCart / WordPress
==== Examples ====
[[https://anythingllm.com|AnythingLLM
]]
[[https://flowiseai.com|Flowise
]]
[[https://openwebui.com|Open
WebUI]]
[[https://botpress.com|Botpress
]]
[[https://rasa.com|Rasa
]]
==== Key Characteristics ====
Built-in UI
Workflow built using forms / nodes
Configured via YAML / JSON
Plug in an LLM and run
==== Use When ====
Internal chatbot
Knowledge assistant
Fast PoC
Team without ML skills
==== Limitations ====
Hard to deeply customize
Performance not optimized
Weak governance if not configured properly
👉 This is true AI CMS.
===== 3) AI Framework Layer – Like Laravel / Django =====
What it is: Frameworks for building AI applications using code.
Equivalent: Laravel / Django
==== Popular Frameworks ====
[[https://www.langchain.com|LangChain
]] ⭐
[[https://www.llamaindex.ai|LlamaIndex
]]
[[https://haystack.deepset.ai|Haystack
]]
[[https://learn.microsoft.com/semantic-kernel|Semantic
Kernel]]
==== What They Do ====
Prompt management
RAG (Retrieval Augmented Generation)
Tool calling
Memory & agent flow
==== Mapping ====
Laravel ≈ LangChain
Django ≈ LlamaIndex
==== Use When ====
Building a real AI product
Have a backend team
Need flow control & testing
===== 4) Pretrained Model Layer – Pretrained Models =====
What it is: AI models trained on large datasets.
Equivalent: Database engine / Search engine
==== Examples ====
[[https://ai.meta.com/llama|LLaMA
/ LLaMA 3]]
[[https://mistral.ai|Mistral
/ Mixtral]]
[[https://huggingface.co/Qwen|Qwen
]]
[[https://deepseek.com|DeepSeek
]]
[[https://ai.google.dev/gemma|Gemma
]]
==== Characteristics ====
No UI
No workflow
Can be self-hosted
Can be fine-tuned
🚫 Not a CMS – just the “brain.”
===== 5) AI Inference Engine Layer =====
What it is: Runtime for executing models efficiently.
Equivalent: JVM / PHP runtime / DB engine core
==== Examples ====
[[https://github.com/vllm-project/vllm|vLLM
]]
[[https://github.com/ggerganov/llama.cpp|llama.cpp
]]
[[https://developer.nvidia.com/tensorrt-llm|TensorRT-LLM
]]
[[https://github.com/huggingface/text-generation-inference|TGI
]]
==== Use When ====
High throughput
Cost optimization
On-prem / air-gapped
===== 6) Managed AI Platform (AI SaaS) =====
What it is: AI platforms fully operated by vendors.
Equivalent: Shopify / Salesforce
==== Examples ====
[[https://openai.com|OpenAI
/ Azure OpenAI]]
[[https://aws.amazon.com/bedrock|AWS
Bedrock]]
[[https://cloud.google.com/vertex-ai|Google
Vertex AI]]
==== Pros / Cons ====
✅ Fast, no infrastructure management
❌ Higher cost, vendor lock-in
===== 7) Overall Layer Diagram =====
AI CMS (No-code)
│ Flowise / AnythingLLM
│
AI Framework (Code)
│ LangChain / LlamaIndex
│
Pretrained Model
│ LLaMA / Mistral
│
Inference Engine
│ vLLM / llama.cpp
│
Infrastructure
│ Cloud / On-prem GPU
===== 8) What to Choose in Practice? =====
"Like OpenCart, download and run" → AnythingLLM / Flowise
"Like Laravel" → LangChain / LlamaIndex
"Enterprise, data control" → Open-source model + vLLM + RAG
====== AI Service Classification – Company & Use Case ======
===== Goal =====
This page classifies popular AI services today using a senior developer mindset (CMS / Framework / Runtime), including:
Company behind it
Real-world use case
Position in system architecture
===== Layer Overview =====
AI is not a single block, but consists of 6 layers, from “ready to use” to “infrastructure core.”
