Scalr vs Spacelift: IaC Platforms Comparison

Infrastructure as code management platforms fundamentally determine how organizations handle cloud provisioning, maintain governance, and scale their operations. The platform you select becomes the foundation for how your teams work daily. Spacelift and Scalr both address infrastructure automation maturely, though with different architectural philosophies and operational priorities.

Where Spacelift emphasizes comprehensive orchestration across multiple tools with flexible deployment models and advanced automation, Scalr concentrates on optimizing the Terraform and OpenTofu experience with transparent consumption-based pricing and GitOps workflows.

This guide examines these platforms’ approaches to infrastructure automation and what their differences mean for your organization.

What we’ll cover:

1. What is Spacelift?
2. What is Scalr?
3. Spacelift vs Scalr comparison table
4. The main differences between Spacelift and Scalr
5. The dual-path deployment model: Infrastructure management reimagined

Spacelift provides infrastructure orchestration designed for teams managing diverse IaC toolchains and complex workflows. Rather than specializing in a single tool, the platform treats Terraform, OpenTofu, Pulumi, CloudFormation, Ansible, and Kubernetes as equal citizens within a unified orchestration environment.

The platform connects directly to version control systems where your infrastructure code lives. You define stacks pointing to your IaC code, establish policy rules for governance, and Spacelift executes deployments with enterprise security built in. 

The architecture supports everything from simple VCS-triggered deployments to sophisticated multi-stack dependencies with policy enforcement at every decision point.

Key features of Spacelift:

• Multi-IaC orchestration: First-class support for Terraform, OpenTofu, Terragrunt, Pulumi, CloudFormation, Ansible, Kubernetes, and custom workflows
• OPA policy integration: Policy-as-code at plan, approval, push, notification, and task decision points, unlimited policies in free tier
• Stack dependencies: Connect stacks and pass outputs between them for comprehensive automation chains
• Drift detection: Automatic identification of unauthorized infrastructure changes with policy-driven remediation
• Worker architecture: Choose between public workers or deploy private workers with full execution environment control
• Spacelift Intent: Natural language infrastructure provisioning, removing code requirements for specific workloads
• Deployment flexibility: Deploy as SaaS, cloud-hosted, self-hosted, or air-gapped environments
• Module testing: Automated Terraform module testing with ephemeral test environments
• User interface: Interface designed for managing sophisticated infrastructure workflows

Scalr specializes in Terraform and OpenTofu automation, building its architecture around these tools specifically. The platform’s design prioritizes GitOps workflows, hierarchical governance, and pricing transparency tied directly to usage.

The three-tier organizational hierarchy (Account > Environment > Workspace) provides the foundation for Scalr’s governance model. Configuration, credentials, and policies inherit down this hierarchy, enabling centralized control with distributed operations. This structure particularly benefits organizations where platform teams need to maintain standards while enabling development teams to operate independently.

Scalr’s architecture reflects a philosophy that reducing platform engineering toil comes from optimization for specific tools rather than broad tool support. Features like native CLI support and Atlantis-style PR workflows integrate deeply with Terraform and OpenTofu workflows rather than abstracting them.

Key features of Scalr:

• IaC tool support: Optimized for Terraform and OpenTofu, includes Terragrunt integration
• Atlantis-style workflows: Native PR-driven execution for GitOps deployment patterns
• Three-tiered hierarchy: Account, Environment, and Workspace structure with configuration inheritance
• Native CLI support: Work with Terraform/OpenTofu CLI locally while Scalr handles state and governance
• Flexible backend options: Use Scalr’s managed backend or bring your own (S3, Azure Blob, GCS)
• OPA and Checkov integration: Policy enforcement with pre-plan and post-plan checks
• Custom hooks: Integrate tools at multiple lifecycle stages (pre-init, pre-plan, post-plan, pre-apply, post-apply)
• AI-enriched plan output: AI analysis of Terraform plans for faster issue resolution
• Cost management: Native Infracost integration with UI-visible cost estimation
• Platform-as-code: Terraform provider for managing Scalr configuration itself
• Unlimited concurrency: Start with 5 concurrent runs, scale on request at no charge
• Self-hosted agents: Execute runs in your environment without extra costs

The table below summarizes the main comparison points between Spacelift and Scalr. We’ll cover them in more detail in the next section.

