11 Platform Engineering Tools to Help You Deploy Faster

Platform engineering tools turn infrastructure and delivery workflows into repeatable, self-service "products" that developers can use without needing to become part-time site reliability engineers. These platform engineering tools address the core platform engineering challenges of scaling software delivery.

This guide describes and compares 11 popular platform engineering tools, offers best practices for evaluating them, and shows how each tool fits into a modern platform engineering stack. Understanding what is platform engineering helps teams select the right platform engineer tools for their needs.

Key takeaways

• Platform engineering tools are the systems and workflows that help you build internal developer platforms. These platforms provide developers with standardized tools, workflows, and infrastructure capabilities so they can ship faster with less friction. The best platform engineering tools integrate seamlessly into existing workflows.

• Platform engineering differs from DevOps by focusing on productizing platform capabilities. Outputs include the platform itself, plus automation and documentation of key operations workflows. They also include mechanisms for facilitating composability and infrastructure-as-code. Guardrails prevent misuse of platform features. The result is delivery that is repeatable at scale.

• The best platform engineering solutions connect a full platform engineering stack. This includes Infrastructure as Code (IaC), which defines infrastructure through version-controlled configuration files. It includes CI/CD, which automates building and testing code, plus deploying it. It includes GitOps, where Git repositories serve as the source of truth for infrastructure and application deployments. It also includes observability tools for monitoring system health, plus secrets management and policy layers for governance. These integrate into one coherent platform engineering architecture.

• Mirantis helps enterprises deploy faster with Mirantis k0rdent, a Kubernetes-native approach to standardization and multi-cluster control.

Platform engineering tools overview

If you're building or improving an internal developer platform, here is a practical starting point. These platform engineering tools cover the core layers most enterprises need. Platform engineering examples from leading organizations show how these tools combine into effective platform engineering solutions.

How these platform engineering tools fit together

Platform engineering is not a single-tool decision. Most enterprises adopt a platform engineering stack that covers infrastructure provisioning and lifecycle, standardized delivery workflows, governance, and security, with developer experience as the priority.

Below are common platform engineering stack patterns that deploy faster without sacrificing control. These platform engineering examples demonstrate how different tools combine to solve specific platform engineering challenges.

Pattern 1: Kubernetes-native IDP stack (most common for multi-cluster teams)

• Backstage or Port for portal and service catalog

• Mirantis k0rdent for cluster lifecycle management and standardization

• Terraform or Crossplane for infrastructure provisioning

• Argo CD for GitOps delivery and drift correction

• Prometheus for baseline monitoring

Pattern 2: GitHub-centered delivery stack (fast standardization)

• GitHub Actions for CI templates with gates and approvals

• Terraform for provisioning

• Argo CD for CD and environment reconciliation

• Backstage or Port for self-service actions and ownership

Pattern 3: Regulated enterprise stack (governance first)

• Spacelift for workflow governance and policy-as-code for IaC

• Terraform or OpenTofu as the provisioning layer

• Argo CD for drift detection and delivery audit trails

• Backstage or Port for standardized workflows and controls

• Mirantis k0rdent for multi-cluster policy and lifecycle standardization

Pattern 4: Abstraction-first orchestration stack (reduce cognitive load)

• Humanitec for app definitions and environment orchestration

• Terraform or Crossplane for resource execution

• Argo CD for delivery and reconciliation

• Backstage or Port as the UX layer

Regardless of which pattern you choose, the goal remains: make the safe, standardized path the easiest path.

What is platform engineering?

What is platform engineering? Platform engineering is the practice of building and operating reusable internal platform capabilities. These capabilities enable developers to self-serve infrastructure and delivery workflows without requiring manual intervention from operations teams.

It's easy to confuse platform engineering with DevOps. DevOps brought the culture shift that integrated development and operations teams. Platform engineering brings the product: a concrete, self-service system. The platform engineering team takes the tools and workflows that used to live in people's heads, adds guardrails, and turns them into an internal developer platform. The platform is documented and standardized, making it accessible to developers without requiring deep operational knowledge.

