Cloud Computing

8 Best Cloud Cost Optimization Tools for 2026

Guy Brodetzki
·
6 min read

Key Takeaways

  • Multi-cloud, Kubernetes, serverless, and ephemeral infra have made cloud costs harder to track and control, leading to structural inefficiencies.
  • AI is accelerating cloud cost growth and waste, increasing compute and storage demands.
  • Modern cost optimization tools automate optimization through rightsizing, cleanup, scheduling, policy enforcement.
  • AI is becoming the control layer for FinOps with chatbots, auto-generated dashboards, anomaly detection, “next best action” recommendations, and autonomous agents.
  • Quick wins come through idle cleanup and rightsizing, but real impact comes when optimization becomes continuous and embedded in workflows. 
  • InfrOS delivers waste-free infrastructure, reducing the need for cost optimization cleanup.

Why Cloud Cost Optimization has Become a Priority in 2026

Cloud environments have grown significantly more complex over the past few years. Teams are now managing multi-cloud deployments, Kubernetes clusters, serverless workloads, and ephemeral infrastructure.

This sprawl leads to new cost management and optimization challenges:

  • New cost variables that are difficult to understand and track manually.
  • Unused resources, overprovisioned instances, and inefficient scaling policies. 25% of cloud spend is estimated to be wasted.
  • Limited visibility across transient environments makes it difficult to track spend accurately, allocate costs, and identify optimization opportunities.

In addition, the growing adoption of AI agents and systems is further increasing cloud spend. Cloud compute is required for model inference, large-scale data processing and storage, continuous experimentation, and serving AI-driven features in real time. The massive resources required can quickly inflate cloud bills.

What to Look for in Cloud Cost Optimization Software

Cloud cost optimization software helps teams monitor, analyze, and reduce cloud spending through automated insights and actions. The most effective platforms go beyond dashboards and provide direct operational impact.

Here’s what to look for:

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Visibility & Reporting

  • Multi-cloud support (AWS, Azure, GCP) with unified dashboard
  • Real-time cost monitoring and granular spend breakdowns
  • Tagging and cost allocation by team, project, or environment (unit economics)
  • Historical trend analysis and forecasting

Optimization Recommendations

  • Rightsizing suggestions for underutilized resources
  • Idle resource detection and automated cleanup
  • Reserved instance / savings plan recommendations
  • Spot/preemptible instance guidance
  • AI-driven recommendations (not just static rules)

Automation

  • Automated scheduling (e.g., shutting down dev environments at night)
  • Auto-scaling policies and enforcement
  • Policy-based guardrails to prevent overspending
  • One-click or fully automated remediation

Budgeting & Alerts

  • Custom budget thresholds per team, service, or account
  • Anomaly detection with real-time alerts
  • Forecasting to project end-of-month spend

Governance & Accountability

  • Role-based access control
  • Showback/chargeback reporting for internal billing
  • Audit logs and compliance tracking

Integrations

  • Native cloud billing API integrations
  • Ticketing tools (Jira, ServiceNow) for remediation workflows
  • FinOps/ITSM tool compatibility
  • Kubernetes and container cost visibility

Ease of Use

  • Quick setup with minimal configuration
  • Actionable insights (not just raw data)
  • Clear ROI tracking - savings achieved vs. software cost

Support & Pricing

  • Transparent vendor pricing (flat fee vs. % of spend)
  • Strong onboarding and customer success support
  • Regular updates as cloud pricing models evolve

Best Cloud Cost Optimization Tools List for 2026

With so many cloud cost optimization tools to choose from, it might be confusing to choose the right tool for your needs. To help, we compiled a list of the top tools. They were evaluated based on automation capabilities, AI-driven insights, Kubernetes support, multi-cloud coverage, and ease of integration into engineering workflows.

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1. InfrOS

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InfrOS is an IT infrastructure operating system that approaches cost optimization by preventing waste before it even occurs. It focuses on designing, emulating, and validating inherently optimized architectures and architectural decisions to eliminate technical debt from the get-go.

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Top Features

  • Emulation and benchmarking of cloud architectures in a simulation lab
  • Generation of a validated, ready-to-deploy Terraform code (IaC)
  • Continuous lifecycle optimization to prevent configuration drift
  • Risk-free migration planning across multi-cloud or hybrid setups.

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Recommended Use Cases

Use InfrOS when you are deploying new cloud architecture or migrating systems and want to ensure you "ship right the first time" with perfectly aligned, waste-free infrastructure, or when you need to optimize existing evolving architecture and changing cloud elements.

