Mark BurgessThe Inventory Management Problem
Scaling Ansible inventory management and playbooks across dynamic cloud, hybrid and on-prem environments quickly becomes an operational nightmare.
Static inventory sources become stale within days, group memberships fall out of sync, and host variables require constant manual updates.
When infrastructure resources are dynamically provisioned, those hosts won’t appear in your Ansible inventory until someone manually adds them.
When instances terminate, they remain as phantom targets that cause playbook failures.
This manual overhead doesn’t scale. In dynamic environments, inventory maintenance becomes a full-time job that introduces errors and delays at best, or destroys the business case for deploying Ansible at worst.
This article discusses the why, if you want to get straight to the how then go to Streamlining IT Operations: Ansible Automation with CheckMK
How CheckMK Changes the Game
CheckMK solves this by serving as both your monitoring platform and the authoritative source for Ansible automation targeting. Instead of maintaining separate systems that inevitably drift apart, CheckMK’s continuous host and service discovery, along with rich metadata capabilities, provide Ansible with real-time, accurate infrastructure intelligence.
When auto-scaling events occur, CheckMK immediately discovers new instances and applies appropriate labels or groupings based on user defined rules. When instances terminate, they automatically disappear from CheckMK’s active inventory. Your Ansible playbooks always operate against current reality, not stale static files.
Real-World Applications
Database Patching at Scale
Consider patching Oracle databases across development, staging, and production environments at scale, across multiple sites, whilst respecting maintenance windows and service dependencies. In a traditional Ansible setup, you maintain static inventory groups for each database and manually track which servers are in which maintenance window.
With Checkmk, we assign database servers and databases to relevant host or service groups, along with assigning environment specific tags – either dynamically using Checkmk’s powerful rules, or via inherited cloud service tags.
When a Oracle Database RU patch is to be applied, we run a single playbook that handles all the required tasks to apply the patch from setting the host downtimes through to checking the application services are up and running on the other side. This same playbook is able to be run across different customer deployments and configurations due to the metadata being provided by Checkmk.
The real power of this solution is when new database environments are added, Checkmk automatically takes care of the labelling and host/service group assignment for the database servers. This is all that needs to be done to include new database environnments in the patching process as the Ansible playbooks that perform the patching tasks use the host information provided from Checkmk. This significantly reduces the onboarding effort for new environments and ongoing maintenance effort at scale.
Intelligent Capacity Management
Rather than maintaining static information about application tiers and scaling policies, Checkmk provides real-time performance data and service relationships that enable intelligent automation decisions. Checkmk continuously monitors resource utilisation while tracking which applications and services are running on each host.
Using Checkmk’s Alert Handling functionality, Ansible automations are triggered to adjust resource allocations as required. The assigned Checkmk service labels capture the baseline configuration of cloud services and are accessible to Ansible as host variables. Instead of basic threshold-based scaling, Checkmk Alert Handler rules and plugins consider service health, dependency relationships, and actual performance trends.
This capability also helps us manage costs where resources that are over-configured are dialled back to their baseline config. For example, we use this capability to scale up/down VPU settings for OCI block volumes dynamically and it also a key part of the automations that stop/start compute and DB systems.
The Operational Transformation
The shift from static inventory management to CheckMK-driven automation represents a fundamental operational transformation. Instead of spending time maintaining inventory files, we can focus on defining automation logic and monitoring outcomes. Instead of debugging inventory drift issues, we spend our time enhancing automation capabilities far beyond what would be achievable with more manual approaches.
This solution allows our team to deploy new services confident that automation will automatically discover and incorporate them. This operational model scales naturally with infrastructure growth. Adding new data centre, cloud regions, or application environments doesn’t require proportional increases in inventory management overhead.
Implementation Benefits
Our own experience of implementing this capability has yielded immediate improvements in automation reliability and long-term gains in operational scalability. The approach eliminates the fundamental tension between the deployed infrastructure footprint and automation reliability.
We have been able to leverage our monitoring platform as the authoritative source to drive our automation platform. This is a very cost effective approach that you can deploy to operate sophisticated automation at scale.
The investment in these capabilities pays for itself many times over as your infrastructure platform grows and evolves. For a basic setup, you can expect to be up and running in 3-4 days and then adopt at your own pace. We found the returns of adopting this approach were not linear – more like a small exponential curve then a flat line, then a slightly larger exponential curve to another flat line (smaller this time), then to a very large exponential curve (this was when we had most of the environment being monitored correctly and most of the procedures that were a good fit automated).
The AI Factor
The days of hand crafting Ansible playbooks are long gone. We use Claude Code to build and maintain our Ansible playbook catalogue. The productivity and efficiency gains we have had from using AI to build the automations is incredible.
When we started using AI to develop and manage our Ansible code base that uses Checkmk as the inventory source – the return on adopting this technology went through the roof. We are able to develop, test and adopt automation capabilities that would not have been feasible 12-18 months ago.
Whilst this path does require some up-front investment and un-learning ways of doing things – the benefits are massively compounding once you go down this path.
