Cloud infrastructure automation tools handle provisioning, configuring, and managing cloud resources without manual console work. The right tool depends on how much control you need, whether your team has dedicated DevOps resources, and how tightly you want infrastructure tied to your application code.

With AI agents writing more backend code in 2026, the infrastructure layer has become a bottleneck. AI can generate application logic quickly, but Terraform modules, CloudFormation templates, and Kubernetes manifests still require manual review and DevOps expertise. Tools that eliminate or reduce the infrastructure configuration layer are increasingly relevant.

This guide compares the leading cloud infrastructure automation tools based on what matters most in 2026: automation depth, AI compatibility, and developer accessibility.

Quick Comparison

Tool	Approach	Languages	Cloud Support	AI-Ready	Learning Curve
Encore	Infrastructure from code	TypeScript, Go	AWS, GCP	Yes	Low
Terraform	Infrastructure as code	HCL	Multi-cloud	Partial	High
Pulumi	Infrastructure as code	TS, Python, Go	Multi-cloud	Partial	Medium
AWS CDK	Infrastructure as code	TS, Python, Java	AWS	Partial	Medium
SST	Infrastructure as code	TypeScript	AWS, Cloudflare	Partial	Medium
AWS CloudFormation	Templates	YAML/JSON	AWS	No	High
Google Cloud Deployment Manager	Templates	YAML/Jinja2	GCP	No	High

What Matters in 2026

Automation Depth

Not all infrastructure automation is equal. There's a spectrum:

Template-based (CloudFormation, Deployment Manager): You write YAML or JSON templates describing every resource. The tool provisions them. You manage the full complexity.

Infrastructure as code (Terraform, Pulumi, CDK): You write infrastructure definitions in a configuration language or programming language. More expressive than templates, but infrastructure is still a separate codebase from your application.

Infrastructure from code (Encore): Infrastructure is inferred from your application code. When you write new SQLDatabase("users") in TypeScript, the tool provisions PostgreSQL locally, RDS on AWS, or Cloud SQL on GCP. No separate infrastructure definitions to write or maintain.

The deeper the automation, the less infrastructure code your team manages. A Terraform setup for a typical backend with a database, Pub/Sub, and a cron job can easily run to hundreds of lines of HCL across multiple files. Someone on the team needs to understand, review, and update that code every time the application changes. For small teams without dedicated DevOps resources, that overhead competes directly with shipping product. Tools that eliminate the infrastructure layer entirely let those teams move at the speed of their application code.

AI Compatibility

AI agents generate application code well. They struggle with infrastructure configuration:

• Terraform HCL: AI can generate HCL, but the output often has wrong provider versions, missing IAM policies, incomplete networking rules, or security misconfigurations. Every generated module needs careful review.
• Pulumi / CDK: Better than HCL since AI writes in real programming languages, but infrastructure still requires understanding of cloud services, permissions, and networking.
• Infrastructure from code (Encore): AI writes new SQLDatabase("orders") instead of generating a Terraform module for RDS with subnet groups, security groups, parameter groups, and IAM policies. The platform handles all of that provisioning from the single line of application code. There's no infrastructure configuration for the AI to get wrong, which means no DevOps review cycle for infrastructure output. The only review needed is on application logic, where AI is most reliable.

For teams where AI agents write a significant portion of code, this compounds quickly. Every feature that touches infrastructure (a new database, a Pub/Sub topic, a cron job) either requires a corresponding infrastructure PR that someone reviews manually, or it's handled automatically by the platform. The infrastructure automation tool you choose determines which of those worlds you live in.

Local Development

Infrastructure automation tools traditionally focus on cloud provisioning and ignore local development. This creates a gap: your production infrastructure is automated, but local development relies on Docker Compose files, mock services, or manual database setup.

Tools that automate infrastructure across both local and cloud environments eliminate this gap. When your local environment runs the same PostgreSQL, Pub/Sub, and cron that production uses, all provisioned automatically from the same code, you catch integration issues before they reach staging. You also remove the "works on my machine" class of bugs that come from differences between a local SQLite mock and a production PostgreSQL instance, or between a local in-memory queue and a production SNS/SQS setup.

Tool Deep Dives

Encore - Infrastructure from Code

Encore takes a fundamentally different approach: instead of writing infrastructure definitions separately, you declare what you need directly in your TypeScript or Go application code. Encore parses these declarations and provisions the right resources automatically, across local development, preview environments, and production on AWS or GCP.

