Why CMPs no longer work, and what replaced them

Cloud management platforms (CMPs) used to be the go-to for provisioning infrastructure across private and public clouds. But they were only ideal when infrastructure was static and environments unchanging.

Ephemeral environments, increasing infrastructure complexity and the growing use of GPUs to support AI are seriously driving Environment as a Service (EaaS) as not just a cloud management platform alternative, but an actual full-scale replacement better attuned to the dynamic needs of modern cloud and AI-native environments.

In this article we’ll provide developers and IT leadership with a guide that thoroughly demonstrates the limitations of cloud management platforms, their ideal replacement platform, and key points to consider when choosing a substitute platform.

Cloud management platform limitations

CMPs such as Terraform, AWS CloudFormation, and Azure Resource Manager centralize IT infrastructure management. Their primary advantage is their ability to automate how resources such as virtual machines (VMs), storage volumes, and networks are provisioned across hybrid and multi-cloud environments.

CMPs also allow for version control, update previews, and resource grouping—which basically means that they let you operate related resources as one unit.

However, as an organization grows or adopts more modern workflows, important CMP limitations begin to surface. These limitations can include:

Support for resource-level automation: Rather than orchestrating the entire infrastructure as a monolith, CMPs handle infrastructure as isolated units. So, when engineers need to build multiple environments, the automation breaks down, requiring teams to manually provision resources individually before orchestrating them together to deliver each environment.

This slows down and complicates deployment, and creates inconsistent environments.

No support for Day-2 operations: CMPs are built for Day 0-1 provisioning, leaving DevOps engineers in a bind when it’s time for Day-2 operations such as autonomously detecting drift, pushing updates to live environments, and more.

Engineers often attempt to handle this by deploying third-party tools, but this increases tool sprawl and security risks.

Poor flexibility: CMPs are designed for centralized resource management, which works best if the said resources are predictable and relatively static. The trouble begins when a developer needs to urgently modify a live environment, for example, offloading security or logging by adding a sidecar container to a running pod.

With a CMP, this can’t be done on demand. Infrastructure layer changes necessitate reprovisioning, so the application must wait while the necessary pod configuration changes are effected in the deployment YAML.

Inadequate visibility: Though CMPs provide visibility into the infrastructure you have deployed, they don’t provide any critical context, such as who deployed the resource, when it was provisioned, how recently it was updated, etc. Without being able to see these details, you can miss important cost management, operational, and security pain points.

Minimal cost control capabilities: Cost controls aren’t native features of CMPs. At best, a CMP provides estimates for cloud costs prior to provisioning resources. It can’t visualize resource utilization metrics, which means it’s unable to proactively prevent wasted spend from occurring or to trace the source of wasted spend (for example, an abandoned test cluster).

The bottom line is that the limitations inherent to CMPs make them unsuited to modern workflows. There’s no clearer indication for that than the fact that Gartner has recently dropped CMPs as a market segment: Enterprises still sticking to CMPs will find that going forward this choice will limit growth, infrastructure coverage, and ultimately, business outcomes. So what does an ideal CMP substitute look like?

Key points to consider when evaluating cloud management platform replacements

Before transitioning from CMPs to another platform, businesses must understand that IaC is actually the pivot point: CMPs cannot take an existing cloud or AI infrastructure, codify it, and programmatically spin up an entirely new copy of it on-demand. Some EaaS platforms such as Quali Torque have evolved to support this.

But whichever CMP replacement you’re choosing, it must have capabilities that address the CMP limitations covered above. These capabilities include:

Support for entire environments: Look out for a solution that codifies entire environments as blueprints. “Entire environments” means that the infrastructure, software, code, configuration, and runtime libraries are all packed into a cohesive unit.

Such a solution will autonomously resolve dependencies between cloud services (such as databases, VMs, and containers), allowing engineers to build environments with just a few clicks. It will also abstract the common “works on my machine” problem that surfaces when engineers try to replicate duct-taped environments built with CMPs.

Full lifecycle management: Consider a solution that facilitates Day 0-2+ operations. It should include critical capabilities such as native monitoring for all resources and the ability to autonomously update live infrastructure with little to no impact on app performance.

Flexibility: Choose a tool that offers self-service catalogues, providing preconfigured cloud environments for various developer use cases such as development, staging, production, testing, and proof-of-concept (POC) deployments, agentic AI apps, and customer demos.

Visibility, customizable governance, and cost controls: Verify built-in support for proactive visibility, governance and cost management. An ideal CMP alternative should provide visibility into resource ownership, resource creation timelines, and other metadata.

