A Field Guide to the AI Infrastructure Stack (for Data Leaders)

Every company is suddenly “doing AI.” The executives have spoken, the board is asking questions, and somewhere in your organization, someone is already spinning up their third vector database this quarter. Last week, I talked to a data leader who discovered their team was running four different embedding stores across three departments, none of which could talk to each other. Sound familiar?

As someone who’s spent years in the orchestration trenches with Apache Airflow and watched the infrastructure landscape evolve, I’ve noticed a pattern: while AI adoption is accelerating, infrastructure maturity is wildly uneven. Some teams are running sophisticated MLOps pipelines in production, while others are still figuring out how to move their notebooks out of individual laptops.

This isn’t necessarily a problem. Different companies are at different stages of their AI journey. But it does create a challenge for data leaders trying to make strategic infrastructure decisions. The AI tooling landscape shifts monthly, vendor positioning is often confusing, and the gap between marketing promises and production reality can be… substantial.

That’s why I wanted to start The Orchestrator’s Notebook with a shared mental model. Before we dive into specific tools, strategies, or war stories, let’s map the terrain. Think of this as your field guide to the AI infrastructure stack as it exists in 2025.

The Seven Layers of AI Infrastructure #

When I think about the AI infrastructure stack, I see seven distinct layers. Each serves a specific purpose, but they’re interdependent in ways that aren’t always obvious.

1. Data Platform Layer #

This is your foundation: data lakes, warehouses, streaming platforms, and the pipelines that move data between them. Companies like Snowflake, Databricks, and the cloud providers own much of this space, though plenty of teams are still running sophisticated setups with open-source tools.

The key insight here is that your AI is only as good as your data platform. If you can’t reliably get clean, fresh data to your models, nothing else matters. This layer tends to be the most mature in most organizations because it predates the current AI boom.

2. Feature and Embedding Stores #

This is where raw data becomes model-ready inputs. Feature stores handle traditional ML features (think customer lifetime value, rolling averages, categorical encodings), while embedding stores manage vector representations from your foundation models.

The distinction matters because they solve different problems. Feature stores are about consistency, reusability and ensuring the same feature logic applies in training and serving. Embedding stores are about similarity search and retrieval at scale. Companies like Tecton and Feast dominate feature stores, while Pinecone, Weaviate, and others compete in the vector database space.

3. Training Infrastructure #

This encompasses everything needed to actually train models: compute orchestration, experiment tracking, hyperparameter tuning, and distributed training frameworks. The cloud providers have substantial offerings here (SageMaker, Vertex AI, Azure ML), but plenty of teams prefer more composable approaches with tools like Weights & Biases, Kubeflow, or Ray.

The big trend I’m watching is the shift from “train everything from scratch” to “fine-tune foundation models.” This changes the infrastructure requirements significantly. There is less need for massive training clusters, more need for efficient fine-tuning and adaptation workflows.

4. Inference Infrastructure #

Once you have a trained model, you need to serve it. This layer handles model deployment, scaling, versioning, and runtime optimization. The requirements vary dramatically depending on whether you’re doing batch inference on millions of records or real-time inference with sub-100ms latency requirements.

Tools range from simple REST APIs to sophisticated model serving platforms like Seldon, KServe, or the cloud providers’ managed offerings. The complexity here often surprises teams! Serving models reliably in production involves a lot more than just pickle.dump() and Flask.

5. Orchestration and Workflow Management #

Yes, I’m biased. I’ve spent years in this space! But orchestration is where everything comes together. You need to coordinate data pipelines, trigger training jobs, manage inference workflows, and handle all the dependencies between these components.

Apache Airflow dominates much of this space, though newer tools like Prefect and Dagster are gaining traction with ML-specific features. The key challenge is that AI and ML workflows are more dynamic and interdependent than traditional data pipelines. A failed training job might need to trigger a rollback to the previous model version, or a data quality issue might require pausing inference entirely.