This scalability is why Gartner expects 80% of large software engineering organizations to establish platform engineering teams by 2026, up from 45% in 2022. Platform engineering demands increase as organizations scale, making these platform engineering solutions essential for enterprise success.

What platform engineering looks like in practice

Platform engineering manifests as repeatable workflows that make the correct way to ship software obvious and fast.

Before platform engineering

• Provisioning requires tickets and back-and-forth approvals

• Continuous integration and continuous delivery pipelines vary by team and break in different ways

• Staging environments drift from production environments

• Security controls are late-stage blockers

• Platform teams are flooded with one-off requests

After platform engineering

• Developers self-serve standardized environments in minutes

• Pipelines are reusable templates with consistent quality gates

• GitOps reconciles clusters to the desired state defined in Git

• Policies and secrets controls are automated and enforced

• Observability and ownership are built into every service by default

11 best platform engineering tools for enterprises

Enterprises don't adopt platform engineering because it's trendy. They adopt it because the status quo breaks down at scale. The best platform engineering tools address these platform engineering challenges systematically.

More clusters. More services. More security requirements. More developers. More environments. And somehow, also "do it faster."

The platform engineering tools below show up repeatedly because they solve real problems and help platform teams deliver standardization without becoming a bottleneck. These platform engineer tools form the foundation of effective platform engineering solutions. Each platform engineering tool addresses specific platform engineering challenges that teams face at scale.

1. Mirantis k0rdent

Mirantis k0rdent is built for platform teams who need to standardize how Kubernetes infrastructure is created and operated across environments. You can do this without building a custom control plane from scratch.

k0rdent is Kubernetes-native and designed around composability. You create repeatable templates for clusters and policies, along with operational configurations, then enforce them centrally across multiple cloud providers and on-premises estates.

Most large enterprises run dozens of Kubernetes clusters, not just one. Operational variance becomes the enemy of deployment speed.

What k0rdent offers

• Kubernetes-native multi-cluster lifecycle management

• Templates for repeatable cluster creation

• Centralized policy enforcement and governance

• Multi-cloud and on-prem support for consistent operations

• Built for scalable platform engineering architecture patterns and IDPs

When k0rdent fits

• You have multiple clusters across clouds or regions and want consistent cluster lifecycle operations

• Your platform team needs to standardize cluster templates and upgrades. Policies must be managed centrally.

• You want platform teams to manage Kubernetes at the estate level, not cluster by cluster

What to watch for

• Success depends on defining and maintaining templates as a product, not as a one-time configuration effort

• Governance needs a clear adoption strategy so teams do not bypass the standard path

2. Backstage

Backstage is a developer portal framework that became a standard for a reason. It gives developers one place to find services, documentation, owners, templates, and workflows.

Backstage is not the platform by itself. It is the UX layer that makes platform engineering self-service feel real. Teams use it for service catalogs and ownership standards. They also use it for documentation discovery and golden paths, which are standardized, well-documented workflows that represent the recommended way to accomplish common tasks.

Why teams choose Backstage

• Strong plugin ecosystem and broad adoption

• Excellent for service discovery and ownership, with built-in standards enforcement

• Strong foundation for golden paths and templates

Where it falls short

• Requires build and maintenance effort

• Needs strong integrations to deliver meaningful self-service

When Backstage makes sense

• You want maximum flexibility and customization

• You want a long-term portal framework with broad ecosystem support

• You have the internal capability to treat portal ownership as a product

3. Humanitec

Humanitec is a platform orchestration layer that helps teams define applications and map them consistently to environments. The value is abstraction: developers define what they need, and the platform translates it into infrastructure and delivery behind the scenes.

Humanitec is useful when your platform engineering architecture must support many teams and environments, along with diverse deployment patterns. It does this without every team reinventing deployment logic.

