Cognitive load refers to the total amount of mental effort being used in a person's working memory. Think of it as the mental bandwidth a developer has. When that bandwidth is consumed by non-coding tasks, their ability to focus on complex problem-solving decreases.

Developers often have to switch between dozens of different tools, dashboards, and configuration files just to get a simple change deployed. Each context switch forces the brain to load a new set of information, which is mentally taxing. This constant juggling of tools and processes increases cognitive load, which can lead to burnout, more mistakes, and slower innovation.

Platform engineering is the discipline of designing, building, and maintaining an internal developer platform. Think of it this way: if the IDP is the product, then the platform engineering team is the product team responsible for its life cycle. Their customers are the organization's developers.

This approach means the platform is treated like any other software product. The platform engineering team is responsible for understanding developer needs, defining a roadmap, and providing a reliable, secure, and easy-to-use set of tools.

A frequent question is the distinction between a platform and a portal. While the terms are sometimes used interchangeably, they represent different layers of the developer experience. Think of the internal developer platform (IDP) as the entire engine, encompassing all the tools, workflows, and infrastructure that power your development life cycle.

The internal developer portal, on the other hand, is the dashboard for that engine. It's the graphical user interface (GUI) that provides developers with a single, centralized place to access the platform's capabilities. A developer portal can help developers discover and use tools, view documentation, scaffold new services, and check the status of their builds without needing to understand the complex machinery running underneath. While a comprehensive IDP often includes a developer portal to improve usability and discoverability, the platform itself is the complete set of underlying technologies and automated processes.

To see how an IDP works in practice, let's look at a common internal developer platform example using Google Cloud. Imagine a developer needs to spin up a new microservice that requires its own database and CI/CD pipeline.

The developer would need to manually create a Cloud Source Repository, write a Terraform module to provision a Cloud SQL database, create and configure a Google Kubernetes Engine (GKE) deployment file (YAML), set up a new trigger in Cloud Build, and manage IAM permissions for everything. This could take days and involve multiple teams.

The process is much simpler.

Request from the portal: The developer logs into the developer portal and finds a pre-built template for a "Python microservice with a Cloud SQL database."

Simple inputs: They fill out a short form with the service name and their team name.

Orchestration begins: The developer clicks "Create," and the platform orchestrator takes over.

Automated actions: Behind the scenes, the orchestrator performs a series of automated tasks using Google Cloud APIs:

• It creates a new repository in Cloud Source Repositories from the template
• It runs a Terraform plan to provision a new Cloud SQL for PostgreSQL instance
• It creates the necessary manifests and applies them to the Google Kubernetes Engine (GKE) cluster to create a new deployment environment
• It configures a new CI/CD pipeline in Cloud Build to automatically build and deploy the service

Completion: Within minutes, the process is complete. The developer gets a notification with links to their new repository, their running application in the GKE staging environment, and the database credentials, all without writing a single line of configuration.