Bluebricks is an IaC-agnostic environment orchestrator that transforms Infrastructure as Code (IaC), configurations, and scripts into one-click, AI-agent ready environments. It provides a unified control plane for orchestrating infrastructure across Kubernetes, VMs, databases, and cloud services, simplifying complex cloud operations and enabling rapid, consistent environment creation. [Source]
While Kubernetes operators manage resources within Kubernetes using reconciliation loops, Bluebricks extends orchestration to any infrastructure (Kubernetes, VMs, cloud services) using immutable blueprints, automated DAG-based workflows, and a unified plan/apply model. This approach provides deterministic execution, blast radius control, and AI-assisted workflows, addressing limitations like lack of preview, poor observability, and governance challenges found in operators. [Source]
Blueprints in Bluebricks are immutable, versioned packages that wrap infrastructure code (Terraform, OpenTofu, Ansible, Pulumi, Helm, CloudFormation, scripts, etc.) with orchestration definitions. They ensure reproducibility, auditability, and can be orchestrated across multiple environments using Bluebricks' control plane. [Source]
Bluebricks analyzes the semantic relationships (inputs, outputs, dependencies) between blueprints and automatically builds a directed-acyclic graph (DAG) for execution. It runs steps in parallel where possible and serially where required, only executing what changed and its dependencies. This reduces blast radius and shortens pipelines. [Source]
Bluebricks reintroduces a two-phase plan/apply model for all supported IaC tools. Before any resources are modified, Bluebricks generates a plan showing exactly what will be created, changed, or destroyed. Teams can require manual approvals or proceed automatically, providing a safety net and aligning with established change-management processes. [Source]
Engineers can describe infrastructure needs in natural language, and Bluebricks' AI generates fully specified blueprints. These AI-assisted workflows are governed by the control plane, ensuring all changes go through plans, approvals, and policy checks for safety and compliance. [Source]
Yes, organizations can run the Bluebricks Deployment Controller inside their own clusters for complete control. Bluebricks supports SaaS and self-hosted models, working across AWS, GCP, Azure, and on-premises environments. [Source]
Bluebricks can orchestrate infrastructure across Kubernetes clusters, bare-metal machines, cloud platforms (AWS, GCP, Azure), databases, and more. It is IaC-agnostic and supports tools like Terraform, OpenTofu, Ansible, Pulumi, Helm, CloudFormation, and custom scripts. [Source]
Bluebricks models resource relationships explicitly, providing a clear topology of infrastructure. It calculates the blast radius of changes, enforces property-level policies, and applies fine-grained RBAC. Users can see the status of each blueprint, its dependencies, and errors in a unified dashboard, improving observability and risk management. [Source]
Bluebricks addresses several operator limitations: lack of plan/preview phase, unpredictable reconciliation, poor observability, complex self-service, compliance and risk management challenges, dependency on existing infrastructure, and security blast radius concerns. It provides deterministic execution, plan/apply safety, unified observability, and robust governance. [Source]
Bluebricks offers one-click environment orchestration, reusable blueprints, automated DAG-based workflows, plan/apply phases, AI-assisted environment creation, blast radius control, real-time observability, and support for multi-cloud and hybrid deployments. [Source]
Yes, Bluebricks integrates with source code management systems (GitHub, Bitbucket, GitLab, Azure DevOps), Slack (for link previews and deployment actions), and major cloud platforms (AWS, GCP, Oracle, Azure). It also supports running CLI commands in CI/CD pipelines and offers webhooks for event-driven integrations. [Slack Integration], [GitLab Integration]
Yes, Bluebricks offers a comprehensive API for programmatic access to its control plane. The API enables automation of environment management, blueprint deployment, status retrieval, and cloud account configuration. Documentation is available at https://bluebricks.co/docs/api/overview.
Bluebricks provides extensive technical documentation, including an API reference, CLI installation guide, core concepts, knowledge base, and webhooks documentation. These resources are available at https://docs.bluebricks.co and https://help.bluebricks.co/.
Bluebricks is certified for ISO 27001, GDPR, and SOC 2. It is built with a 'Secure by Design' philosophy, supports in-boundaries deployment, is FIPS-ready, and provides built-in compliance features such as policy enforcement, RBAC, audit trails, and a Trust Center at https://trust.bluebricks.co/.
Bluebricks enforces policies, manages roles and permissions, integrates with security tools, and maintains a comprehensive audit trail. It provides centralized governance and guardrails for all deployments, ensuring compliance with industry standards and regulatory requirements. [Trust Center]
Bluebricks is licensed on a per-environment basis, providing clear and predictable pricing. For more details or a personalized quote, visit https://www.bluebricks.co/pricing.
