Continuous monitoring is a crucial practice in the fields of DevOps, cybersecurity, and compliance. It involves the proactive and ongoing process of observing, assessing, and collecting data from various systems, applications, and infrastructure components in real-time or near real-time. Continuous monitoring is closely related to observability, which goes beyond simple monitoring to provide a deep understanding of complex and dynamic systems. This holistic view of system behavior relies on diverse data sources, including logs, metrics, traces, and distributed tracing, making it essential for modern software engineering, particularly in microservices and containerized environments.
In this blog, we'll explore why continuous monitoring is important and how it can help organizations understand their systems and make data-driven product decisions. We'll also discuss how to reduce costs for log analytics — one of the most costly aspects of continuous monitoring.
Continuous monitoring enables teams to gain a better understanding of their applications and infrastructure. This knowledge can help not only with troubleshooting and security issues, but also with proactive data-driven product decisions based on actual customer usage patterns. Let’s explore some of the top use cases for continuous monitoring, and how this practice can both lower risk and optimize organizational performance.
Continuous monitoring encompasses performance monitoring, enabling organizations to identify performance bottlenecks, system outages, or service degradation in real-time. Addressing these issues promptly ensures a seamless user experience and minimizes downtime.
Continuous monitoring allows organizations to quickly detect and respond to security threats and vulnerabilities. By constantly analyzing system and network activities, it can identify suspicious or anomalous behavior, helping to mitigate potential security breaches before they cause significant damage. Popular resources like NIST and its Risk Management Framework (RMF) are great starting points for learning more about continuous monitoring for security.
It enables proactive issue identification and resolution, addressing problems before they become critical and reducing the impact on operations and user satisfaction.
The data collected through continuous monitoring provides valuable insights into system and business performance, helping organizations allocate resources effectively and align IT operations with business goals. In addition, leveraging customer usage data can help product teams decide which features in the roadmap to prioritize, and which may need improvement.
In a DevOps context, continuous monitoring assesses the impact of code changes and infrastructure modifications in real-time, ensuring that new deployments do not introduce performance issues or security vulnerabilities.
Continuous monitoring generates detailed logs and audit trails, aiding in forensic analysis and compliance audits by providing a historical record of system activities.
Many organizations turn to a proactive monitoring strategy to control cloud costs. Yet, they may not immediately see that certain continuous monitoring tools like Elasticsearch, Splunk and DataDog are driving up costs unexpectedly. Many DevOps teams discover hidden cost drivers when it comes to log management, in particular. The rise of cloud-native infrastructure and microservices-based systems means most companies are generating more logs today than they were a decade ago.
Why are these hidden costs so prominent? Most observability solutions charge ingestion, data transfer and retention fees, driving up the bill for popular observability solutions. With a lot more data and varying types of logs, it’s harder to prioritize logs in a microservices environment.
Not to mention, developer flexibility has driven up the cost of application logs. Many organizations have multiple developers working on multiple microservices, continuously developing new features. While these developers have more freedom over what they can log and how, this freedom has ballooned both complexity and log volume.
For example, many ELK Stack (Elasticsearch, Logstash and Kibana) users find their costs spiraling out of control, as environments scale up to incorporate more data sources, and organizations look to retain data beyond just a few days. At the root of the problem is the distributed architecture, which requires data to be partitioned and stored across numerous shards. And separate servers must be deployed, with each one responsible for its portion of the data. Even if it is easy to deploy and begin using the ELK stack with a low initial investment, most organizations quickly face ELK stack cluster sprawl, in which they are managing, and paying for, significant compute and storage resources.
To reduce the cost of continuous monitoring and observability, organizations can consider the following approaches:
Opt for a combination of tools that offer the best features for your specific needs. A single user interface can provide visibility into various best-of-breed tools, allowing you to choose the right tools for different aspects of observability.
Open-source tools can be cost-effective, but they may become hard to manage at scale. Consider using managed services and open APIs to augment your observability stack for greater flexibility and scalability.
Send logs to a dedicated log management solution like ChaosSearch, which can provide cost-efficient log analytics by using cloud object storage you already have (e.g. Amazon S3 or Google Cloud Platform). Teams can create searchable data indexes, analyze logs via Elastic or SQL APIs, and gain insights without the high costs of ingestion and retention associated with some other solutions.
Use your observability tool for what it does best. For example, real-time monitoring and alerting from a monitoring solution like Splunk can be complemented with deeper log analysis from ChaosSearch.
Continuous monitoring and observability are essential for modern organizations, but they can come with high costs. By adopting cost-saving approaches and leveraging the right tools and services, organizations can strike a balance between effective observability and cost efficiency, ensuring that their systems remain secure, performant, and reliable without breaking the bank. As IT and software development evolves, optimizing observability costs is crucial for sustainable growth and competitiveness.
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