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2. ScaleOps

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ScaleOps is an autonomous, real-time resource optimization platform focused on Kubernetes and AI infrastructure. It dynamically rightsizes workloads in production environments for cutting cloud costs.

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Top Features

  • Automated real-time pod rightsizing for CPU and memory resource requests.
  • Replica optimization that dynamically manages triggers and scales
  • GPU workload rightsizing, offering automated optimization for real-time demand
  • Spot, Node, and Karpenter optimization to efficiently utilize nodes and eliminate underutilized capacity.

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Recommended Use Cases

Choose ScaleOps when you are looking for an autonomous solution for your K8s and AI infrastructure.

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3. Cast AI

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Cast AI is an application performance automation platform for Kubernetes and cloud applications. It proactively rightsizes workloads and manages infrastructure to improve performance and shrink costs.

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Top Features

  • Self-healing AI Agents that remediate drift and automatically fix operational issues without tickets.
  • Precision workload rightsizing for CPU and memory requests.
  • Infrastructure automation including GPU allocation, node scaling, and intelligent workload placement.
  • Spot instance interruptions predictions

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Recommended Use Cases

Choose Cast AI for use cases requiring autonomous solutions for K8s and app performance and when using Spot instances.

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4. OpenOps

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OpenOps is a no-code, open-source FinOps automation solution that helps organizations connect their existing visibility tools and multi-cloud environments so they can create optimization and remediation workflows.

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Top Features

  • No-code customizability with unlimited steps, conditional branching, and thresholds to build workflows from scratch.
  • Pre-packaged workflows for top FinOps domains
  • Multiple integrations with public clouds, FinOps tools, DevOps tools, and communication platforms.
  • Human-in-the-loop approvals to streamline feedback loops and avoid blind automation.

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Recommended Use Cases

Choose OpenOps if you are a FinOps practitioner who needs highly customizable workflows without wanting to write code, and you need to maintain tight governance.

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5. PointFive

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PointFive provides deep waste detection and agentic remediation for cloud and AI efficiency. 

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Top Features:

  • DeepWaste Detection featuring over 400 optimization types across AWS, Azure, GCP, Kubernetes, Snowflake, Databricks, and more.
  • Agentic Remediation, where AI coding agents generate contextual IaC fixes
  • Optimization for AI, analyzing GPU instance rightsizing, model selection, prompt caching, and provisioned throughput.
  • Workflow automation routing tasks via Jira, Slack, or ServiceNow to accelerate resolution.

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Recommended Use Cases

Use PointFive when you need to uncover deep architectural waste (including complex AI infrastructure costs) and want to speed up implementation by providing your engineers with ready-to-deploy IaC fixes directly in their workflows.

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6. IBM Turbonomic

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IBM Turbonomic is an application resource management platform for hybrid and multicloud environments. It optimizes compute, storage, and network resources to real-time, for optimizing performance.

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Top Features

  • Full-stack visibility that continuously analyzes applications, VMs, containers, and infrastructure to map resource flows and dependencies.
  • Policy-driven automation for executing safe, auditable actions 
  • Rightsizing compute, storage, network and GPU resources based on live demand.
  • Data center, Kubernetes, and cloud optimization.

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Recommended Use Cases

Choose IBM Turbonomic if you’re a large enterprise with a complex hybrid IT infrastructures(mixing on-premises data centers, VMs, and multicloud environments).

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7. Harness

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Harness provides an AI-powered FinOps tool that provides recommendations, reports and answers to natural language questions.

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Top Features

  • Reporting and visibility for cost allocation, kubernetes, chargeback/showback and anomaly detection.
  • Automated surfacing of insights and optimization opportunities.
  • Automated policy creation and remediation
  • AutoStopping of idle resources
  • Commitment Orchestrator for automated purchasing and management of Instances
  • Cluster Orchestrator for autoscaling with spot orchestration and bin packing

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Recommended Use Cases

Use Harness when you want to rely on AI for cost optimization management

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8. Wiv

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Wiv is an AI-powered FinOps workflow  automation platform that provides cloud cost optimization recommendations and uses conversational AI to automate routines and enforce governance.

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Top Features

  • AI FinOps agent, which learns business context, alerts teams to cost spikes, and answers cost questions in natural language.
  • Low-code or natural language options for building tailored optimization workflows.
  • Advanced filtering options for case management
  • Human-in-the-loop approvals
  • Real-time dashboards

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Recommended Use Cases

Choose Wiv if you’re looking for a no-code interface and an AI copilot for building and enforcing your workflows.