Humanitec strengths

• Strong environment orchestration model

• Reduces cognitive load for developers

• Accelerates IDP adoption through structured app definitions

Humanitec limitations

• Requires upfront standardization discipline

• Some organizations prefer open-source composition instead of vendor orchestration

When Humanitec works

• You need consistent application definitions across teams

• You want strong environment abstraction and orchestration

• You are standardizing deployment patterns across many product teams

4. Spacelift

Spacelift is a governance and automation layer for Infrastructure as Code workflows. Terraform and OpenTofu are powerful Infrastructure as Code tools, but Infrastructure as Code at scale needs governance. This includes required approvals, audit trails, drift detection, and policy enforcement.

Spacelift gives platform engineering teams control over how infrastructure changes are executed across environments and teams.

Spacelift advantages

• Strong policy-as-code and approvals for Infrastructure as Code

• Drift detection improves stability

• Scales across teams and environments

Spacelift challenges

• Requires integration into broader platform workflows to avoid becoming a silo

• Needs process alignment across security and platform teams, plus engineering

When Spacelift fits

• You need strong governance and auditability, with required approvals for IaC workflows

• Your organization runs Terraform at scale across many teams

• You have compliance requirements that require traceability and control

5. Crossplane

Crossplane lets you model infrastructure as Kubernetes APIs. You can manage cloud resources like databases or storage using the same Kubernetes tools and patterns you use for applications. The abstraction becomes clear once you've experienced creating resources across different cloud providers. The problem is inconsistent conventions.

Crossplane shines when teams request infrastructure through claims. The platform team controls how those claims map to real cloud resources using compositions.

Crossplane benefits

• Kubernetes-native infrastructure abstraction

• Strong composition model for reusable patterns

• Enables consistent provisioning across clouds

Crossplane drawbacks

• Learning curve for composition patterns

• Can cause YAML sprawl without governance and good templates

When Crossplane is right

• You want Kubernetes-native infrastructure composition

• You want to abstract infrastructure behind standardized interfaces

• Your platform team is comfortable building reusable compositions

6. Argo CD

Argo CD is one of the best examples of GitOps principles implemented cleanly. Git repositories serve as the source of truth for infrastructure and application deployments. Clusters sync to Git, and drift is detected and corrected automatically.

When combined with standardized templates and deployment conventions, Argo CD makes delivery repeatable and observable.

Argo CD highlights

• Drift detection and reconciliation improve reliability

• Great multi-cluster support

• Strong visibility into delivery state across environments

Argo CD considerations

• Requires GitOps discipline and repo hygiene

• Complexity grows without standards and structure

When Argo CD fits

• You want GitOps-based delivery with drift control

• You manage multiple Kubernetes clusters and want consistent deployment visibility

• You want a standard CD model that supports repeatable environments

7. Terraform

Terraform remains the backbone for infrastructure provisioning in many platform engineering stacks. It is widely adopted and supported by a massive ecosystem. It works across nearly every cloud and SaaS platform.

The biggest problem is not Terraform itself, but ungoverned Terraform. If every team builds their own modules and conventions, the platform fragments.

Terraform strengths

• Large ecosystem and enterprise adoption

• Strong repeatability and modularity

• Works across almost every environment

Terraform gaps

• Governance and drift detection require additional tools

• Module sprawl becomes technical debt without standards

When Terraform works

• You need cross-cloud provisioning and a large module ecosystem

• You want a mature tool with strong enterprise adoption

• Your platform team is building standardized modules and workflows

8. Port

Port is a developer portal platform that emphasizes workflows and scorecards, plus self-service actions. Scorecards are automated assessments that measure how well services meet standards like security or reliability requirements. If you want portal value quickly without building Backstage from scratch, Port accelerates adoption.

Port is useful for publishing standardized actions like provisioning environments, requesting access, generating scaffolding, and running compliance checks.

