Artificial intelligence often feels like magic when you’re interacting with tools that write full essays, analyze documents, or help you make decisions. But behind that magic lies a detailed, multi-layered AI tech stack that makes everything function smoothly. Understanding this stack helps you make better choices, whether you’re evaluating AI tools for your business, planning to build something yourself, or just curious about how today’s systems actually work.

In the last year, the AI landscape has shifted dramatically. The rise of multimodal models, agentic workflows, and hybrid cloud-edge deployments has added even more layers to the stack. A recent overview published on InfoWorld offers a helpful snapshot of this evolution (read it here). Still, many explanations are either too technical or too shallow. This post aims to clarify the entire architecture in a clear, conversational way.

Let’s unpack the modern AI tech stack layer by layer so you can see how it all fits together.

The Foundation: Data Infrastructure

Every AI system begins with data, and the quality of that data determines the quality of the AI. You can think of data as the raw ingredients for a meal. Even the best chef (or model) can’t produce great results with stale ingredients.

Modern AI data infrastructure typically includes:

  • Data sources such as documents, images, transaction logs, APIs, and sensors.
  • Data pipelines that extract, transform, and load (ETL/ELT) data into usable formats.
  • Data storage solutions like data lakes, cloud storage buckets, or vector databases.
  • Data quality tools that detect duplication, inconsistencies, or missing information.

Many companies now use vector databases like Pinecone or Chroma for handling AI-specific workloads. These databases store numerical representations of text or images, enabling powerful semantic search and retrieval.

Real-world example: When you ask ChatGPT to summarize a contract you uploaded, a retrieval system looks for relevant information in vector form before the model generates an answer.

The Heart of the System: Model Layer

Right above the data infrastructure sits the model layer, which includes the machine learning models that perform tasks such as generation, classification, or prediction.

There are three major types of models you’ll encounter:

  1. Foundation models
    Large general-purpose models like ChatGPT, Claude, Gemini, and Llama. These models can handle broad tasks including reasoning, writing, and coding.

  2. Domain-specific models
    These models are trained for specific industries or problems, such as medical imaging models or models trained on legal documents.

  3. Fine-tuned models
    These build on top of foundation models but are adjusted using additional training data to perform better in a specific context.

A crucial point: models don’t work alone. They rely on fast, specialized hardware like GPUs and TPUs. When people talk about AI being expensive, they’re often referring to the cost of model training and inference on this hardware.

The Infrastructure Layer: Compute and Deployment

Once you have models, you need the computing infrastructure to run them. This layer determines performance, speed, cost, and scalability.

Key components include:

  • Cloud compute from providers like AWS, Google Cloud, and Azure.
  • GPUs/TPUs for training and high-performance inference.
  • Inference servers that host the model and respond to user requests.
  • Scaling systems that handle load spikes (for example, millions of users signing in after a new AI feature drops).

A modern trend is hybrid deployment, where part of the AI runs in the cloud and part runs locally on phones or edge devices. For example, Google now runs smaller versions of Gemini directly on Android devices, reducing latency and improving privacy.

The Intelligence Layer: Reasoning, Agents, and Orchestration

This is one of the most exciting layers today. It’s where raw model output becomes something more structured, reliable, and useful.

You can think of this layer as the “brain glue” that organizes AI behavior.

It includes:

  • Agents that can take action based on goals or instructions.
  • Tool use where models call APIs, search databases, or trigger workflows.
  • Planning and reasoning systems that help AI break down complex tasks.
  • Orchestration frameworks like LangChain, LlamaIndex, or OpenAI’s new function calling systems.

Example: If you ask an AI assistant to create a report, it might:

  1. Search your files for relevant information.
  2. Extract important data.
  3. Use a model to summarize it.
  4. Call a visualization library to generate charts.
  5. Compile everything into a formatted document.

This entire process sits in the orchestration layer, not the model itself.

Application Layer: User-Facing AI Products

At the top of the stack is the application layer, which is the part most people see. This includes:

  • Chatbots and assistants
  • AI-powered business software
  • Content creation tools
  • Search engines
  • Productivity apps
  • Analytics dashboards

Good applications handle context, user preferences, error recovery, and workflow integration. Great ones make AI feel almost invisible by focusing on experience rather than complexity.

For example, tools like Notion AI and Microsoft Copilot integrate AI directly into everyday tasks like writing or analyzing spreadsheets. The user never thinks about the data infrastructure or model layers beneath them.

Trust, Governance, and Security: The Overlapping Layer

One of the most overlooked parts of the AI tech stack is the trust and governance layer, which touches every other part of the system.

This includes:

  • Security controls to protect data from leaks or unauthorized use.
  • Ethical guidelines for responsible deployment.
  • Evaluation systems to measure accuracy, bias, and reliability.
  • Governance frameworks that track model updates and usage rules.
  • Monitoring tools that detect abnormal behavior or hallucinations.

Companies that deploy AI without this layer usually run into problems fast. For example, if an AI chatbot at a bank gives customers incorrect financial advice, the issue isn’t just a model problem. It’s a governance problem.

How the Layers Work Together

To make this more concrete, imagine you’re using an AI tool that drafts marketing copy.

Here’s what happens behind the scenes:

  1. The application layer receives your prompt.
  2. The orchestration layer determines the steps required (retrieve past campaigns, choose the right model, call external APIs).
  3. The model layer generates an initial draft.
  4. The data layer retrieves historical brand guidelines or tone examples.
  5. The model revises the text based on the retrieved data.
  6. The governance layer checks for banned phrases, compliance issues, or plagiarism.
  7. The result is delivered back to you.

All of this happens in seconds.

Conclusion: How to Navigate the AI Tech Stack

Understanding the AI tech stack empowers you to make better decisions and build more effective solutions. Whether you’re exploring AI tools or planning a deployment, clarity on the layers helps you spot bottlenecks and opportunities.

Here are three concrete next steps you can take:

  • Map your current tools or data processes to the layers in this stack.
  • Identify which layer matters most for your next project (data quality, model selection, orchestration, etc.).
  • Try experimenting with a modern orchestration framework like LangChain to see how components fit together.

The AI ecosystem is evolving quickly, but the stack itself provides a stable mental model for understanding what’s happening behind the scenes. As tools become more advanced, this structure will only grow more important for building reliable, trustworthy, and high-performing AI systems.