How AI Tools are Changing Cloud Cost Optimization

AI tools for cloud cost optimization use ML models, LLMs and MCP servers to automate and enhance and deliver cost optimization workflows. These systems continuously learn from workload behavior to predict usage, identify anomalies and adjust rightsizing recommendations over time. They can reduce cloud costs by 15-35% through real-time alerts and recommendations, with tools like InfrOS reducing costs by 43% as well as time to deployment.

With AI in cloud cost optimization, teams can:

  • Automate rightsizing recommendations - Continuously analyze resource utilization and suggest or automatically apply optimal instance types and sizes, eliminating manual guesswork
  • Predict and prevent cost spikes - Use forecasting models to anticipate usage surges before they occur, enabling proactive budget controls rather than reactive fixes
  • Detect anomalous spending in real time - Identify unusual cost patterns the moment they emerge, reducing the window between a misconfiguration and its financial impact
  • Optimize reserved instance and savings plan coverage - Analyze historical usage trends to recommend the right mix of commitment-based pricing, maximizing discounts without over-committing
  • Eliminate idle and zombie resources - Surface underutilized VMs, orphaned snapshots, and forgotten storage buckets that accumulate costs silently over time
  • Accelerate FinOps workflows - Reduce the manual effort of tagging audits, cost allocation, and reporting, freeing engineers to focus on higher-value work
  • Improve multi-cloud visibility - Consolidate spending insights across AWS, Azure, and GCP into unified recommendations, making cross-cloud tradeoffs easier to evaluate
  • Answer cost questions via chatbot - Allow teams to ask natural language questions like “Why did spend spike yesterday?” and get immediate, contextual answers
  • Generate dashboards on demand - Turn prompts into real-time cost views, breaking down spend by service, team, or workload without manual setup.
  • Recommend next best actions - Go beyond insights to suggest exactly what to do next, from shutting down resources to changing pricing models.
  • Operationalize MCP integrations - Connect AI agents to cloud and FinOps systems through MCP to take action (e.g. resize instances, apply policies) directly from insights
  • Unify context across tools - Pull data from billing, observability, and infra into a single ai-driven view, reducing fragmentation and decision latency

FAQs

How do cloud cost optimization tools differ from FinOps platforms?

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Cloud cost optimization tools focus on identifying and reducing infrastructure waste through automation and technical insights. FinOps platforms guide decision-making and budgeting by connecting spend to business units, enforcing policies, forecasting usage, and enabling teams to track unit economics and ROI.

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Are cloud cost optimization solutions safe for production workloads?

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Most modern solutions are designed with safeguards such as approval workflows, policy controls, and rollback mechanisms to ensure safe operation in production. Teams can configure automation levels, starting with recommendations before enabling execution, minimizing the risk of performance impact or unintended disruptions.

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Can cloud cost optimization software support Kubernetes environments?

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Yes, many modern tools provide Kubernetes-native support, offering visibility into pod-level costs, idle resources, and cluster efficiency. They also deliver rightsizing recommendations and workload optimization strategies specifically tailored to containerized environments, which are now central to most cloud architectures.

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How quickly can teams see ROI from cloud cost optimization services?

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Teams often begin seeing measurable savings within weeks, especially when addressing obvious inefficiencies like idle resources or overprovisioned instances. Full ROI typically depends on adoption depth, but organizations that integrate optimization into engineering workflows can achieve continuous and compounding cost reductions.

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Do AI-powered tools replace manual infrastructure optimization?

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AI-powered tools significantly reduce the need for manual optimization by automating analysis and remediation, but they do not fully replace human oversight. Engineers are still responsible for defining policies, validating changes, and aligning optimization efforts with performance, reliability, and business requirements.

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Keep on reading

Events
Meet InfrOS at AWS re:Invent 2026 in Las Vegas
Guy Brodetzki
Oct 11, 2026
·
6 min read

InfrOS is excited to be exhibiting at AWS re:Invent 2026 in Las Vegas!

We'll join the global cloud community to talk about how to build better cloud systems, cut costs, and keep them secure and reliable.

Make the Whole Thing Work at Booth #1387

Cloud architecture is complicated enough. Come have some fun at our booth:

  • Pull the lever: Set our Rube Goldberg machine in motion and win a prize.
  • Enter our daily raffles: Get a chance to win even bigger prizes.
  • Meet the team: Chat with us and see InfrOS in action with a live demo.