Strengths:

• No separate infrastructure code to write or maintain
• Automatic provisioning across local dev, preview environments, and production
• Deploys to your AWS or GCP account (not a managed platform)
• Built-in distributed tracing, metrics, and service catalog
• AI agents write only application code, so there's no infrastructure config to hallucinate
• MCP server gives AI tools visibility into schemas, traces, and architecture
• No DevOps expertise required

Trade-offs:

• TypeScript and Go only
• AWS and GCP only (no Azure)
• Requires adopting Encore's declarative patterns for infrastructure primitives

Portability: A common concern with infrastructure-from-code is lock-in, but Encore is designed to minimize it. The framework is open source, and roughly 99% of the code you write is standard TypeScript or Go. The infrastructure declarations (SQLDatabase, Topic, CronJob) are the Encore-specific parts, but they're thin wrappers around standard cloud resources that run in your own AWS or GCP account. You own all infrastructure from day one. If you want to leave, encore build docker generates a standard Docker image you can deploy anywhere, and migrating means taking over CI/CD and infrastructure provisioning yourself rather than rewriting application logic.

How it works:

import { SQLDatabase } from "encore.dev/storage/sqldb";
import { Topic } from "encore.dev/pubsub";
import { CronJob } from "encore.dev/cron";

// Database - automatically provisioned
const db = new SQLDatabase("orders", { migrations: "./migrations" });

// Pub/Sub topic - automatically provisioned
const orderCreated = new Topic<OrderEvent>("order-created", {
  deliveryGuarantee: "at-least-once",
});

// Cron job - automatically scheduled
new CronJob("daily-report", {
  title: "Generate daily order report",
  schedule: "0 9 * * *",
  endpoint: generateReport,
});

Locally: PostgreSQL in Docker, NSQ for Pub/Sub, cron runs on schedule. On AWS: RDS, SNS/SQS, CloudWatch Events. On GCP: Cloud SQL, GCP Pub/Sub, Cloud Scheduler.

The application code is the infrastructure definition. You don't write or maintain Terraform, CloudFormation, or Docker Compose separately.

Best for: Backend teams who want full infrastructure automation without writing or maintaining IaC, and teams using AI agents for development.

Try Encore free →

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Terraform - Industry Standard IaC

Terraform by HashiCorp is the most widely adopted infrastructure as code tool. It uses HCL (HashiCorp Configuration Language) to define resources across multiple cloud providers.

Strengths:

• Multi-cloud support (AWS, GCP, Azure, and hundreds of providers)
• Massive ecosystem of modules and providers
• Industry standard with extensive documentation
• State management and drift detection
• Large community and hiring pool

Trade-offs:

• HCL is a separate language to learn and maintain
• State file management adds operational complexity
• No local development solution, so infrastructure automation applies only to cloud environments
• AI-generated HCL requires careful review for security and correctness
• Separate codebase from application code creates drift risk over time

Best for: Teams with DevOps resources who need multi-cloud support and fine-grained control over infrastructure.

Compare Encore vs Terraform →

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Pulumi - IaC in Real Languages

Pulumi lets you define infrastructure using real programming languages (TypeScript, Python, Go, C#) instead of YAML or HCL.

Strengths:

• Infrastructure in TypeScript, Python, Go, or C#
• Multi-cloud support
• Better IDE experience than HCL (autocompletion, type checking)
• Good state management with Pulumi Cloud
• Reusable components and abstractions

Trade-offs:

• Still requires understanding cloud services and permissions
• Infrastructure is a separate codebase from application code
• Complexity scales with cloud resource count
• No local development automation

Best for: Teams managing complex multi-cloud infrastructure who want to use familiar programming languages.

Compare Encore vs Pulumi →

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AWS CDK - TypeScript for AWS

AWS CDK (Cloud Development Kit) uses TypeScript, Python, or Java to define AWS infrastructure as high-level constructs.

Strengths:

• TypeScript for infrastructure definitions
• High-level constructs that bundle common patterns
• Direct integration with AWS services
• Generates CloudFormation under the hood

Trade-offs:

• AWS only (no multi-cloud)
• Requires understanding AWS services and CDK patterns
• Constructs can be opaque, so debugging often means reading generated CloudFormation
• No local development automation
• Separate codebase from application code

Best for: AWS-only teams who want TypeScript-based infrastructure definitions with direct cloud control.

Compare Encore vs AWS CDK →

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SST - Serverless-First IaC

SST (formerly Serverless Stack) builds on CDK and Pulumi to provide a higher-level developer experience for serverless and full-stack applications on AWS and Cloudflare.

Strengths:

• Higher-level abstractions than raw CDK
• Live Lambda development environment
• Good Next.js and Astro deployment support
• Active community and documentation

Trade-offs:

• AWS and Cloudflare only
• Serverless-first, so less suited for long-running services
• Still requires understanding underlying cloud services
• Infrastructure definitions are separate from application code

Best for: Teams building serverless applications on AWS who want a better developer experience than raw CDK.

Compare Encore vs SST →

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AWS CloudFormation - Native AWS Templates

AWS CloudFormation is AWS's native infrastructure automation service. You write YAML or JSON templates describing resources, and CloudFormation provisions them.

Strengths:

• Native AWS integration
• No additional tooling required
• Extensive resource coverage
• Stack management and rollbacks

Trade-offs:

• Verbose YAML/JSON templates
• AWS only
• Steep learning curve
• Debugging template errors is difficult
• No local development story

Best for: Teams already invested in the AWS ecosystem who prefer native tooling.