Port benefits

• Faster time-to-value than building a portal framework

• Strong workflow and self-service action model

• Scorecards support standards enforcement

Port trade-offs

• Less customizable than open source portal frameworks

• Requires integration discipline to avoid dashboard sprawl

When Port makes sense

• You want a portal quickly with built-in workflows

• You want scorecards to enforce standards and ownership

• You want less DIY maintenance than a framework approach

9. Jenkins

Jenkins is still deeply embedded in enterprise continuous integration environments, sometimes because teams love it, sometimes because it already exists. Replacing it is risky.

In platform engineering, Jenkins can still be useful. You need to standardize pipelines, centralize shared libraries, and reduce per-team pipeline snowflakes.

Jenkins advantages

• Highly flexible and widely understood

• Large plugin ecosystem

• Fits many enterprise workflows

Jenkins costs

• Maintenance overhead is significant

• Pipelines become fragile without standardization

When Jenkins is viable

• You have an established Jenkins investment

• You can enforce standardized pipeline templates

• You can centralize shared libraries and security controls

10. Prometheus

Prometheus is the default metrics foundation for the Kubernetes ecosystem. Platform engineering teams use it to standardize observability baselines across services. This includes metrics and dashboards, plus alerts.

Prometheus matters because observability often becomes the bottleneck at scale.

Prometheus capabilities

• Kubernetes-native metrics standard

• Strong ecosystem integrations

• Enables service level objectives and alerting, providing visibility into system health

Prometheus challenges

• Alerting becomes noise without discipline

• Scaling Prometheus requires architecture decisions

When Prometheus fits

• You need baseline metrics and alerting across Kubernetes environments

• You want Kubernetes-native observability foundations

• You want to build SLO-driven reliability practices on top

11. GitHub Actions

GitHub Actions is a practical answer to continuous integration standardization when GitHub is already the system of record. It is easy to publish reusable workflows and enforces consistent quality gates. It also reduces tool sprawl.

It will not replace everything, but it is a strong default pipeline layer for many platform engineering solutions.

GitHub Actions strengths

• Built into the developer workflow

• Easy to standardize workflow templates

• Large ecosystem of integrations

GitHub Actions limitations

• Enterprise governance may require additional controls

• Teams can create inconsistent workflows without standards

When GitHub Actions works

• GitHub is your center of development and policy

• You want standardized CI templates with consistent gating

• You want low friction workflow adoption across teams

Tools that are missing from most "top tools" lists (but you likely need)

Most enterprise platform engineering stacks also require tools for security and policy enforcement, secrets management, and software supply chain integrity. These become essential once you scale.

Policy enforcement

• OPA Gatekeeper or Kyverno - Use these to enforce guardrails at admission time, such as allowed container images and required metadata labels. They also restrict container privileges.

Secrets management

• HashiCorp Vault, External Secrets Operator - Standardize secret storage and rotation, and remove credentials from continuous integration pipelines and code repositories.

Progressive delivery

• Argo Rollouts, Flagger - Use canary and blue-green delivery patterns to reduce risk and speed rollback. Canary deployments gradually roll out changes to a small percentage of users first. Blue-green deployments maintain two identical production environments and switch traffic between them. Both patterns allow quick rollback if problems are detected.

Supply chain security

• SBOM tooling, artifact signing, provenance enforcement (Cosign, SLSA patterns) - Make artifact integrity part of the standard delivery workflow. SBOM (software bill of materials) is a list of all components and dependencies in a software artifact. Artifact signing uses cryptographic signatures to verify that artifacts haven't been tampered with. Provenance enforcement verifies the origin and build process of artifacts.

Telemetry standardization

• OpenTelemetry - Standardize traces and logs, plus metrics, so teams can observe systems consistently across tools and vendors.

These layers are essential for maintaining control as delivery velocity increases.