Feature	Spacelift	Scalr
Multi-IaC orchestration	Unified workflows across all tools	Terraform, OpenTofu, Terragrunt focus
Policy capabilities	Unlimited OPA policies, free tier+	OPA + Checkov, pre-plan checks
Pricing model	Concurrency-based, $399/mo	Run-based, $0.99/run flex rate
Free tier	2 users, unlimited policies, core features	50 runs/month, all features
Deployment options	SaaS, cloud, self-hosted, air-gapped	SaaS with self-hosted agents
AI capabilities	Intent (natural language), Saturnhead AI	AI-enriched plan output
Module testing	Automated with ephemeral environments	Private module registry
Stack dependencies	Advanced output passing, workflow chaining	Run triggers for workspace chaining
Configuration management	Native Ansible integration	Via Custom Flows
Drift handling	Policy-driven automated remediation	Detection with cause analysis
Worker control	Public or fully customizable private workers	Self-hosted agents (25 to start)
Backend flexibility	Managed Spacelift backend	Scalr backend or customer-managed (S3, Azure, GCS)
Organizational model	Stacks within Spaces	Account > Environment > Workspace hierarchy
GitOps workflow	VCS-driven with hooks	Atlantis-style PR execution
Concurrency	Based on tier, scales with pricing	Unlimited, starts at 5 concurrent runs

These platforms solve infrastructure automation from different angles. Understanding where they diverge helps clarify which architecture fits your organization’s needs.

Spacelift delivers comprehensive IaC management across multiple tools with powerful policy-as-code, while Scalr optimizes specifically for Terraform and OpenTofu with enterprise governance and transparent pricing.

1. Pricing transparency and predictability

The pricing model shapes how organizations think about infrastructure automation costs and how teams use the tool day to day.

Spacelift charges based on concurrency, starting at $399 per month. You pay for how many jobs can run at the same time, which aligns cost with team productivity rather than deployment frequency or resource count. The free tier includes two users with full access to core features, including unlimited policies.

Scalr uses run-based pricing, with a free tier that includes 50 monthly runs and access to all features. Additional runs cost $0.99 each, with automatic volume discounts applied at higher usage levels. Some run types don’t count toward usage, including drift detection and runs blocked early by policy or initialization failures. Scalr also includes baseline concurrency and self-hosted agents, with increases available through support as usage grows.

2. Organizational structure and scalability

How a platform organizes infrastructure has a direct impact on how governance works at scale. The underlying structure determines how policies, variables, and credentials are defined, shared, and enforced as environments grow.

Scalr is built around a fixed three-tier hierarchy (Account, Environment, and Workspace). Configuration defined at higher levels automatically flows down to the workspaces beneath them. This inheritance-based approach is designed to reduce repetition by centralizing setup and applying it consistently across large numbers of workspaces.

Spacelift takes a different approach. We organize infrastructure using Stacks grouped within Spaces, with explicit dependencies between stacks where needed. Shared configuration is handled through Contexts, which can be attached to any number of stacks without relying on a rigid hierarchy. This model gives teams fine-grained control over how configuration is applied, while still supporting centralized governance.

In practice, this flexibility makes it easier to model real-world organizations, where teams, environments, and ownership boundaries don’t always fit into a strict hierarchy. Platform teams can standardize guardrails where required, while allowing application teams to evolve independently.

3. Backend and state management flexibility

State management approaches reflect different trade-offs between control and operational consistency.

Scalr supports multiple state storage configurations. Teams can use Scalr’s managed state backend or configure customer-managed backends such as AWS S3, Azure Blob Storage, or Google Cloud Storage. In these setups, Scalr executes runs through its platform while state is stored in a location defined by the customer.

Spacelift uses a fully managed state backend that is tightly integrated with the platform’s orchestration, security, and governance capabilities. This managed approach reduces configuration overhead and ensures state handling is consistent across workflows, making it easier to enforce policies and maintain operational clarity at scale.

4. Multi-tool orchestration and workflow flexibility

Tool support philosophy is a key differentiator between these platforms, shaping how teams design and scale their automation workflows.

Spacelift is designed for teams that operate more than one infrastructure as code (IaC) tool. It provides a single orchestration layer with stack dependencies that allow outputs to flow directly between stacks. This makes it possible to build cohesive automation pipelines across Terraform, Pulumi, CloudFormation, Ansible, and Kubernetes, using one dependency model, one policy framework, and consistent observability.

Scalr takes a narrower approach, focusing primarily on Terraform and OpenTofu, with strong Terragrunt support. By concentrating on a smaller toolset, it emphasizes workflows that align closely with the Terraform ecosystem, such as pull request–driven workflows and deep CLI integration. This focus shapes Scalr’s positioning around a Terraform-centric operating model rather than a multi-tool orchestration platform.

5. Developer workflow and CLI integration

How developers interact with the platform daily matters significantly for adoption and productivity.

Scalr treats the Terraform and OpenTofu CLI as first-class workflows. Developers work entirely from their local CLI using familiar Terraform/OpenTofu commands while Scalr manages remote state, enforces policies, and orchestrates runs behind the scenes. 