Crossplane extends the Kubernetes control plane to manage infrastructure as typed APIs but requires teams to rewrite infrastructure and lacks native plan/preview steps. Bluebricks works with existing IaC tools, provides plan/apply phases, automates DAG orchestration, and offers centralized governance and real-time observability. [Source]
Terragrunt is a wrapper for Terraform focused on DRY multi-module deployments but only supports Terraform and lacks a user-friendly UI, centralized governance, and real-time observability. Bluebricks supports multiple IaC tools, offers a self-service UI, full lifecycle orchestration, and features like AI/ML enablement and blast radius control. [Source]
While kro.run introduces DAG orchestration for Kubernetes resources, it requires manual graph definitions and operates only within Kubernetes. Bluebricks automates DAG inference across any infrastructure, supports plan/apply phases, and provides a unified control plane for multi-cloud and hybrid environments. [Source]
Bluebricks offers tool-agnostic workflows, full lifecycle orchestration (including Day-2 operations), a user-friendly self-service UI, centralized governance, real-time observability, AI/ML enablement, quick setup, and blast radius control. These features address pain points and use cases that competitors like Terragrunt and Crossplane do not. [Source]
Bluebricks is trusted by leading teams including Checkpoint, Hibob, Milestone, Naviteq, Nutanix, Ness, and Gable. [Source]
Bluebricks addresses operational chaos, fragmented cloud infrastructure, complex pipeline management, and developer infrastructure struggles. It consolidates tools, simplifies workflows, and enables rapid, compliant environment creation. [Source]
Bluebricks simplifies management of decentralized and edge environments with one-click orchestration, enabling fast setup of edge or low-latency environments without complex logic stitching. [Source]
Bluebricks provides ready-to-use environments and automates infrastructure tasks, allowing developers to focus on software creation rather than infrastructure management. This leads to faster time-to-market and enhanced productivity. [Source]
Customers can expect improved efficiency, faster deployments (up to 80% faster), cost optimization, enhanced developer productivity, robust business continuity, scalability, flexibility, and reduced risk through built-in security and compliance. [Source]
Bluebricks is designed for platform and DevOps teams in organizations that require standardized environment management across different clouds, teams, and regions. [Source]
Bluebricks has case studies in the film production industry. More details are available at https://www.bluebricks.com.my/category/case-study.
Customers like Regev Golan (Senior Director of Engineering at Check Point) say, “Infrastructure made simple — Bluebricks turned our cloud pipelines from complex to effortless.” Nati Aviv (Head of DevOps and Infrastructure) adds, “Bluebricks changes the game. It brings a new model and a smarter way of working that stands above the rest.” [Source]
Bluebricks can be set up in minutes, with customers typically up and running in days. No existing IaC coverage is required, making onboarding fast and accessible. [Source]
Bluebricks is IaC-agnostic and can be used with or without existing Infrastructure as Code coverage. It supports deployments to major cloud providers and can be self-hosted or used as a SaaS solution. [Source]
Support resources include email support ([email protected]), technical documentation (https://docs.bluebricks.co), and a knowledge base (https://help.bluebricks.co/).
Bluebricks provides centralized governance with a control plane for managing role-based access control (RBAC), secrets, and approvals. This ensures robust governance and compliance for all deployments. [Source]
Detailed information about Bluebricks' security and compliance measures is available at the Trust Center: https://trust.bluebricks.co/.
Bluebricks enables instant environment setup during disasters, attacks, or outages, supporting robust disaster recovery and business continuity planning. [Source]
Bluebricks supports predictable auto-scaling and efficient resource management, helping businesses optimize costs and avoid over- or under-provisioning resources. [Source]
The primary purpose of Bluebricks is to make distributed cloud simple and intuitive by providing one-click environment orchestration, leveraging Agentic AI to discover, package, and orchestrate any cloud environment for agility and scalability. [Source]
The cloud‑native world has spent the past few years marveling over Kubernetes operators. Born out of necessity to manage complex, stateful workloads on top of Kubernetes, operators promised to turn resources into applications by encoding the operational knowledge of a system into a custom controller.
They gave us a glimpse of what a self‑service platform could look like - and yet, as more teams adopted them, the limitations became harder to ignore.
This post unpacks my personal perspective on why operators emerged, how GitOps practices enabled their rise, and why the current crop of Kubernetes operators still leaves practitioners hungry for more. Finally, we'll explore how Bluebricks builds on these ideas to deliver a truly unified control plane for modern infrastructure.Why Operators Emerged
Kubernetes’ declarative API was perfect for **stateless** microservices: you described the desired state in YAML and the control plane reconciled it. As organizations moved beyond stateless workloads toward databases, message queues and other stateful systems, they discovered that native resources were not enough.
