If you’re in need of a comprehensive understanding of what constitutes an AI tech stack, including its components and structure, you’re in the right place. Read on for all you need to know about the full architecture of the AI technology stack, including vector databases, modeling frameworks, and deployment strategies.
Let’s start with the basics: What is an AI tech stack? Essentially, it’s a series of layers that describe the various components that make it possible to use AI systems. These layers encompass each application, vector database, framework, and infrastructure component you need to make AI work. The layers span hardware and software, breaking down managing AI into a more modular approach, and thus making it easier to scale, upgrade, monitor, and optimize.
Core layers of the AI tech stack
There is no single, definitive tech stack for AI because approaches differ across this still-emerging sector. However, we can broadly group a modern AI stack into four essential layers: data, model, deployment, and monitoring and optimization. Some of the components in your stack will span multiple layers; others will sit within a single layer.
It’s worth noting the role of APIs in all of this. Well-governed APIs are fundamental to successful AI deployment. You use them to feed data to your model and when you deploy, monitor, maintain, and optimize it. This puts robust, efficient API management at the heart of your AI success.
1. The data layer
This is the foundation of the AI tech stack. It includes everything that’s needed to ingest, store, and process data. That ranges from physical hardware such as AI accelerators for compute capabilities (something we explored recently in this article on the AI value stack), to local and cloud storage systems.
In terms of data ingestion, the data layer of a modern AI stack encompasses everything you need to gather raw data from APIs, logs, and databases. The data layer also includes data lakes for storing raw, unstructured data and vector databases for AI-specific memory. Processing and extract, transform, and load (ETL) also fall within the data layer, providing all the elements needed to clean and normalize data to make it machine-ready
2. The model layer
The model layer is where you find the brains of the machine learning tech stack. It includes frameworks such as PyTorch, which is an open-source deep learning library used to build models.
Alongside libraries in the model layer are training environments. These are where algorithms learn from data (algorithm selection falls within this layer of the tech stack too). The training environments feed labeled data into the model, enabling it to learn relationships and patterns, operating within predefined hyperparameters. You can optimize the model iteratively during this process, with the model improving on its answers during each iteration.
There are two key approaches to this type of training. You can choose to train a model from scratch or to take a foundation model, such as GPT-4, Llama, or Anthropic’s Claude, and fine-tune it. Training from scratch takes longer (often significantly so) and costs more in computational terms, making the adaptation of pretrained, customizable models a popular choice for many use cases.
3. The deployment layer
Once you have a trained model, you need to develop and deploy it as a usable AI product. This is where the deployment layer of the AI tech stack comes in. It encompasses everything you need to package and deploy your model. Think model serving (wrapping the model in an API, so applications can talk to it) and the need to orchestrate (using tools like Kubernetes to manage scale, ensuring the system doesn’t crash under heavy user load).
4. The monitoring and optimization layer
Deployment doesn’t mean your work is done. It then becomes time to monitor, maintain, and optimize your AI, ensuring it remains flexible and scalable in line with your business needs, as well as secure and efficient. This is what the monitoring and optimization layer is all about.
Components in this layer cover everything from tracking model drift (where a model becomes stale or less accurate over time) and performance metrics (such as latency and speed). Having a process in place to capture and analyze this data is crucial. It supports you to keep your AI deployment enterprise-ready, as you use the components in this layer to trigger retraining cycles to continually sharpen and improve it. You can automate your workflow here to support a cycle of continual improvement.
How the AI tech stack parts work together
We’ve broken the tech stack into four layers to explain it. Each of these layers may fall within the remit of different teams, but they need to be integrated and work together for AI to succeed. This means you need a view of your overall pipeline and how to integrate each component within it.
The end-to-end pipeline
The end-to-end pipeline extends from the point at which data is ingested (from sources such as APIs, databases, logs, and streams) into storage systems such as data lakes and vector databases. ETL pipelines then process, clean, and transform the data to make it suitable for training and inference.
Next, the data feeds into training environments to build, fine-tune, and update models. You can then package and expose these models via APIs, for consumption by applications and services, with the deployment infrastructure handling scaling, load balancing and reliability during real-time or batch inference.
Once the model is live, your monitoring systems collect performance, accuracy and usage metrics, feeding the results into continuous improvement loops to underpin automated retraining and optimization workflows.
The role of MLOps
Supporting the ongoing success of the AI tech stack is MLOps – machine learning operations. These are the practices and processes that automate and standardize the machine learning lifecycle, providing the operational glue that connects the data, model, deployment, and monitoring layers.
MLOps standardizes your workflows for training, testing, deploying, and updating models across environments. You can apply version control to models, data, and configurations to ensure reproducibility and traceability, for superior efficiency and scalability. You can also use CI/CD pipelines to automate model validation, deployment, and rollback processes.