Atlantis-style PR workflows enable developers to trigger plans and applies directly from pull request comments. This makes Scalr feel like a natural Terraform extension rather than a separate platform layer.

Spacelift provides a comprehensive web interface for infrastructure workflow management, supporting VCS-driven deployments, manual runs, and CLI interaction through Spacelift’s own CLI tooling. The platform emphasizes visibility and control through its interface design.

6. Policy and governance capabilities

Both platforms integrate OPA but implement governance at different lifecycle stages with different emphasis.

Spacelift includes unlimited OPA policies in all tiers, including free. Policies operate across multiple decision points: plan, approval, push, notification, and task policies. A policy workbench enables testing before deployment. This framework maintains consistency across all supported IaC tools, with policies written once applying uniformly whether you’re running Terraform, Pulumi, or Ansible.

Scalr combines OPA with Checkov for security scanning, emphasizing pre-plan enforcement. Policies live in VCS and follow GitOps patterns, allowing teams to review changes before activation. This approach catches problems earlier in the pipeline. 

Scalr’s hierarchical model enables policy inheritance from Account level through to individual Workspaces, reducing policy management overhead in large organizations.

7. Worker architecture and execution control

Spacelift offers public workers for ease of use alongside private workers running in your environment with customizable specifications. 

Private workers provide complete execution environment control, including custom Docker images, resource allocation, and network configuration. Organizations gain flexibility balancing security, performance, and operational control.

Scalr relies on self-hosted agents that run in a customer’s private network and communicate with the Scalr control plane using outbound-only connections. This model avoids inbound firewall rules and limits direct platform access to private infrastructure, with a fixed number of agents included by default.

8. Drift detection and remediation

How platforms handle infrastructure drift reflects different philosophies about automation versus investigation.

Scalr provides drift detection with cause analysis for understanding drift origins. The platform emphasizes visibility and drift investigation, reflecting a philosophy of understanding change before taking action. Drift detection runs remain free and don’t count against run quotas, encouraging frequent drift checks.

Spacelift’s drift detection continuously monitors infrastructure and can automatically initiate remediation through policy-driven responses. Drift detection integrates with the comprehensive policy framework, enabling definition of platform responses to different drift types. This supports automated correction workflows where appropriate.

9. AI and automation capabilities

Both platforms apply AI to infrastructure workflows, but with different areas of emphasis.

Scalr focuses on developer-facing assistance during Terraform planning. Its AI-enriched plan output analyzes changes and surfaces recommendations to help developers interpret errors independently. This approach aims to reduce escalation to platform teams by supporting self-service troubleshooting.

Spacelift applies AI to make infrastructure operations more intuitive and resilient at scale. Spacelift Intent allows teams to describe infrastructure changes in natural language while preserving policy enforcement. Saturnhead AI adds intelligent diagnostics and targeted troubleshooting recommendations for failed runs, helping teams identify root causes and resolve issues efficiently across complex environments.

10. Configuration management integration

Configuration management approaches differ significantly.

Spacelift treats configuration management as a first-class capability. Ansible playbooks receive the same sophisticated management as IaC, including centralized execution, complete visibility, and the ability to chain configuration management with infrastructure provisioning. Platform teams orchestrate both infrastructure and configuration from one platform.

Scalr enables configuration management through Custom Flows configurable at various lifecycle stages. While not a native feature like Spacelift, Custom Flows provide flexibility for integrating tools like Ansible, Chef, or Puppet into deployment pipelines.

11. Module management and testing

Module management reflects different priorities around testing versus versioning.

Spacelift offers automated module testing with ephemeral environments, validating modules before release and catching breaking changes before they impact downstream stacks. Module testing receives first-class workflow treatment, enabling teams to run test suites against modules before publishing.

Scalr focuses more heavily on module distribution and version control. It offers global and environment-level private registries, with fine-grained controls over which module versions are available where. Integrations like Dependabot help surface updates and create pull requests to keep modules current. 

However, Scalr does not provide a dedicated module testing infrastructure, so teams typically rely on existing workspace workflows to validate modules before release.

12. Observability and reporting

What a platform helps you observe reflects its operational priorities.

Scalr focuses on platform-level hygiene and usage reporting. It surfaces metrics such as workspace size, long-running plans, module usage, and Terraform version adoption, which can help platform teams spot outliers and standardize IaC practices. These capabilities are centered on maintaining consistency across Terraform environments, with optional integrations for external observability and event-driven workflows.

Spacelift provides comprehensive resource visualization across all managed infrastructure, including relationship views showing resources, their connections, and managing stacks. The platform emphasizes understanding infrastructure state and dependencies across all IaC tools.

Scalr delivers an optimized Terraform and OpenTofu experience with GitOps workflows and native CLI integration. Spacelift takes a different approach, layering natural-language provisioning (Intent) alongside traditional IaC workflows. 