How to evaluate platform engineering tools for faster delivery

Teams often pick tools based on popularity, which can lead to adoption problems.

In modern application development, tools only matter if they integrate into a coherent workflow and reduce friction for the people shipping software. Use these criteria to choose platform engineering tools strategically. Modern application development requires platform engineering tools that work together seamlessly.

Alignment with developer workflows

If developers have to context switch constantly, your platform creates friction. A good platform tool supports Git-first workflows, pull request-driven change management, portal and command-line interface self-service, and standard templates for repeatability.

Questions to ask:

• Does this tool reduce steps or add steps?

• Can developers self-serve without filing tickets?

• How quickly can a new engineer go from zero to deploy?

Integration and automation capabilities

Platform engineering requires integration. Tools that do not integrate cleanly become liabilities. Look for strong API and webhook support, policy and identity integrations like role-based access control and single sign-on, plus audit logs. Consider automation primitives like pipelines and workflows, along with event-driven triggers. You'll know you're on the right track when you see fewer manual handoffs, fewer bespoke scripts, and more reusable golden paths.

Scalability and security considerations

At enterprise scale, security must be built into every layer of the platform. Evaluate multi-tenancy, isolation models, role-based access control, auditability, and access controls. Consider policy-as-code enforcement and performance at scale across multiple clusters, regions, and teams.

Platforms that cannot be secured and operated reliably will not scale effectively.

Open source vs proprietary tools

Most platform engineering teams end up with a mix.

Open source provides flexibility and reduces lock-in risk. Proprietary tools can accelerate adoption and reduce DIY overhead. Many platform engineering teams use open source tools for foundational layers while adding proprietary platform engineering solutions where governance and UX speed matter most.

Decide based on internal skills, time-to-value requirements, and tolerance for maintenance.

Consider this approach:

• Open source for foundational layers

• Paid tooling where governance and UX speed matter most

ROI and maintenance overhead

Platform engineering should make delivery faster and safer, though every tool has an operational cost.

Track ROI using DORA-style outcomes. DORA (DevOps Research and Assessment) metrics measure software delivery performance through four key indicators:

• Lead time for changes

• Deployment frequency

• Change failure rate

• Time to restore service

Platforms that do not improve these metrics are not delivering value.

Platform engineering tool evaluation checklist (what to ask in demos)

Developer experience

• Can a new engineer go from repo to environment to deploy in under one hour?

• Does it support both portal workflows and CLI or API automation?

Standardization

• Can you publish templates and golden paths as versioned assets?

• Can you enforce them without blocking teams unnecessarily?

Governance and auditability

• Does it support approvals and audit trails? Does it support role-based access control and single sign-on?

• How does it handle drift detection and enforcement?

Multi-tenancy

• Can multiple teams share the platform safely?

• What is the isolation model across teams and environments?

Security and compliance

• How do policy-as-code and secrets management integrate? How does identity fit in?

• Can you prove compliance automatically, not via tickets?

Operational maturity

• What breaks at 50+ clusters or 500+ services?

• What does day-2 operations look like, including upgrades and incident response?

Why platform engineering architecture matters for deployment speed

Platform engineering architecture determines whether delivery scales effectively or becomes unmanageable. When tools are stitched together without a clear design, you get inconsistent pipelines, environments, security, and outcomes. Effective platform engineering architecture requires careful planning and integration of platform engineering tools.

A well-designed platform standardizes interfaces, such as how developers request environments and deploy services, and how teams adopt guardrails, while keeping underlying implementation flexible. This lets the platform team improve the system over time without breaking every team's workflow.

Benefits of a well-designed platform engineering architecture

Faster feedback loops. Well-designed platforms connect continuous integration, testing, delivery, and observability, giving developers fast feedback. Shorter feedback loops reduce late-stage surprises and cut release cycle time.

Improved developer productivity. Platforms reduce cognitive load through golden paths, templates, and self-service capabilities. Developers spend less time debugging environment problems and more time shipping product value.