Within MLOps, monitoring and alerting are critical. You can embed these to detect issues such as model drift, performance degradation, and failures.
You’ll also need to enforce governance, security, and compliance controls across the AI lifecycle – something we’ve looked at in this helpful article.
While the MLOps workload may be distributed across different roles within your enterprise, you’ll need close collaboration between your data scientists, engineers, and operations teams to underpin ongoing success. You can streamline this through shared tooling and processes, promoting specific tool use and collaboration for your AI software stack via a centralized platform.
Why the AI tech stack matters
Why does the AI tech stack matter? Because it underpins the long-term success of your AI deployment strategy, ensuring your model remains efficient, optimized and scalable. This latter point is particularly relevant as you move from a successful AI prototype to the realities of production.
Moving from prototype to production
As we discussed in our whitepaper on how to successfully implement, govern, and grow AI (download it free here), the shift from prototype to production can be painful. BCG reports that 74% of companies struggle to achieve and scale value when adopting AI. However, understanding the layers and role of the AI tech stack can help. You can build resilient systems on a robust foundation, designing for scalability and enterprise-wide impact, not just prototype success.
Managing cost and complexity
Breaking down the modern AI stack into easily manageable layers, as we’ve done above, also helps you control the cost and complexity of your deployment. You can understand the cost and role of each layer, along with the interdependencies of the layers and individual components. This clarity provides plenty of scope to analyze costs at each layer of the stack and to understand the wider impact of any proposed changes within a layer.
Essential technologies in a modern AI stack
Within each of the AI tech stack layers, there are certain key technologies and tools to be aware of. These include data and ETL tools, modeling frameworks, storage and database tooling, and tools for monitoring and observability.
Data and ETL tools
There are plenty of excellent tools for moving and cleaning data, ensuring a seamless flow of high-quality input into your model. Established industry tooling in this area includes:
- Apache Airflow, which orchestrates and schedules data pipelines, managing dependencies between ingestion and transformation tasks.
- dbt (data build tool), which transforms raw data inside warehouses using analytics engineering best practices.
- Apache Spark, which you can use for distributed data processing for large-scale batch and streaming workloads.
- Kafka/managed streaming services, which enable real-time data ingestion from events, logs, and user interactions.
- API connectors and ingestion tools, which enable you to pull structured and unstructured data from third-party systems and services.
Modeling frameworks
Several frameworks have emerged that are useful for research, production and supporting retrieval-augmented generation (RAG) and agent workflows. Examples include:
- PyTorch – a research-friendly deep learning framework with dynamic computation graphs, widely used for experimentation and innovation.
- TensorFlow – a production-oriented framework with strong tooling for deployment, scaling, and mobile/edge inference.
- Keras – a high-level API (often used on top of TensorFlow) that simplifies model development and prototyping.
- LangChain – a framework for building LLM-powered applications, including RAG and agent workflows.
- Hugging Face Transformers – a library providing access to pretrained foundation models and model fine-tuning utilities.
Storage and databases
Reliable storage and database solutions are an essential part of the modern AI stack. Some leading examples include:
- PostgreSQL – a reliable relational database commonly used for metadata, configurations, and experiment tracking.
- Amazon S3/MinIO – object storage for large datasets, model artifacts, and training outputs.
- Data lakes – these are centralized repositories for raw, unstructured, and semi-structured data. There are cloud-native options such as Amazon S3 and AWS Lake Formation, as well as open and hybrid data lake technologies such as Databricks Lakehouse, Apache Hudi, and Apache Iceberg.
- Pinecone – a managed vector database optimized for similarity search and AI memory.
- Milvus – an open-source vector database designed for high-performance embedding storage and retrieval.
- Hybrid storage architectures – examples of these platforms, which combine relational, object, and vector storage to support diverse AI workloads, include AWS Lake House architecture and Google BigLake.
Monitoring and observability
The final category of tools to focus on is those devoted to keeping AI visible and healthy, such as:
- MLflow, which manages the machine learning lifecycle, including experiment tracking, model registry, and deployment metadata.
- Arize AI, which provides model observability, drift detection, and performance monitoring in production.
- Prometheus, which collects and stores metrics related to system health, latency, and resource usage. Prometheus has a wide range of uses, as this example of setting service level objectives for your APIs ably demonstrates.
- Grafana, which you can use to visualize metrics and logs to support real-time monitoring and troubleshooting.
You can also use a range of custom alerting and logging tools to detect failures, anomalies, and degradation across models and infrastructure, supporting you to keep everything healthy and efficient over time.
Demand a strong ROI from your AI tech stack
AI investment isn’t cheap, so obtaining value is an essential part of its adoption. This means not only choosing the right frameworks and technologies as part of your AI tech stack but using API governance discipline as a strategic enabler. Your AI return on investment (ROI) depends on it!