MechCloud

Provisioning a globally distributed, strongly consistent database on Google Cloud without the usual complexity. This template shows how to deploy Cloud Spanner using MechCloud. No deep dive into provider-specific constructs, no verbose configurations, just a clear desired state. What stands out: - Define Cloud Spanner instances and databases with minimal inputs   - No state management overhead   - Consistent authoring experience across clouds   - Ready to plug into self service workflows  If you have worked with GCP IaC before, you know how quickly configs grow for something like Spanner. This simplifies it without hiding what matters. Template: https://lnkd.in/g-iBzWuR #DevOps #GCP #SRE #PlatformEngineering #InfrastructureAsCode

Enterprises are fundamentally about integrations, and Infrastructure as Code (IaC) has become the preferred way to manage resource types exposed by a software vendor or, more generally, any platform backed by REST APIs. However, there is a structural problem. Many IaC vendors build providers in collaboration with large, well-established platforms. This works well for big players, but it creates a disadvantage for startups that lack visibility and who are trying to establish themselves in enterprise environments. For a platform to make a mark in enterprises, it often needs to build and maintain IaC providers across multiple IaC tools because the IaC ecosystem is fragmented. This is not trivial. It requires significant engineering effort and ongoing maintenance. Every IaC provider must implement two critical concerns: - Validation of the desired state   - Determining whether a resource will be updated in place or needs to be recreated  Validation largely depends on the quality of the OpenAPI specification. To some extent, OpenAPI can also help with determining update vs recreate behavior. But in practice, this is not enough. The complex logic that decides whether a resource is updated or recreated lives inside the platform’s backend. It is not fully captured in OpenAPI specifications and is rarely available in public documentation. Because of this, every IaC provider ends up re-implementing this logic independently, which is both error-prone and inefficient. Ideally, this logic should not live inside IaC provider code. It should be exposed by the platform itself through a dedicated endpoint that can determine whether a given change results in an in-place update or a recreation. This would allow any IaC vendor to rely on a single source of truth instead of duplicating the same logic across multiple providers. At MechCloud, we have taken a different approach. We make it easier both for users of our platform and for platforms that want to integrate with us. As a platform owner, you only need to focus on: - Writing a high-quality OpenAPI specification (which is already essential, especially as poor specifications become a bottleneck for AI agents)   - Exposing an endpoint that determines update vs recreate behavior for brownfield resources  With this in place, we can onboard any platform or REST API in less than 30 minutes, without requiring you to build or maintain an IaC provider. This approach centralizes a global concern. You define validation and update behavior once, in one place. There is no need to update multiple IaC providers every time the logic changes, and no need to propagate those changes across all customers. #DevOps #SRE #PlatformEngineering #InfrastructureAsCode #aws #StatelessIac

Authentication is often implemented the wrong way. Most teams embed OAuth logic into every frontend and backend   and slowly accumulate complexity they didn’t plan for. The problem isn’t OAuth 2.0 or OpenID Connect.   It’s how we integrate them into modern applications. A more scalable approach: - Introduce a central authentication layer (for example, a reverse proxy)   - Register only that layer with your identity provider   - Keep auth logic out of your application code  What this changes: - Frontends don’t deal with client secrets   - Backends don’t need to validate tokens individually   - Authentication becomes consistent across services   - Security improves because the surface area is reduced  This model works especially well when you have: - Multiple frontends (SPAs, micro-frontends)   - Multiple APIs or services   - A single identity provider  Instead of solving authentication in every service, you solve it once. That’s the architectural shift. If you’re building web apps or backend systems, this pattern is worth considering. #Authentication #OAuth #OpenIDConnect #WebArchitecture #BackendDevelopment #Security

Most Amazon Web Services (AWS) architectures fail or become hard to scale not because of compute, but because the network foundation wasn’t designed properly from day one. This MechCloud template shows how to provision a multi-subnet VPC with a clean and production-ready structure. Here’s what this template sets up: - VPC with configurable CIDR range   - Multiple subnets across availability zones   - Public and private subnet separation   - Route tables and internet gateway configuration   - NAT gateway for controlled outbound access   - Security groups and network boundaries  Why this matters: - Strong isolation between public and private workloads   - Better security posture with controlled ingress and egress   - High availability across multiple zones   - Scalable network design for future workloads   - Clean baseline for any AWS architecture  If you're building anything beyond a simple setup on AWS, getting the VPC design right early will save a lot of rework later. Explore the template:   https://lnkd.in/gAH-PGm6  #DevOps #AWS #SRE #PlatformEngineering #InfrastructureAsCode

Container orchestration on Google Cloud can quickly become complex if you're managing clusters, nodes and scaling logic manually. This MechCloud template shows how to provision a production-ready GKE cluster with the right defaults, without getting lost in Kubernetes setup details. Here’s what this template sets up: - Google Kubernetes Engine (GKE) cluster for container orchestration   - Node pools backed by Compute Engine instances   - Networking with VPC, subnets and firewall rules   - IAM configuration for secure cluster access   - Integration with load balancing and service exposure   - Autoscaling for nodes and workloads  Why this matters: - Fully managed Kubernetes control plane reduces operational overhead   - Built-in scalability for containerized workloads   - Native integration with Google Cloud services   - High availability and self-healing infrastructure   - Consistent and repeatable cluster provisioning  GKE handles the underlying control plane and cluster lifecycle, so teams can focus on deploying and scaling applications instead of managing Kubernetes itself. If you're building microservices or platform workloads on GCP, this gives you a clean and reliable starting point. Explore the template:   https://lnkd.in/gnsfkcr2  #DevOps #GCP #SRE #PlatformEngineering #InfrastructureAsCode