This dual-path model enables platform teams to support rapid experimentation and rigorously reviewed production code from a single platform.

Two paths, one platform

Path 1: Traditional IaC for production workloads

Production infrastructure and critical workloads demand the traditional IaC approach. Full code control, rigorous testing, comprehensive policies, and complete auditability remain essential. Terraform, OpenTofu, Pulumi, and other IaC tools excel here, delivering the precision and control that critical infrastructure requires.

Use traditional IaC (Path 1) for:

• Production infrastructure
• Long-lived environments
• Infrastructure requiring complex dependencies
• Resources needing version-controlled configuration

Path 2: Spacelift Intent for rapid provisioning

Spacelift Intent removes infrastructure code requirements for non-production workloads, rapid prototyping, testing environments, and experimental infrastructure. Developers describe needs in natural language. Intent provisions infrastructure while maintaining complete governance and policy enforcement.

Use Spacelift Intent (Path 2) for:

• Development and testing environments
• Rapid prototyping
• Proof-of-concept infrastructure
• Short-lived experimental workloads
• Ad-hoc infrastructure needs

Better together

The power emerges from using both paths simultaneously, not choosing between them. Your organization maintains production infrastructure rigor while dramatically accelerating everything else. Same team, same policies, same platform, with workflows optimized for different needs.

Consider the developer workflow: Use Intent to create a test environment in minutes, validate changes, then transition to traditional IaC workflows for promoting changes to production with full code review and testing. Platform teams configure policies once, applying them to both paths. The governance framework stays consistent even as provisioning methods change.

How Intent transforms infrastructure workflows

A typical scenario illustrates the difference. A developer needs a test environment for validating a new feature. Traditional IaC platforms require writing code, submitting for review, running through deployment pipelines, and waiting for approval.

With Spacelift Intent:

1. Developer describes: “I need a PostgreSQL database and an EC2 instance for testing the new authentication service”
2. Intent provisions resources with appropriate policies applied
3. Developer has their environment in minutes

The critical insight: not all infrastructure demands the same rigor. Your production database cluster needs meticulous code review. Your Tuesday afternoon test environment doesn’t.

No code, full governance

Intent doesn’t trade governance for speed. Every resource provisioned through Intent flows through your organization’s policy framework. Policies prohibiting public S3 buckets prevent Intent from creating them. Policies requiring specific tagging ensure Intent applies those tags automatically.

Real-world impact

The dual-path model transforms platform team and developer interactions:

• Development teams provision test, dev, and staging infrastructure without platform team dependencies or HCL learning requirements
• Platform teams concentrate expertise on production-critical infrastructure while enabling self-service for everything else
• Experimentation accelerates because prototype environments don’t require pull requests
• Both paths enforce identical policies, maintaining consistency across all infrastructure

The competitive advantage

Scalr and other platforms continue improving traditional IaC management capabilities, competing in a single dimension. Spacelift’s dual-path deployment model establishes an entirely new infrastructure provisioning category.

Organizations using Spacelift gain substantial velocity advantages: maintaining identical rigor and control for production infrastructure while dramatically accelerating everything else. The platform recognizes that speed and governance aren’t opposites but complementary when applied appropriately.

This isn’t about replacing IaC. It’s about recognizing that different infrastructure workloads have different requirements and providing appropriate tooling for each, all within the same unified platform with consistent governance.

Spacelift and Scalr both represent capable platforms for infrastructure as code management, each bringing strengths in different areas. Scalr suits organizations standardizing on Terraform and OpenTofu which prioritize transparent pricing, GitOps workflows, and reduced platform engineer toil. Spacelift delivers broader multi-tool orchestration, greater deployment flexibility, and revolutionary natural language provisioning.

The meaningful distinction appears when considering how infrastructure teams actually operate. Most platforms, including Scalr, optimize for a single workflow: write code, review code, and deploy code. This works well for production infrastructure but creates friction elsewhere.

Spacelift’s dual-path deployment model with Intent recognizes that infrastructure provisioning isn’t one-size-fits-all. By delivering both rigorous IaC workflows for production and natural language provisioning for everything else, working together on a unified platform, Spacelift eliminates the false choice between speed and governance. You get both, applied appropriately based on workload requirements.

For teams seeking to accelerate infrastructure delivery without sacrificing control, the question transcends feature comparison. It’s about whether you want a platform optimized for Terraform/OpenTofu workflows or a platform that adapts to how your team actually works across multiple IaC tools and different provisioning needs.

Ready to see how Spacelift’s dual-path deployment model can transform your infrastructure workflows? Start with our free tier to experience both traditional IaC orchestration and Intent firsthand, or book a demo with our engineering team to see it in action.