What is AI infrastructure? Core components and how to structure your stack
- AI infrastructure is the combined stack of compute, storage, networking, orchestration, security, and observability needed to develop, train, and deploy AI models in production.
- GPUs handle training and inference, but production AI products also need databases, APIs, background jobs, CI/CD, secure runtimes, and cost tracking.
- Most AI-specific platforms (Modal, Baseten, Together AI) focus on model serving or GPU access, leaving teams to assemble the rest of the stack separately.
- Northflank combines GPU provisioning, databases, background jobs, CI/CD, and secure multi-tenant runtimes in one control plane, which teams use to deploy fine-tuning jobs, inference APIs, and AI agent sandboxes without managing separate tools for each layer.
Get started with a stack template: Ollama, Jupyter on AWS / GCP / Azure, LiteLLM, DeepSeek v3.1, pgvector. Or start a project or book a demo.
AI infrastructure determines how a model moves from a training job to a production endpoint, and what else needs to run alongside it. This article covers the core components, how to assemble them into a stack, and where common AI platforms leave gaps.
AI infrastructure is the set of compute, storage, networking, orchestration, and developer tooling used to train, deploy, and run AI models. It includes GPUs and job schedulers, as well as APIs, databases, and observability tools.
GPUs are the most visible part of AI infrastructure, since training and inference both depend on high-performance hardware. A production AI product needs more than a model endpoint, though.
Teams typically also need secure runtimes for executing user or agent-submitted code, vector databases for storing embeddings, microservices for exposing models via APIs, and a deployment workflow across multiple environments.
The stack also needs CI/CD, cost tracking, logging, and metrics, the same categories found in general software infrastructure, adapted for AI workloads.
For anything beyond a basic demo, the infrastructure needs to support both the model and the application services it depends on.
Most AI infrastructure platforms focus on one part of the stack, typically model serving or GPU access. Production AI systems also need storage, databases, APIs, scheduling, secure environments, and deployment tooling.
Northflank runs AI and non-AI workloads in one platform, including GPUs, background jobs, preview environments, databases, and CI/CD.
AI infrastructure is made up of several layers that work together across training, deployment, and production monitoring. The table below summarizes each layer, followed by a more detailed look at each one.
| Layer | What it covers |
|---|---|
| Compute | GPUs for training and inference, CPUs for orchestration and API logic |
| Storage | Object storage, block volumes, and vector databases for training data, embeddings, and artifacts |
| Networking | Service discovery, public endpoints, and secure API access across nodes or clouds |
| Orchestration | Job scheduling, container lifecycle management, and autoscaling, usually built on Kubernetes |
| Developer platform | APIs, services, preview environments, and CI/CD for both ML and application code |
| Security | Tenant isolation, RBAC, secret management, and audit logs for running untrusted code |
| Observability | Logs, metrics, usage breakdowns, and cost attribution for production systems |
GPUs run training and inference workloads. CPUs handle orchestration, background jobs, and API logic. Most AI products run both together. For a breakdown of GPU providers and pricing models, see top GPU hosting platforms for AI.
AI workloads generate large volumes of data, including raw training sets, embeddings, and model artifacts. Object storage, block volumes, and vector databases each handle a different part of this. Storage that persists across sessions is also a requirement for persistent sandboxes used by AI agents and development environments.
Models and services are often split across nodes or clouds. The networking layer needs to support internal service discovery, public endpoints, and secure API access.
Orchestration covers scheduling jobs, running containers, managing autoscaling, and controlling workload lifecycles. Kubernetes is the common backbone, with additional tooling layered on top for usability.
AI teams need APIs, services, preview environments, CI/CD pipelines, and custom tooling, not just notebooks or dashboards. Teams shipping AI-generated or vibe-coded apps depend on this layer to move from a prototype to a deployed service.
Running untrusted or user-generated code is common in AI products such as agents and code sandboxes. A secure runtime with tenant isolation, RBAC, secret management, and audit logs is required at scale. For details on isolation models, see what is sandbox infrastructure.
Once a model is in production, logs, metrics, usage breakdowns, and cost attribution are needed to monitor behavior and debug issues.
With the core components defined, the next step is combining them into a stack that supports a complete product rather than just a model endpoint.
Most AI companies building on top of LLMs or training custom models need to run a mix of heavy compute jobs and lightweight services, give multiple teams access without sharing credentials, and deploy across clouds or regions.
