Alexis Kestler
Introduction
As infrastructure automation becomes critical for software teams, Terraform and Kubernetes have emerged as two leading yet complementary tools. But what is each one best suited for, and how exactly do they differ? This in-depth guide compares Terraform vs Kubernetes across all key factors, including use cases, workflows, languages and more. Read on for clear guidance on when to reach for Terraform or Kubernetes.
What is Terraform?
Terraform is an open source infrastructure as code software tool created by HashiCorp. It allows you to safely and efficiently provision, modify and version infrastructure in a descriptive, declarative way across on-prem and multi-cloud environments.
Key Terraform capabilities include:
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Infrastructure Provisioning: Terraform can spin up infrastructure components like networks, load balancers, and compute instances across public clouds as well as private data centers.
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Declarative Syntax: Infrastructure is defined in Terraform using HCL (HashiCorp Configuration Language), a declarative language optimized for reading and writing config files.
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Execution Plans: Before making any infrastructure changes, Terraform generates an execution plan showing what actions it will take to reach your desired end state. You review and approve the plan before Terraform provisions or modifies resources.
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State Tracking: Terraform maintains persistent state data to track all provisioned infrastructure and metadata. This provides visibility and aids change management.
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Modularity: Terraform configurations can be modularized into reusable components called modules. Modules improve abstraction, encapsulation and organization.
In summary, Terraform excels at safely provisioning and modifying infrastructure across environments in a declarative, plan-driven way.
What is Kubernetes?
Kubernetes (also known as K8s) is an open source container orchestration system originally designed by Google. It automates tasks like container deployment, scaling and management across clusters of hosts.
Key Kubernetes capabilities include:
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Container Orchestration: Kubernetes efficiently schedules and manages container workloads across clusters while abstracting away underlying infrastructure.
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Scaling and Load Balancing: Applications running on Kubernetes can seamlessly scale up or down based on demand. Kubernetes also load balances traffic between application instances.
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Self-Healing: Kubernetes constantly monitors the health of containers and nodes, automatically restarting or rescheduling failed containers, and replacing failed nodes.
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Service Discovery and Networking: Kubernetes groups sets of containers into services and handles cross-cluster networking, service discovery and load balancing for these services out of the box.
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Portability: Kubernetes runs anywhere – on prem, public clouds, private clouds and bare metal – providing workload portability between environments.
In summary, Kubernetes simplifies deploying and running distributed containerized applications by handling orchestration, availability and networking.
Key Differences Between Terraform and Kubernetes
Now that we‘ve introduced both tools, let‘s analyze how Terraform and Kubernetes differ across some key vectors:
Focus Area
The root difference between the tools is their areas of focus. Terraform handles provisioning infrastructure while Kubernetes specializes in container orchestration:
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Terraform provisions the underlying compute, storage, networking and security resources needed to run applications. It focuses on infrastructure automation.
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Kubernetes abstracts infrastructure away from developers and operators by handling container orchestration tasks like deployment, scaling and availability. Its focus is the orchestration layer rather than the provisioning layer.
In essence, Terraform provisions infrastructure that tools like Kubernetes then leverage to run container workloads. The tools occupy complementary spaces but have little direct overlap.
Configuration Language
Terraform and Kubernetes take different approaches when it comes to their configuration languages:
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Terraform utilizes a custom language called HCL (HashiCorp Configuration Language) for defining infrastructure resources. HCL provides terse, readable syntax optimized specifically for infrastructure descriptions.
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Kubernetes leverages YAML files for declaring Kubernetes objects. YAML is a broadly adopted general purpose language adept at describing hierarchical data (like Kubernetes objects) as well as handling complex data types.
In practice, HCL leads to more compact and focused syntax for infrastructure clients, while YAML provides Kubernetes greater flexibility across object kinds.
Learning Curve
Terraform and Kubernetes also differ substantially when it comes to ease of getting started:
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Terraform offers a smoother onboarding experience, especially for infrastructure engineers. HCL, execution plans and state tracking introduce clear structure. Most features operate intuitively. Critical functionality surfaces right away.
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Kubernetes has a steeper initial learning curve. It employs its own expansive terminology system. Operators must grasp many abstract components like pods, deployments, services and ingresses before achieving proficiency. The tool rewards perseverance with immense power, but mastery takes time.
Consequently, Terraform tends to be more beginner-friendly, while Kubernetes rewards investment in the tool.
Configuration Drift Management
A key Terraform capability lacking in Kubernetes is configuration drift detection:
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Terraform leverages state tracking and execution plans to detect and visualize drift from desired infrastructure specifications. You can view a plan outlining any anomalies between code and state before applying changes. This prevents surprises.
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Kubernetes has no native notion of configuration drift or planning. The live state of a cluster can diverge from config over time without warning. While Kubernetes aims to reach specified declarations, ops teams risk losing visibility into deviations over time.