AI App / Tool ↓ AI Platform (API) ↓ AI Framework ↓ Pretrained Model ↓ Inference Engine ↓ Infrastructure (GPU)
===== 1) AI App / AI Tool (Ready to Use) =====
Equivalent: CMS (WordPress / OpenCart)
^ Service ^ Company ^ Main Use ^
| ChatGPT | OpenAI | General assistant, Q&A, coding, spec writing |
| Claude Chat | Anthropic | Long document analysis, reasoning |
| GitHub Copilot | GitHub / Microsoft | Code completion, pair programming |
| AnythingLLM | Mintplex Labs | Internal document chatbot |
| Flowise | FlowiseAI (OSS) | Rapid AI workflow building |
| Open WebUI | Open-source | Chat UI for self-hosted models |
| Botpress | Botpress Inc. | Customer service chatbot |
| Rasa | Rasa Technologies | Conversation engine |
Use when: Fast PoC, internal use, small team
===== 2) AI Platform / AI SaaS =====
Equivalent: Shopify / Firebase
^ Platform ^ Company ^ Main Use ^
| OpenAI API | OpenAI | LLM API: chat, embedding, tool calling |
| Azure OpenAI | Microsoft | OpenAI + enterprise security |
| AWS Bedrock | Amazon | Multi-model AI for enterprise |
| Google Vertex AI | Google | End-to-end AI platform |
| IBM watsonx | IBM | AI + data governance |
Use when: Calling AI via API, no GPU management
===== 3) AI Framework (Build AI Logic) =====
Equivalent: Laravel / Django
^ Framework ^ Company / Organization ^ Main Use ^
| LangChain | LangChain Inc. | Orchestrate prompt, agent, tools |
| LlamaIndex | LlamaIndex Inc. | RAG (document → answer) |
| Haystack | deepset | Search + QA pipeline |
| Semantic Kernel | Microsoft | Enterprise AI orchestration |
| CrewAI | CrewAI Inc. | Multi-agent workflow |
| AutoGen | Microsoft Research | Agent collaboration |
Use when: Building real AI features, need testing & CI/CD
===== 4) Pretrained Model (AI Brain) =====
Equivalent: Database / Search Engine
^ Model ^ Company ^ Main Use ^
| GPT-4 / GPT-5 | OpenAI | Reasoning, general-purpose |
| Claude 3 / 4 | Anthropic | Long context, safety |
| Gemini | Google | Multimodal |
| LLaMA 3 | Meta | Open-source, self-host |
| Mistral / Mixtral | Mistral AI | Lightweight, fast, low cost |
| Qwen | Alibaba | Multilingual |
| DeepSeek | DeepSeek AI | Reasoning, open |
Note: Models have no UI and no workflow.
===== 5) Inference Engine (Runtime) =====
Equivalent: JVM / PHP-FPM
^ Engine ^ Company / Organization ^ Main Use ^
| vLLM | UC Berkeley | High-throughput LLM serving |
| llama.cpp | Open-source | Run LLM on CPU / edge |
| TensorRT-LLM | NVIDIA | GPU optimization, low latency |
| TGI | Hugging Face | Production LLM endpoint |
| ONNX Runtime | Microsoft | Cross-platform inference |
Use when: Self-host models, high traffic, cost optimization
===== 6) Infrastructure (GPU / Cloud) =====
Equivalent: Server / Datacenter
^ Infra ^ Company ^ Main Use ^
| AWS GPU | Amazon | AI cloud |
| Azure GPU | Microsoft | AI cloud |
| GCP GPU | Google | AI cloud |
| NVIDIA A100 / H100 | NVIDIA | Large-scale training / inference |
| On-prem GPU | Enterprise | Full privacy |
===== Real Architecture Example =====
React / Vue
↓
Laravel / Go API
↓
LangChain / LlamaIndex
↓
OpenAI API or LLaMA
↓
(vLLM if self-host)
===== Important Notes =====
⚠️ AI is not deterministic like traditional code.
Always require:
Guardrails
Evaluation
Human review