Meet Our Team

Our founders and team members will be at the event to meet cloud architects, engineering leaders, and organizations looking to improve their cloud infrastructure:

Schedule a Meeting with Us

We'd love to connect! Stop by for a quick chat or book a meeting in advance to see InfrOS in action.


Book a Meeting with InfrOS


See you in Las Vegas!

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Events
Meet InfrOS at KubeCon + CloudNativeCon North America 2026
Guy Brodetzki
Oct 11, 2026
·
6 min read

InfrOS is heading to KubeCon + CloudNativeCon North America 2026!

We'll join the global cloud community to talk about how to build better cloud systems, cut costs, and keep them secure and reliable.

Make the Whole Thing Work at Booth #360

Cloud architecture is complicated enough. Come have some fun at our booth:

  • Pull the lever: Set our Rube Goldberg machine in motion and win a prize.
  • Enter our daily raffles: Get a chance to win even bigger prizes.
  • Meet the team: Chat with us and see InfrOS in action with a live demo

The Best Seats aren't at KubeCon

Join InfrOS on November 11 for an NBA game in our private skybox as the Utah Jazz take on the Orlando Magic. It's a chance to catch the game, meet fellow attendees, and spend time with the InfrOS team away from the conference floor.
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Register for the event here

Meet Our Team

Our team will be available throughout the event to discuss your infrastructure challenges and demonstrate the InfrOS platform.

Here's our team attending the event:

Let's Meet

Have an infrastructure challenge you'd like to discuss? Stop by our booth or schedule a dedicated meeting with our team.


Book a Meeting with InfrOS


See you in Salt Lake City!

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Cloud Architecture Design
9 Best Infrastructure Automation Tools for Cloud Engineering Team
Harel Dil
Sep 25, 2026
·
6 min read

Most cloud teams are already using cloud automation. There's Terraform in one repo, Ansible playbooks in another and a CI pipeline ties them all together. The problem is that "infrastructure automation tools" cover such a diverse range of use cases that it’s hard comparing them directly. In this blog post we’ll review 9 such tools, that actual task each one solves, and the niche it occupies. InfrOS is our product hence we will list it first, but we made sure to be fair in reviewing the rest. And also, we'll be upfront about our view - most cloud teams have too many tools stitched together, and we believe fewer is better.

‍Key Takeaways

  • Infrastructure automation covers provisioning, configuration, orchestration and, more recently, architecture decisions.
  • Terraform, OpenTofu, Pulumi and CloudFormation build infrastructure. Spacelift and env0 are infrastructure orchestration tools that run and govern those builds.
  • Almost every tool starts after someone has already picked the architecture. InfrOS works on that earlier step.
  • Most teams juggle too many tools. The fewer tools you work with, the fewer handoffs where things break.

What Counts as Infrastructure Automation

Infrastructure automation, for the purposes of the list, refers to software that performs a step within the process of creating, configuring, or maintaining an infrastructure component in an automated way instead of a manual one. This includes IaC, configuration management, the process of running and approving changes, and policy enforcement. General CI/CD systems like GitHub Actions or Jenkins were excluded because they belong to a separate category - they could run Terraform, but wouldn't know what infrastructure is. The same is true about monitoring solutions that merely point at broken parts of an infrastructure but don't fix it.

Automating a Decision Is Not the Same as Automating Deployment

The vast majority of tools perform an action according to the plan - you decide which cloud, regions, database, networking design and redundancy level you need, and the tool implements it. This is good, but the tool doesn't care whether your design was right or wrong. If your architecture costs you twice as much as it needs to, or collapses after region failure, Terraform will build it anyway.

A subset of these tools actually creates the design. They consider your requirements, contrast various architectural options and highlight the trade-offs in terms of costs, performance, resilience, and compliance before any implementation takes place. This is important because an architecture design mistake can easily be corrected on the whiteboard but would be very costly once implemented. For more details check out this blog post about shift-left cloud infrastructure design.

The 9 Best Infrastructure Automation Tools

With that split in mind, here's each tool, what it automates, how it deploys and who it fits.

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1. InfrOS

InfrOS covers the entire cycle in one single package. It first helps to identify your infrastructure as well as requirements. This is followed by creation of different architectural options for you and analysis of them based on cost, performance, resiliency, and compliance. Before making any choice, it simulates the behavior of all options. Thus, you will be choosing based on facts rather than guesswork. Finally, after selecting one option, InfrOS helps to generate infrastructure as code and deploy the same. It will keep monitoring the live environment to inform you about better options whenever available. We call this Evolving Architecture™.

