Azure Databricks provides different types of clusters to meet various workload requirements. These clusters are designed to handle different types of workloads, such as data engineering, data science, and machine learning. Here are the main types of clusters in Azure Databricks:

Standard Clusters: These clusters are suitable for most general-purpose workloads. They provide a balance between performance and cost-efficiency. Standard clusters can be configured with different instance types and sizes to meet specific requirements.

High Concurrency Clusters: These clusters are optimized for scenarios where multiple users need to run concurrent workloads. High concurrency clusters use a shared pool of resources to efficiently handle multiple queries and jobs simultaneously.

GPU Clusters: These clusters are equipped with powerful GPUs (Graphics Processing Units) and are designed for computationally intensive workloads, such as deep learning and GPU-accelerated analytics. GPU clusters provide significant performance improvements for tasks that require parallel processing.

Auto Scaling Clusters: These clusters automatically scale up or down based on workload demand. Auto scaling clusters are ideal for scenarios where workload requirements vary over time. They ensure optimal resource utilization and cost efficiency by dynamically adjusting the cluster size.

Serverless Pools: Serverless pools are a new type of cluster in Azure Databricks that provide on-demand compute resources without the need to manage and provision clusters manually. Serverless pools are well-suited for ad-hoc and interactive workloads, as they automatically scale resources based on workload demand.