Consistent environments for testing. Standardized environments eliminate configuration drift and "works on my machine" failures. Consistency improves test reliability and reduces rollout risk across development, staging, and production environments.

Reduced risk of human error. Automated workflows and policy guardrails reduce manual steps and misconfigurations. GitOps reconciliation and policy-as-code enforcement improve auditability and reduce drift.

Built-in governance without bottlenecks. The best platforms do not rely on late-stage approvals. They embed governance through policy-as-code, secrets management, and standardized identity patterns. This approach makes security and speed complementary rather than competing priorities.

Key components of a modern platform engineering stack

A modern platform engineering stack is modular, with each layer solving a specific problem. The platform engineering team's job is to integrate those layers into one coherent system. Understanding platform engineering architecture helps teams build effective platform engineering stacks.

Infrastructure as Code (IaC) and automation

Infrastructure as Code is the foundation, managing infrastructure through version-controlled configuration files. Without standard provisioning, everything else becomes fragile.

Use Infrastructure as Code to standardize environments, reduce drift, and enforce compliance, making infrastructure repeatable.

What good looks like:

• Consistent naming and tagging, plus resource boundaries

• Versioned modules and templates

• Automated approvals and policy checks

• Drift detection and remediation workflows

Continuous integration and continuous delivery (CI/CD)

Continuous integration and continuous delivery help you turn code into production outcomes predictably. Use CI/CD to standardize quality gates and testing, automate security scanning, and make deployments routine.

What to standardize:

• Shared pipeline templates and reusable workflows

• Artifact promotion strategies

• Progressive delivery strategies like canary or blue-green deployments where appropriate

• Rollback and recovery patterns

Developer portals and self-service interfaces

Developer portals provide the primary interface to the platform. They reduce cognitive load by making workflows discoverable and documented. They also make workflows repeatable.

Portals should enable service discovery and ownership standards. They should also enable golden paths and templates, plus self-service actions with built-in guardrails.

The goal is fewer tickets, faster onboarding, and fewer "who owns this?" moments.

Observability and monitoring

Observability is the ability to understand what's happening inside a system through metrics and logs, plus traces.

Use monitoring to detect incidents early, measure platform impact on delivery, and identify bottlenecks in continuous integration, continuous delivery, and runtime performance. Kubernetes monitoring provides the foundation for understanding platform performance across clusters and services.

Standardize:

• Telemetry conventions for logs, traces, and metrics

• SLO reporting (service level objectives, which are measurable reliability targets) and error budgets (acceptable amount of unreliability before action is required)

• Dashboards per service

• Alert thresholds and routing rules

Secrets and policy management

Security must be integrated from the start, not added later. Use secrets management and policy enforcement to prevent credential leaks, enforce least privilege, and reduce risk from manual configuration changes. Effective Kubernetes secrets management is essential for secure platform engineering architecture.

Standardize:

• Secret storage and rotation

• Policy-as-code guardrails (automated governance rules defined as code)

• Enforced runtime restrictions for privileged workloads

• Audit trails and access visibility

Environment and configuration management

Many deployment problems stem from environment issues.

Standardize:

• Configuration patterns across development, staging, and production environments

• Environment templates for development, staging, and production environments, plus naming conventions

• Deployment boundaries and resource quotas

• Ephemeral environments (temporary environments created on-demand for previews and testing)

This reduces surprises, inconsistent bugs, and rollback scenarios caused by drift.

Progressive delivery and rollback automation

Fast delivery requires shipping safely, not just frequently. Progressive delivery uses gradual rollout strategies to reduce risk.

Standardize:

• Automated health checks (verifying services are working correctly) and promotion gates (quality checks before advancing to the next stage)

• Canary strategies (gradually rolling out to a small percentage of users first) and blue-green strategies (maintaining two identical environments and switching traffic between them)

• Rollback workflows that are automated and reliable

This reduces high-risk releases and enables faster recovery from failures.