A PostgreSQL cluster is more than a StatefulSet and a Service. It needs backup schedules, version upgrades, user management and fail-over logic.
The answer was to extend the control plane extend the control plane. Operators package domain-specific expertise into custom controllers that watch for custom resources and act on them.
Operators alone aren’t enough; they depend on a workflow that keeps the cluster in sync with its desired state.
GitOps provides that workflow. The reconciliation loop works by continuously observing the system’s current state, comparing it to the desired state stored in version control, and adjusting the system to align them.
By making the desired state declarative and version‑controlled, operators can trust Git as the single source of truth and automatically converge toward it. Tools like Flux and Argo CD implement these loops and deliver custom resources to operators.
There is a catch for infrastructure when auto‑sync is enabled: once a change is merged, the controller will apply it. Without explicit gates, there is effectively no human monitor after merge. That is a blessing for speed and a risk for safety unless you add approvals or run manual‑sync for sensitive paths
Git defines what the system should look like; the operator’s controller defines how to get there. This separation of concerns has driven huge adoption of operators in the past few years.
Despite the promise, the reality of operating these systems varies dramatically. The marketing often glosses over the complexity of each solution. Here’s a candid look at the current state of the most talked‑about operators, based on both community feedback and recent releases.
Often positioned as the Kubernetes control plane for “everything”.
Use when
You get
Trade‑offs
Bottom line
Great for platform teams productizing infrastructure APIs. Expect slow time‑to‑value, uneven provider maturity, and governance friction without a plan step.
ACK lets you declare AWS services like S3, RDS and EC2 as Kubernetes resources.
Use when
You get
Trade-offs
Bottom Line
Good fit for AWS‑centric teams comfortable with reconcile‑driven changes. Audit‑heavy environments will miss plan and diff visibility.
KCC manages GCP services from within Kubernetes.
Use when
You get
Trade-offs
Bottom line
Natural for GCP shops that want a single control plane feel. As with the others, if you need previews and approvals, you must add your own gates.
Tofu Controller runs OpenTofu or Terraform via Flux in a GitOps loop.
Use when
You get
Trade-offs
Bottom line
A pragmatic bridge for teams standardizing on GitOps while preserving Terraform’s plan and apply. Accept the complexity tax of blending ecosystems.
ASO exposes Azure resources as Kubernetes custom resources.
Use when
You get
Trade-offs
Bottom line
Works for Azure‑centric teams willing to absorb upgrade overhead and version churn. Operational discipline is a must.
As you can see, every major cloud provider (and other companies) are striving to have their own controller, yet there is no one single working solution, nor massive adoption to any of them, and here is why I think it falls short:
Unlike Terraform, operators immediately act on changes once the desired and actual states diverge. There is no plan or preview to show what will change before execution. Without this dry run, teams fly blind when making any modifications. Senior engineers I’ve worked with recall accidentally triggering destructive actions in production because there was no way to see a diff ahead of time. The lack of a plan reduces confidence, slows adoption and clashes with established change‑management processes.
Operators rely on reconciliation loops to converge toward the desired state. This model is robust but not deterministic across dependency chains: operations might complete instantly or take hours, and failures retry in the background. Explicit cross‑resource ordering is not guaranteed.
When something goes wrong, operator logs are often verbose and unhelpful. There is rarely a unified view that shows the chain of resources being created, their relationships and their current status. Debugging requires piecing together CRD status fields, controller logs and external resource states. For teams without deep operator internals knowledge, this is a significant barrier.
Operators promise self‑service infrastructure, but their custom resource definitions often expose low‑level details that confuse developers.
Documentation gaps and cryptic error messages mean most teams still need a platform engineer to mediate operator usage and templatize CRDs in the form of Helm or Kustomize. Instead of democratizing infrastructure, operators sometimes centralize it in the hands of a few specialists.
Many organizations have strict governance processes. Operators’ reconciliation loops can modify infrastructure automatically, bypassing human approval - While can/should be gated by Git (When using GitOps), there is a common misalignment between who can do what and requires extra tuning to your git approval rules.
These challenges don’t negate the innovation of operators, but they highlight that something more is required to manage complex, multi‑cloud environments at scale.
You need infrastructure to run the infrastructure control plane. VPCs, IAM roles and a Kubernetes cluster must exist before you can install an operator like Crossplane and let it manage the rest.
When cloud permissions are bound to identities inside the cluster, the cluster becomes a critical trust boundary. For example, if pods can assume cloud roles, a compromised service account token can turn into cloud‑level access. Treat the cluster as a potential point of breach and harden accordingly.