Compare Encore vs CloudFormation →

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Google Cloud Deployment Manager - Native GCP Templates

Google Cloud Deployment Manager is GCP's native infrastructure automation. It uses YAML templates with optional Jinja2 or Python templating.

Strengths:

• Native GCP integration
• No additional tooling beyond gcloud CLI
• Template reuse with Jinja2

Trade-offs:

• GCP only
• Less ecosystem support than Terraform
• Limited community and documentation compared to alternatives
• YAML templating is verbose and error-prone

Best for: GCP-only teams who want native infrastructure automation without third-party tools.

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Infrastructure from Code vs Infrastructure as Code

The key distinction in 2026 is between tools that require you to write and maintain infrastructure definitions (IaC) and tools that infer infrastructure from your application code (IFC).

Infrastructure as Code (Terraform, Pulumi, CDK, SST)

You write infrastructure definitions in a separate codebase. Application code and infrastructure code are maintained independently. Changes to application requirements (new database, new queue) require corresponding changes to infrastructure code.

Advantages:

• Fine-grained control over every resource
• Multi-cloud flexibility
• Mature ecosystems with large communities

Challenges:

• Two codebases to maintain and keep in sync. When a developer adds a new service that needs a database, someone also needs to update the Terraform modules, and those changes need to be reviewed by someone who understands both the application and the cloud provider
• Requires DevOps expertise to write and review infrastructure code
• AI agents struggle to generate correct infrastructure configs, which means AI-assisted development still bottlenecks on human infrastructure review
• No local development automation. These tools only handle cloud provisioning, so local dev relies on separate Docker Compose or manual setup

Infrastructure from Code (Encore)

Infrastructure requirements are declared in application code. The tool parses the code and provisions the right resources automatically. There's one codebase, and infrastructure stays in sync with the application by definition.

Advantages:

• No separate infrastructure codebase. Adding a database or Pub/Sub topic is a one-line change in application code, not a separate PR to an infrastructure repo
• No DevOps expertise required for standard infrastructure patterns. The platform knows how to provision RDS, configure security groups, and set up IAM policies from a database declaration
• AI agents write only application code, which means the entire AI output is reviewable by any backend developer, not just someone who understands cloud infrastructure
• Automatic local development with production-like infrastructure. encore run provisions real databases and queues locally, so development and production behavior match
• Infrastructure is always in sync with application requirements by definition, since they're declared in the same codebase

Challenges:

• Less fine-grained control than raw IaC for edge-case resource configurations
• Limited to supported cloud providers and resource types
• Requires adopting the framework's patterns for infrastructure declarations

The lock-in concern is worth addressing directly. With traditional IaC, you're locked into a configuration language (HCL, CloudFormation YAML) and a state management system, but you control every resource. With infrastructure from code, you adopt a framework's patterns, but the underlying code is standard TypeScript or Go, the infrastructure runs in your own cloud account, and you can export to Docker at any time. The lock-in profile is different rather than worse: you trade configuration-level control for not having a separate infrastructure codebase to maintain.

For teams with dedicated platform engineering resources and complex multi-cloud requirements, traditional IaC tools provide the control they need. For backend teams who want to ship without managing infrastructure, infrastructure from code eliminates the operational overhead.

Making the Right Choice

Choose Encore if:

• You want infrastructure automation without writing or maintaining IaC
• You're building TypeScript or Go backends on AWS or GCP
• Your team doesn't have dedicated DevOps resources
• You're using AI agents and want to minimize infrastructure code they generate
• You want automation across local dev, preview environments, and production

Choose Terraform if:

• You need multi-cloud support including Azure
• You have DevOps expertise on the team
• You need fine-grained control over every cloud resource
• You want the largest ecosystem and community

Choose Pulumi or CDK if:

• You want IaC in a real programming language
• You have cloud service expertise on the team
• You need AWS-specific constructs (CDK) or multi-cloud with type safety (Pulumi)

Choose SST if:

• You're building serverless applications on AWS
• You want a better developer experience than raw CDK
• You need live Lambda development

Conclusion

The right cloud infrastructure automation tool depends on your team's DevOps capacity and how much infrastructure control you need. For backend teams who want full automation without maintaining IaC, Encore provides infrastructure from code that provisions databases, Pub/Sub, cron, caches, and object storage automatically across local, preview, and production environments on AWS or GCP.

For teams with platform engineering resources who need multi-cloud or fine-grained control, Terraform and Pulumi remain strong options.

Try Encore free →

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• Encore vs Terraform
• Encore vs Pulumi
• Encore vs AWS CDK
• Encore vs SST
• Encore vs CloudFormation
• Infrastructure as Code: An Introduction
• How to Deploy to AWS Without Terraform
• Best Cloud Development Platforms 2026