Recommended for: Cloud teams working in one or multiple clouds who seek a single solution for their design, validation, IaC and ongoing assessments.

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2. Terraform

Terraform remains the default choice for most teams, and deservedly so. Describe infrastructure in HCL files, execute the plan to see the changes to be done, and then apply those changes. With hundreds of providers, it can connect to pretty much any cloud or SaaS APIs. Nearly all Terraform automation done at scale happens either via HCP Terraform or a third party orchestrator which provides capabilities of remote executions and state management.

Best for: Pretty much any team that starts with IaC, especially in the context of multiple clouds.

Watch out for: HashiCorp put Terraform under Business Source License in 2023. HashiCorp got acquired by IBM recently. For most of us, nothing much changed, but for some companies, a fully open source option became preferable.

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3. OpenTofu

OpenTofu is the open-source fork of Terraform, created after that license change and now run under the Linux Foundation. Same HCL, same providers, very similar CLI, so moving over is usually a small job. It has also added features of its own, like built-in state encryption.

Best for: Teams that want the Terraform workflow with an open-source license and community governance.

Watch out for: The two projects are slowly drifting apart, so features don't always match.

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4. Pulumi

Pulumi allows for writing infrastructure using real programming languages such as TypeScript, Python, Go, C# and Java. This implies usage of loops, functions and unit testing instead of configuration language. It still operates through the desired state at its core meaning that you get to see the preview of each change before it is deployed.

Good for: Teams with a lot of developers willing to write code instead of learning HCL.

Keep in mind: With power comes increased ability to write messier infrastructures. Having a good system of code reviews in place is crucial.

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5. AWS CloudFormation

AWS CloudFormation is AWS’ own IaC solution. Using YAML or JSON templates AWS deploys and updates your infrastructure as a stack with rollback upon failure. Service itself is free but you have to pay for the resources it builds.

Good for: Teams which only use AWS and are interested in staying within their tooling ecosystem.

Keep in mind: It's AWS-only. The moment you start deploying your infrastructure to other clouds, you'll need another tool.

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6. Ansible

Ansible manages configurations of running servers. You craft playbooks using YAML format, and then Ansible connects via SSH to deploy packages, manage settings, and provision applications. There’s no agent involved, which makes the whole process straightforward.

Best for: Operations teams managing dozens of VMs, on-premise servers or networking devices. Usually used together with Terraform, where Terraform provisions and Ansible configures.

Be aware of: Ansible is not good with monitoring state of cloud resources, and therefore it’s not the best choice as a primary infrastructure provisioning solution.

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7. Spacelift

Spacelift is one of those infrastructure orchestrators that are sitting on top of your IaC. It allows running Terraform, OpenTofu, Pulumi, CloudFormation, Ansible, and Kubernetes workloads directly from your Git repositories. On top of it, it provides policies in code through Open Policy Agent, approvals, drift management and stack dependencies.

Best for: Platform teams with multiple stacks and multiple contributors that need some guardrails.

Be aware of: It’s one more platform to learn and use. Teams with only a few stacks probably don’t need it yet.

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8. env0

Similar in concept to Spacelift but focusing more on developer self-service and cost optimization, env0 allows platform teams to release approved templates for use by developers who build environments from those templates, with cost information added by env0 prior to deployment. If your concern is cloud governance, we've covered cloud governance tools for engineering teams before.

Best For: Organizations where developers should be able to spin up their own environments without giving them access to everything.

Beware: Like Spacelift, this is worth its weight in gold at scale.

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9. Crossplane

Crossplane is Kubernetes-as-a-control-plane for cloud infrastructure. Resources are defined as Kubernetes objects and managed through reconciliation (even when manual changes have been made), allowing platform teams to define APIs for themselves as a sort of wrapper, like a "database" object that handles all the cloud configuration under the hood.

Best For: Platform engineering teams who know their way around Kubernetes well.

Beware: This one's quite steep for those just starting with Kubernetes.

Tools Compared at a Glance

Here's how all nine line up on the same criteria.