A working AI infrastructure stack typically needs to support the following.
Frameworks like PyTorch and DeepSpeed require running long training jobs and scaling inference on demand based on traffic. For an overview of model options to fine-tune or self-host, see an engineer's guide to open source AI models.
The same container used in testing should be the one deployed to production, removing environment-specific differences.
Multi-cloud or hybrid GPU support provides flexibility and cost control, particularly for A100s, H100s, or spot instances. Northflank provisions compute nodes with current GPU models across cloud providers (see Northflank GPU support). For more on why teams choose this model, see why smart enterprises are insisting on BYOC for AI tools.
Most AI products need more than the model itself, including Redis for caching, Postgres for storage, and background workers for scheduled or async tasks. Northflank deploys services, databases, and background jobs within the same project as GPU workloads.
Pipelines need to support application code, ML logic, and model retraining or evaluation steps. Northflank's CI/CD system supports app deployments and training pipelines with GPU and background job support built in. For tooling options, see top AI tools for CI/CD pipeline automation.
Platforms that let users submit code, such as agents or code interpreters, need isolation that prevents container escapes and cross-tenant access. Northflank's secure runtime supports multi-tenancy with isolation by default. For more on running agent workloads safely, see ephemeral execution environments for AI agents.
As usage scales, tracking GPU time, container usage, team activity, and costs across environments becomes necessary for visibility and budgeting.
A number of platforms have been built specifically for AI workloads, with strong support for targeted problems such as GPU access or model inference at scale. Modal, Baseten, and Together AI all let teams deploy models without managing low-level infrastructure.
These platforms typically cover one part of the stack: serving the model. An AI product running in production usually also needs:
- Databases for user data, features, and embeddings
- Background jobs for scheduling tasks or fine-tuning models
- CI/CD pipelines for shipping updates across services
- Preview environments for testing new features
- APIs for exposing models in production
- Multi-service coordination
- Hybrid cloud or BYOC support for managing GPUs
This is because most AI-specific platforms are built for inference, not for full product development workflows. Northflank extends GPU support with the databases, CI/CD, APIs, and secure runtimes needed to build, ship, and scale a complete product. For a broader comparison of options, see AI deployment platforms.
Northflank supports the AI workload lifecycle, from training and fine-tuning to deployment, monitoring, and scaling, alongside the application services a model depends on in production.
GPU-intensive jobs like fine-tuning and inference run alongside CPU-based services, notebooks, or background workers. Northflank treats AI workloads as containers, managed consistently with the rest of the stack.
Northflank runs Postgres databases, FastAPI services, and scheduled jobs in the same project as GPU workloads. It also supports Redis, RabbitMQ, microservices, and Jupyter notebooks, with CI/CD and preview environments included. Templates are available for deploying common AI workloads such as Llama, Jupyter, and model trainers.
Northflank's runtime is built for multi-tenant usage, with RBAC, private networking, audit logs, and SOC 2 Type 2 compliance. It scales across environments, supporting concurrent workloads with strong tenant isolation by default.
Northflank supports bringing your own GPUs across providers and regions, including A100s, H100s, on-demand instances, and spot instances.
Teams running fine-tuning jobs, inference APIs, or AI agent backends on Northflank typically start from a Northflank stack template for a model or framework, then add databases, CI/CD, and GPU workloads to the same project. For a step-by-step walkthrough, see how to deploy vibe-coded Claude Code apps to production. Sign up or book a demo to discuss a specific stack.
AI infrastructure refers to the compute, storage, networking, and orchestration layers that support AI workloads. MLOps refers to the practices and tooling used to manage the model lifecycle, including training, versioning, and deployment, which run on top of that infrastructure.
Kubernetes is the common orchestration layer for AI infrastructure, handling container scheduling, autoscaling, and lifecycle management. Smaller workloads can run without it, but most production AI stacks use Kubernetes or a platform built on top of it.
A100 and H100 GPUs are commonly used for training and inference workloads. Availability and pricing vary by provider, which is why multi-cloud or BYOC GPU support is often part of an AI infrastructure stack.
Most AI products store user data, embeddings, and application state in databases, and use background jobs for tasks like scheduled fine-tuning or async processing. These components run the surrounding product, not just the model.
Running user-submitted code, such as in AI agents or code interpreters, requires a secure runtime with tenant isolation, RBAC, and audit logging to prevent container escapes and cross-tenant access.
