You can't hope for a rich harvest if your plants are in lousy soil. Farmers have known the secrets of a rich harvest for thousands of years. They plant their seeds on the best soil and provide water and care until everything is ready for the harvest. If farmers stopped removing weeds, providing water, or feeding their animals, their entire crop would be lost.
Our IT industry finally adopted the secrets for a rich harvest in the last decade. We no longer release business applications to customers without monitoring essential user experience and health metrics. This continuous feedback improves engagement and ensures that organizations are building not only systems right but also the right systems.
What are the costs of Observability?
The list of monitoring, tracing, and debugging tools is long. Some are open-source, and others come with full vendor support. Some can be installed in your data center, while others are fully SaaS-based. No matter what type of monitoring solution you select, there are the following cost factors:
The main cost driver is the number of systems under monitoring. Large deployments consist of thousands of servers; in such environments, a high level of automation is crucial for keeping the monitoring costs low. But things are not always so black or white. Even for much smaller enterprises is the cheapest option, the most expensive option in the long term.
Let's look more at the hidden costs. If you develop your observability product, basic data collecting, charting, and integration capabilities must be built in. The open-source observability products seem very attractive in the first place because no license costs are involved. Many businesses realize that open-source observability products' integration and maintenance efforts slow down their teams and create high costs.
In one of our projects, a customer invested 50 days for a single application to onboard it to their open-source monitoring solution. This customer had several hundred applications, so their open-source monitoring costs were extremely high. After all, they had no tracing capability and AI-powered problem analysis or automatic problem detection built in.
How to keep your Observability investments low?
There is a saying, "If your only toolbox is a hammer, you tend to see nails everywhere." Translated to Observability, we should avoid applying the same level of tracing and monitoring to all our business services and applications. From my perspective, a risk-based approach can help keep your observability investments low, so we deploy the most advanced tracing only to our high-risk applications. Not-so-critical apps could be equipped with infrastructure monitoring only.
Don't forget the hidden costs in DIY solutions. Building your observability product by stitching multiple tools together might block your best resources and re-invent the wheel. According to a study conducted by Dynatrace in 2022, businesses use 10+ tools to achieve Observability. All of these tools undergo update cycles and come with additional configurations. You might imagine that such a DIY observability platform creates too much effort.
Ask yourself the following questions:
#Investments - What are your development, license, operation, and maintenance costs?
#Expertise - Do you have full-stack observability expertise to build a scalable solution that supports all your technologies?
#Operations - Is your platform scalable, and who will provide the 24x7 support?
#Security - How will you ensure the security and privacy of your observability solution?
#Automation - Have you considered automatic integration and problem remediation?
#Analytics - Do you have data analysts to identify problems and trends
#Acceptence - How can you ensure acceptance by your entire team
Where are you in your Observability journey?
You can waste too much time and money on an outdated observability stack. As you transform your services from monoliths to microservices, you must ask crucial questions about the technology signal. We use five maturity levels and you can easily find out how you are doing by asking the questions below.
Monitoring: You use Dashboards and correlate alerts
Full Stack: You understand dependencies across the entire stack
Automated: You transform data into continuous answers
BizDevSecOps: You embed intelligence into your SDLC
Proactive: You achieve optimal business outcomes
If you prefer a more detailed self-assessment and comparison with peers, you should explore our Human-AI-powered knowledge modeling platform Gobenchmark. Check it out on www.gobenchmark.io
I wish you a successful transformation of your observability approach! Keep up the great work!