ToolWhat it automatesHow you define itCloud scopeModelBest for
InfrOSArchitecture design, validation, IaC generation, deployment, ongoing checksRequirements and existing infrastructureSingle cloud, multi-cloud or hybridCommercialReplacing a multi-tool stack with one loop
TerraformProvisioningHCLMulti-cloudSource-available (BSL)General IaC
OpenTofuProvisioningHCLMulti-cloudOpen source (MPL)Open-source IaC
PulumiProvisioningTypeScript, Python, Go, C#, JavaMulti-cloudOpen-source coreDeveloper-led teams
CloudFormationProvisioningYAML or JSON templatesAWS onlyFree AWS serviceAWS-only teams
AnsibleConfigurationYAML playbooksAny server, any cloudOpen sourceServer and app config
SpaceliftIaC orchestration and policyWraps your IaCMulti-cloudCommercialPlatform teams with many stacks
env0IaC orchestration and self-serviceWraps your IaCMulti-cloudCommercialDev self-service and cost control
CrossplaneProvisioning through KubernetesKubernetes resourcesMulti-cloudOpen source (CNCF)Kubernetes platform teams

Swipe sideways to see all columns.

A few things jump out. Four of these tools (Terraform, OpenTofu, Pulumi and CloudFormation) do basically the same job with different languages and licenses, so you only need one of them. Spacelift and env0 only make sense once you have one of those four. And InfrOS is the only one that covers the steps before, during and after provisioning. So the real question isn't which tool is best. It's how many tools you actually need.

How These Tools Fit Together in One Pipeline

Consider the traditional pipeline to have four tiers. The first one is the decision tier where you select the architecture. The second is the definition tier where the architecture gets converted into code using Terraform, OpenTofu, Pulumi, or CloudFormation. Then, there is the orchestration tier where Spacelift or env0 executes the code with policy enforcement and approvals, or Crossplane does the same within Kubernetes. Finally, there is the configuration tier where Ansible configures the servers and applications.

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And that means four tools, four vendors, and four handoffs – and every single handoff becomes another point of failure. Instead, InfrOS uses a different approach, encompassing the decision-making, the IaC itself, the validation process and the monitoring within one loop, making it possible for you to remove quite a few elements from the stack rather than keeping it all running. This is end-to-end cloud automation, spanning design and live operations. Looping is important here because the process does not stop at deployment. Workloads evolve, pricing changes, new requirements come up – and an architecture that was perfect last year may no longer work.

 Keeping it in shape is an ongoing job, which our guide to cloud infrastructure optimization best practices covers.

FAQ

What is infrastructure automation?

It's the use of software for performing the tedious tasks of creating and managing the infrastructure. The tasks include coding of the infrastructure, provisioning of cloud resources, server configurations, approvals and policy enforcement. This aims at improving the speed, repeatability, and accuracy of change. More recent automation technologies extend the process of automation right from the stage of architectural designs to determine the best architecture options.

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What is the difference between automation and orchestration?

Automation is getting one task done without a person, like creating a database with a single Terraform apply. Orchestration is coordinating many automated tasks into one flow, in the right order and with the right checks. Think provisioning, then a policy check, then an approval, then configuration. Spacelift and env0 are infrastructure orchestration tools, while Terraform and Ansible are mostly automation. Once things grow, you usually need both.

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Do these tools replace Terraform?

Automation involves getting a single task completed without human intervention, such as building a database through a single apply in Terraform. On the other hand, orchestration involves managing all the automated tasks together as part of one workflow, in order and with the proper checks. For example, provision, policy check, approval, and configuration. Env0 and Spacelift are examples of infrastructure orchestration tools, whereas Terraform and Ansible are more automation tools.

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Which infrastructure automation tools suit small teams?

Keep it simple. You typically need one infrastructure-as-code tool for a small group, such as Terraform, OpenTofu or Pulumi, that operates within the CI pipeline you’re already using. CloudFormation will do the job just fine if you're only working in the AWS environment. Orchestration platforms and Kubernetes tools like Crossplane are more likely to give you return value later down the line when you have many stacks.

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How do you measure the return on infrastructure automation?

Let's start from things that are quantifiable. Measure the time that is required to deploy a change, the number of deployments that failed, the amount of time spent handling manual tickets, and how frequently drift occurred. In case of architecture level automation solutions, let's not forget about comparing cloud costs to some kind of baseline and counting the number of designs that were avoided after the go live.

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Our post on a FinOps cost optimization agent shows one way to tie automation to real cost numbers.

About InfrOS

InfrOS assists cloud teams in choosing what to build, validating that decision prior to production, and adapting as needed going forward.

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