Running databases on VMs still makes sense in many real-world workloads, but getting the setup right is where things usually get messy. This MechCloud template shows how to provision a VM alongside Microsoft Azure SQL with a clean, production-ready foundation. Here’s what this template sets up: - Virtual Machine for application or database workloads   - Azure SQL Database for managed relational storage   - Networking with VNet, subnets and security rules   - Secure connectivity between VM and database   - Identity and access configuration   - Storage and compute sizing aligned for performance  Why this matters: - Flexibility of VM-based workloads with managed database benefits   - Reduced operational overhead compared to self-managed databases   - Secure and structured network setup   - Clear separation between compute and data layers   - Faster provisioning with consistent configurations  If you're building applications that still require VM-level control but want managed database reliability, this is a practical starting point. Explore the template:   https://lnkd.in/gnWDNXjS #DevOps #Azure #SRE #PlatformEngineering #InfrastructureAsCode

Many teams building Amazon Web Services (AWS) containerized workloads often face the same friction: managing infrastructure complexity without overengineering. This MechCloud template shows how to run a production-ready ECS Fargate service without touching servers or clusters. Here’s what this template sets up: - ECS Fargate service for serverless container execution   - Task definitions with CPU and memory configuration   - Application Load Balancer for traffic distribution   - Networking with VPC, subnets and security groups   - IAM roles for secure service execution   - Auto scaling configuration for demand-based scaling  Why this matters: - No EC2 management overhead   - Clean separation between infra and application   - Predictable scaling behavior   - Secure by default setup   - Faster path from idea to deployment  If you're building microservices or APIs on AWS, this is a solid baseline to start with and extend. Explore the template:   https://lnkd.in/g3j3PCk5 #DevOps #AWS #SRE #PlatformEngineering #InfrastructureAsCode

In Google Cloud, not every VM needs to be exposed to the internet. In fact, most should not be. A MechCloud IaC template is available to provision a private VM with no external IP, aligning with a default-deny and internal-first architecture. Why this approach matters: - Eliminates direct exposure to the public internet   - Reduces attack surface without relying only on firewall rules   - Encourages access through controlled paths like bastion hosts or private networking   - Fits internal services, backend systems, and data processing workloads  What this template enables: - A Compute Engine VM without any external IP assignment   - Private networking for internal communication   - A clean foundation for secure, internal-only deployments   - Flexibility to layer access patterns like IAP or VPN on top  This shifts the mindset from “secure the VM” to “don’t expose it in the first place.” A practical starting point for teams adopting zero-trust or private-first infrastructure patterns on GCP. Template: https://lnkd.in/gzH8_ckq #DevOps #GCP #InfrastructureAsCode #SRE #PlatformEngineering

In Microsoft Azure, exposing a VM to the internet is not just about attaching a public endpoint. It is about controlling how that endpoint behaves over time. A MechCloud Azure IaC template is now available to provision a public web VM with a dedicated Public IP, designed for consistency and operational clarity. Where this becomes important: - Reallocated or dynamic IPs can disrupt DNS records and downstream integrations   - Stable endpoints are required for partner allowlists and external dependencies   - Predictability is key when moving from testing to real usage  What this template sets up: - A Virtual Machine ready for public web access   - A Public IP resource explicitly associated with the VM   - Networking components aligned for inbound connectivity   - A clean baseline suitable for extension into real workloads  Instead of treating public exposure as an afterthought, this approach makes it an explicit part of the infrastructure design. Useful for teams standardizing how internet-facing compute is provisioned on Azure. Template: https://lnkd.in/gFSvQ5eD #DevOps #Azure #InfrastructureAsCode #SRE #PlatformEngineering

Most “hello world” cloud setups stop at launching a VM. But real-world workloads need something more stable than a dynamic IP. That’s where this template comes in. A new MechCloud Amazon Web Services (AWS) IaC template provisions a public web VM with a static Elastic IP, providing a predictable and production-ready entry point. Why this matters: - Dynamic public IPs change on stop/start, breaking DNS mappings and integrations   - A static IP (Elastic IP) ensures consistent access to your application   - Critical for web servers, APIs, allowlists, and external integrations  What this template does: - Provisions an EC2 instance configured for public access   - Allocates and attaches an Elastic IP to the instance   - Sets up networking and security required for a basic web workload   - Keeps the configuration minimal but practical  This is one of those small but important steps that separates a demo setup from something reliable. If you're building anything externally accessible, start here instead of a plain VM. Template: https://lnkd.in/g8_Gahag #DevOps #AWS #InfrastructureAsCode #SRE #PlatformEngineering