6. Evaluation and Observability #

How do you know if your AI system is working? This layer handles model performance monitoring, data drift detection, prediction quality evaluation, and the operational metrics that keep you up at night.

The tooling here is still evolving rapidly. Companies like Arize, Evidently AI, and Fiddler are building specialized ML monitoring platforms, while traditional observability vendors are adding AI-specific features. The challenge is that ML systems can fail in subtle ways that don’t show up in traditional infrastructure monitoring.

7. Governance and Safety #

The newest and arguably most critical layer: ensuring your AI systems are safe, compliant, auditable, and aligned with organizational policies. This includes bias detection, explainability, privacy-preserving techniques, and regulatory compliance.

This layer is where I see the most greenfield opportunity. The tooling is nascent, the requirements are still being defined, and every organization has different needs based on their industry and risk tolerance.

What Feels Durable vs. Volatile in 2025 #

After watching this space evolve, some patterns are emerging about which layers are stabilizing and which remain in flux.

The durable layers tend to be the foundational ones. Data platforms are mature and consolidating around a few major players. Training infrastructure is stabilizing, especially as more teams shift to fine-tuning rather than training from scratch. Orchestration is relatively mature, though still evolving to handle AI-specific workflows better.

The volatile layers are higher up the stack. Vector databases are having their Kubernetes moment: lots of options, most will consolidate or disappear, but the underlying need is real. Evaluation tooling is changing rapidly as we learn what metrics actually matter for different types of AI systems. And governance tooling is in its infancy. Everyone knows they need it, but few are sure exactly what “it” looks like.

The inference layer sits somewhere in between. The basic patterns are well-understood, but the performance and cost optimization techniques are still evolving, especially for large language models.

Where the Gaps Are #

Two areas consistently surprise teams with their complexity: orchestration and governance.

Orchestration feels like it should be simple! Just run some jobs in the right order, right? But AI workflows have characteristics that traditional data pipelines don’t: they’re more dynamic, have complex dependencies, need sophisticated error handling, and require coordination across multiple compute environments. Many teams underestimate this complexity until they’re trying to coordinate model training across multiple GPUs while simultaneously running inference workloads and retraining on new data.

Governance is the bigger gap. Most organizations have some version of data governance, but AI governance is different. What happens when your customer service chatbot starts giving incorrect refund information? How do you trace that back through your fine-tuning data, model versions, and deployment pipeline? You need to track model lineage, ensure reproducibility, monitor for bias and safety issues, and often comply with regulations that are still being written. The tooling here is immature, and many teams are building custom solutions.

You Don’t Need to Bet on Every Tool #

Here’s my closing take: you don’t need to have an opinion about every tool in this stack, but you do need a map of the terrain.

Start by auditing which of these seven layers you actually have versus which ones you’re assuming exist. Most data leaders think they know their stack, but AI workflows expose gaps that weren’t obvious in traditional data pipelines. Map your current tools to each layer, identify what’s missing or inadequately covered, then focus your next 90 days on the 1-2 layers that represent your biggest risk or bottleneck

Once you have that clarity, understand which layers are most critical for your specific use cases. If you’re doing traditional ML with tabular data, you might not need sophisticated embedding stores. If you’re building chatbots with foundation models, you might not need complex training infrastructure.

Focus your tool evaluation energy on the layers that will differentiate your organization or represent significant risk if they fail. For many teams, that’s the data platform and orchestration layers. Get those wrong, and everything else struggles.

And remember that the stack is interconnected. Your choice of vector database affects your embedding pipeline design, which affects your orchestration requirements, which affects your observability needs. Think in systems, not individual tools.

The AI infrastructure landscape will continue evolving rapidly, but the fundamental layers and their relationships are becoming clearer. Having a shared mental model helps you navigate the vendor noise and focus on what actually matters for your organization’s AI journey.

In upcoming posts, I’ll dive deeper into why orchestration deserves special attention in AI systems and explore the governance lessons that could help us avoid repeating data infrastructure’s growing pains.