It should offer customizable policy-as-code templates for controlling key issues such as:

• Who can spin up and modify resources.
• How long those resources should run.
• The maximum amount the resources should cost.
• Which user roles should have access.

Support for advanced technologies: CMPs were created to provision basic infrastructure, which makes it difficult for them to scale advanced cloud-native technologies or resources needed to support artificial intelligence (AI) workloads, such as AI models and machine learning (ML) services.

Designed to support static and predictable workloads, CMPs are too rigid for ephemeral, high-performance AI workflows, which often require just-in-time provisioning and policy-bound environments. This lack of support for GenAI workflows is a critical limitation accelerating the shift from CMPs to other substitutes.

Your CMP alternative should come with out-of-the-box support for AI/ML workload automation and governance, facilitate accurate GPU provisioning, keep resources up-to-date, provide MLOps pipelines, and prevent cost overruns.

What is a top CMP replacement?

Considering CMP limitations and key considerations in an ideal substitute, what does a viable CMP replacement look like? Consider Quali Torque. Torque is an end-to-end infrastructure automation platform, from application delivery and security to environment-as-a-service deployment and cloud management.

Let’s take a quick look at some of the features that Torque offers that are designed to fix CMP gaps:

Better automation

Full lifecycle management for infrastructure, environments, and AI/ML workloads

Quali provides self-service catalogues, complete with integrations spanning IaC, CLI, IDE, Kubernetes, CI/CD, and AIOps tools, for provisioning infrastructure, environments and AI/ML workloads.

Once provisioning is done, Torque automates Day-2 actions such as monitoring, cloud cost optimization, and inference accuracy, which is relevant in AI workflows. Torque independently pushes updates to live environments, aligns updates with environment blueprints, and alerts about and automatically fixes drift.

Environment-as-code (EaC) blueprints, a step beyond IaC

Torque autonomously handles full lifecycle management. It does this by creating and leveraging environment-as-code blueprints:

• Step 1: Torque discovers customer IaC modules and cloud resources which it automatically uses to create IaC modules.

• Step 2: It lets users submit prompts in basic human-readable language.

• Step 3: It converts the prompts, with insights from the IaC modules, into EaC blueprints.

• Step 4: Using the blueprints, Torque lets engineers create reusable assets (via the self-service portal) for building, running, and governing cloud environments and AI workloads.

The payoffs? Preconfigured, tailored environments that developers can use on-demand and easier infra setup for non-expert developers—which means, faster software development pipelines as infra teams aren’t bogged down with tickets.

More controls

Governance, security, and compliance workflows: A common worry is that self-service may complicate governance or lead to unauthorized resource provisioning. Torque lets governance and infrastructure teams define cloud governance policies in plain language while AI does the heavy-lifting of converting it into Policy-as-Code (PaC) formats.

These policies ensure that the requisite deployments are routed through infrastructure teams to get approval, developers can only launch resources based on predefined policies. For example, the policies may halt resource deployment if it violates enterprise standards or exceeds cost specifications.

Deduplication and resource tagging: Torque applies default tags to resources out of the box, preventing untagged or inconsistently tagged resources from slowing down engineers. Where default tags won’t do, Torque lets users select from administrator-configured pick lists, ensuring flexibility and consistency simultaneously.

Torque allows environment sharing across teams, eliminating the overhead of running duplicate environments where just one environment would do.

Lower Costs

Cloud cost-optimization features: Torque offers a number of cost-optimization capabilities. First, it supports budgeting and cost prediction with cloud-cost dashboards and visual analysis. Then, it implements cloud-cost policies in customers’ PaC.

It also offers AI modules that optimize cloud spend, uncovering abandoned/idle resources, identifying and quantifying potential cost-saving opportunities, and recommending ways to cut waste.

How does Quali Torque compare to CMPs?

Having highlighted a few of the core Quali Torque deliverables, here’s a summarized view of how Torque actually compares to CMPs.

Conclusion

As we’ve seen, CMPs have their strengths, they clearly excel at automating infra provisioning. But they also have impossible-to-overlook weaknesses, including a lack of support for environment-level automation, Day-2 operations, and cloud cost-optimization features.

Quali Torque has emerged as a CMP replacement that addresses CMP limitations and introduces key capabilities, such as self-service, out-of-the-box governance, environment-layer automation, and support for advanced workloads.

Want to see how these Torque capabilities can revolutionize your IT processes? Check the Torque demo video, book a demo, or reach out to start a free trial.