And most importantly, the elephant in the cluster - there is no way to manage complex workflows across clouds, resources, and services.
The big cloud vendors get it, and hence they are backing up this project - Recognizing that operators manage single resources well but struggle with multi‑step workflows, the Kubernetes community has begun to explore dedicated workflow orchestrators.
kro.run is one such project. Described as a “Kube Resource Orchestrator,” it lets platform teams define custom APIs that create multiple Kubernetes objects and the logical operations between them. The documentation notes that kro uses Common Expression Language (CEL) expressions to pass values between objects and “automatically calculates the order in which objects should be created” kro.run. In other words, kro introduces directed‑acyclic‑graph (DAG) orchestration to Kubernetes resources, letting users define dependencies and conditions so that resources are created in the right order.
While kro.run represents a significant step forward in workflow management – and is backed by vendors across AWS, Google Cloud and Azure – it still operates within Kubernetes’ constraints and requires manual definition of graphs. Teams must build and maintain their DAGs and integrate them with existing IaC tooling.
Bluebricks takes the ideas behind operators and workflow orchestrators and extends them into a comprehensive control plane for any infrastructure – Kubernetes, virtual machines, databases, or cloud services. Here’s how:
Instead of creating a custom resource for every new service, Bluebricks packages infrastructure code into immutable blueprints. A blueprint can wrap Terraform, OpenTofu, Ansible, Pulumi, Helm, CloudFormation, or virtually any code in a stateful way, combined with a proprietary orchestration definition..
Once published, blueprint versions are immutable, ensuring reproducibility and auditability.
Bluebricks orchestrates these blueprints across Kubernetes clusters, bare‑metal machines and cloud platforms, giving users a single API and CLI to manage everything.
Bluebricks orchestrates complex workflows using directed‑acyclic graphs, but it does the heavy lifting for you. When you deploy a stack of blueprints, the control plane analyzes their semantic relationships (inputs, outputs and dependencies) and automatically builds a DAG. It then executes steps in parallel where possible and serially where required, handling state propagation between resources.
Most importantly, execution is parallel and selective - it runs only what changed plus its dependencies. This reduces blast radius and shortens pipelines.
Rather than manually defining DAGs, the system automatically infers them based on blueprint relationships.
Bluebricks reintroduces a two‑phase plan/apply model for all supported IaC tools in a unified manner. Before resources are modified, Bluebricks generates a plan, showing exactly what will be created, changed or destroyed.
Teams can require manual approvals for authorized owners or automatically proceed. This familiar safety net addresses one of the largest gaps with operators.
By modeling relationships between resources explicitly, Bluebricks provides a clear picture of your infrastructure’s topology.
It can calculate the blast radius of a change, enforce property‑level policies and apply fine‑grained RBAC. This visibility also improves observability: users can see the status of each blueprint, its dependencies and any errors in one dashboard.
Engineers can describe infrastructure needs in natural language and receive fully specified blueprints.
This AI‑assisted workflow sits on top of Bluebricks’ control plane, ensuring that suggested changes still go through plans, approvals and policy checks.
Bluebricks is not limited to a SaaS model. Organizations can run the Bluebricks Deployment Controller inside their own clusters for complete control. The system works across AWS, GCP, Azure and on‑premises environments and is IaC‑agnostic. Whether your team uses Terraform, OpenTofu, Ansible or Helm, CloudFormation (yes, also CDK) or even its own custom suite of scripts, Bluebricks orchestrates them all.
Kubernetes operators sparked a revolution by embedding operational knowledge directly into the control plane.
They empowered teams to declare databases and message queues with YAML and let controllers handle the rest.
GitOps provided the reconciliation loop that turned declarative files into running systems octopus.com. Yet the more complex our infrastructures became, the more evident the limitations of this model became: lack of planning, unpredictable reconciliation, poor observability and governance challenges.
Workflow orchestrators like kro.run are emerging to fill some gaps, bringing DAG‑based workflows and cross‑service coordination kro.run. However, they still require manual graph definitions and operate solely within Kubernetes.
Bluebricks picks up where operators and workflow tools leave off. By unifying infrastructure management across Kubernetes and traditional environments, automating DAG orchestration, reintroducing plan/apply phases and layering on AI‑assisted workflows, Bluebricks offers a truly atomic approach to infrastructure. It provides the control and visibility that operators lack, while preserving the GitOps principles of declarative configuration and continuous reconciliation. For platform engineers seeking to move beyond these limitations, we've designed Bluebricks to represent what we believe infrastructure management should look like in the modern era.