As infrastructure systems grow large, Terraform‘s drift planning becomes critical for maintainability.
Resource Creation Methods
While both tools are declarative, the user experience creating resources differs:
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Terraform relies on a CLI (command line interface) invoked from a shell. After authoring infrastructure as code, terraform commands like
init,planandapplyexecute configuration against chosen providers. -
Kubernetes instead employs
kubectl, a robust CLI focused on managing live resources in clusters.kubectlfeatures verb-oriented commands allowing operators tocreate,delete,describe,editand otherwise manipulate declared objects.
Terraform keeps a higher level orientation centered on infrastructure lifecycles rather than discrete object operations. Kubernetes offers granular control over dynamic resources in running environments.
| Factor | Kubernetes | Terraform |
|---|---|---|
| Created By | HashiCorp | |
| Initial Release | 2014 | 2014 |
| Configuration Language | YAML/JSON | HCL |
| Command Line Tool | kubectl | terraform |
| Learning Curve | Steep | Gradual |
This table summarizes differences between the tools across key dimensions. With differences established, let‘s now explore common use cases.
Common Kubernetes Use Cases
Given its container-centric design, below are some typical scenarios making use of Kubernetes:
Orchestrating Containers Across Machines
Kubernetes efficiently schedules container workloads across nodes in a cluster. This makes it well-suited for coordinating resource usage across teams of machines, whether on premises or in the cloud. Kubernetes abstracts the distribution of containers across distinct underlying hosts.
Managing Computing Resources
For organizations running multiple complex applications generating volatile traffic patterns, Kubernetes simplifies capacity planning through automatic scaling. Teams can reduce excess margin and overprovisioning by right-sizing clusters dynamically via Kubernetes instead of maintaining excess static capacity.
Running CI/CD Platforms
By containerizing continuous integration/continuous deployment (CI/CD) tools like Jenkins, Spinnaker and CircleCI, teams can leverage Kubernetes for rapid, repeatable deployments of these systems. Kubernetes ensures availability and scalability.
Storage Orchestration
Via persistent volumes and storage classes, Kubernetes enables teams to attach dynamic storage to stateful applications. Cluster operators can integrate solutions ranging from public cloud block storage (EBS, GCE PersistentDisk) to distributed file systems (GlusterFS, Ceph) for container workloads.
Service Discovery and Load Balancing
Kubernetes provides native facilities for service discovery and load distribution. The Kubernetes service resource automatically load balances traffic across pods while exposing the service via the cluster DNS. This alleviates manual IP and port management.
As evidenced above, Kubernetes unifies deployment, resource allocation, networking, scaling and availability for containerized apps across infrastructure, providing comprehensive container orchestration.
Common Terraform Use Cases
Now let‘s explore some typical use cases where Terraform delivers immense value:
Tracking Infrastructure
Using persistent state, Terraform maintains a consistent snapshot of all managed infrastructure and associated metadata to serve as a source of truth. By applying changes to reach a desired state described in config files, Terraform provides visibility into an infrastructure‘s exact components and their relationships over time.
Multi-Cloud Deployment
Via providers, Terraform can manage infrastructure across all major cloud platforms – AWS, Azure, Google Cloud and more – in a unified way. Teams can stand up interdependent resources across environments while Terraform handles underlying API interactions, abstracting away cloud distinctions.
Managing Multi-Tier Apps
Terraform readily handles complex multi-tier applications with interdependent components like load balancers, databases and container clusters. Dependencies between resources are automatically handled per config rules. Terraform assures order of operations when modifying layered architectures.
Software-Defined Networking
Terraform integrates readily with software-defined networking (SDN) tools like NSX-T, Cisco ACI and more via providers. It manages logical networks under software control programmatically, saving teams from manual processes while unlocking agility.
Where Kubernetes focuses inward on application availability and scalability, Terraform manages outward facing dependencies – infrastructure, networks, subnets, firewalls – essential for distributed systems. The tools work hand in hand.
Final Thoughts: Terraform and Kubernetes Better Together
This guide explored key distinctions between Infrastructure as Code pioneer Terraform and container orchestration leader Kubernetes.
We saw how the tools occupy complementary capability spaces, with Terraform serving as the preferred choice for provisioning foundational infrastructure across environments while Kubernetes excels at deploying and running containerized, distributed applications.
For modern development teams embracing infrastructure automation and cloud native architecture patterns, HashiCorp Terraform and Kubernetes form an incredibly powerful combination – especially as Kubernetes norms require dynamically provisioned infrastructure.
By leveraging Terraform for infrastructure lifecycle management and Kubernetes for container orchestration, teams unlock robust, scalable and portable systems extending across clouds and data centers.
Hopefully this overview has provided you clarity on how Terraform and Kubernetes compare and where each tool shines. To dig deeper, refer to these additional resources:
Feel free to reach out with any other questions!