Software supply chain security

Every enterprise platform eventually runs into supply chain requirements. Software supply chain security involves verifying the integrity and origin of software components and dependencies.

Standardize:

• Dependency scanning and enforcement in pipelines

• SBOM (software bill of materials, a list of all components and dependencies) generation and storage

• Provenance policies (rules that verify the origin and build process) for builds and dependencies

• Signed artifacts (cryptographically signed software packages to verify they haven't been tampered with) and verification at deploy time

This enables faster security reviews, reduces production surprises, and simplifies compliance reporting.

Best practices for implementing platform engineering

Platform engineering succeeds when managed as a product, starting with developers as the primary users, delivering value incrementally, and measuring results.

What actually helps:

1. Start with developer experience as the core metric

If developers do not adopt the platform, it does not matter how elegant it is. Measure time to first deploy, onboarding time, self-service adoption, and developer satisfaction feedback.

Practical steps:

• Interview developers regularly

• Publish clear golden paths (standardized, well-documented workflows)

• Treat UX and docs as part of the platform

2. Standardize and automate repetitive platform tasks

Start with what slows teams down every day. Automate environment provisioning, pipeline scaffolding, compliance checks, and standard dashboards. Standardize templates instead of tribal knowledge, workflows instead of one-off scripts, and repeatable processes instead of free-for-all variation.

3. Prioritize observability and continuous feedback

Your platform needs feedback loops too. Track DORA outcomes, service level objectives, incident trends, and adoption metrics. Build in platform usage telemetry and actionable alerting, and establish continuous improvement cycles.

4. Foster cross-functional collaboration in platform workflows

Platform engineering is cross-functional. If security and ops are not aligned, you will have friction and workarounds. Align on policy-as-code standards, shared ownership models, escalation paths, and incident processes. Design the platform so the correct approach is the easiest approach.

5. Use data to measure platform tool ROI

ROI is not "we shipped a platform." ROI is a measurable improvement. Track ticket reduction, toil hours saved, deployment frequency improvements, and reduction in change-related incidents. Without measurable improvements, platform investments are difficult to justify.

Mirantis platform engineering solutions accelerate deployments

Platform engineering focuses on making delivery repeatable at scale.

Mirantis k0rdent provides a complete Kubernetes-native platform creation and lifecycle automation system. It combines composable components, including many of the platform engineering tools covered in this article, into purpose-built Platform-as-a-Service (PaaS) environments on Kubernetes. You assemble templates to define platform capabilities, then operate these platforms consistently at massive scale. This makes k0rdent one of the most comprehensive platform engineering solutions available.

k0rdent integrates with CI/CD systems and automation tools like Jenkins to create an end-to-end, template-oriented workflow for platform definition, instantiation, and operations. This integration connects platform provisioning to application delivery pipelines, making the entire stack repeatable and manageable.

The platform uses ClusterAPI to maintain consistency across any cloud provider, on-premises infrastructure, or edge deployments. This approach eliminates vendor lock-in and enables workload portability. You can move workloads between environments without rewriting platform definitions or changing operational procedures.

k0rdent extends these capabilities to manage specialized infrastructure requirements, including GPU resources and other specialized hardware needed for AI workloads and Neocloud deployments. The same template-based approach that standardizes standard Kubernetes platforms applies to these specialized environments, making AI infrastructure solutions as repeatable and manageable as conventional application platforms.

Key capabilities Mirantis brings to platform engineering teams

• Complete Kubernetes-native platform creation and lifecycle automation

• Composable templates that integrate with existing platform engineering tools

• End-to-end integration with CI/CD systems for template-driven workflows

• ClusterAPI-based consistency across clouds, on-premises, and edge deployments

• Support for specialized infrastructure including GPUs and AI hosting requirements

• Workload portability without vendor lock-in